Archive for the ‘Education’ Category

Vladislav Zorov, a Quora user, brought this to my attention. It is a worthy follow-up to Jerry King’s “The Art of Mathematics,” called Lockhart’s Lament. Lockhart really fleshes out what King was talking about. Both are worth your time. Lockhart’s lament reminds me a lot of a post I wrote, called The challenge of trying to get a real science of computing in our schools. I talked about an episode of South Park to illustrate this challenge, where a wrestling teacher is confronted with a pop culture in wrestling that’s “not real wrestling” (ie. WWE), as an illustration of the challenge that computer scientists have in trying to get “the real thing” into school curricula. The wrestling teacher is continually frustrated that no one understands what real wrestling is, from the kids who are taking his class, to the people in the community, to the administrators in his school. There is a “the inmates have taken over the asylum” feeling to all of this, where “the real thing” has been pushed to the margins, and the pop culture has taken over. The people who see “the real thing,” and value it are on the outside, looking in. Hardly anybody on the inside can understand what they’re complaining about, but some of them are most worried that nothing they’re doing seems to be enough to make a big dent in improving the lot of their students. Quite the conundrum. It looks idiotic, but it’s best not to dismiss it as such, because the health and welfare of our society is at stake.

Lockhart’s Lament — The Sequel is also worth a look, as it talks about critics of Lockhart’s argument.

Two issues that come to mind from Lockhart’s lament (and the “sequel”) is it seems like since we don’t have a better term for what’s called “math” in school, it’s difficult for a lot of people to disambiguate mathematics from the benefits that a relative few students ultimately derive from “math.” I think that’s what the critics hang their hat on: Even though they’ll acknowledge that what’s taught in school is not what Lockhart wishes it was, it does have some benefits for “students” (though I’d argue it’s relatively few of them), and this can be demonstrated, because we see every day that some number of students who take “math” go on to productive careers that use that skill. So, they will say, it can’t be said that what’s taught in school is worthless. Something of value is being transmitted. Though, I would encourage people to take a look at the backlash against requiring “math” in high school and college as a counterpoint to that notion.

Secondly, I think Lockhart’s critics have a good point in saying that it is physically impossible for the current school system, with the scale it has to accommodate, to do what he’s talking about. Maybe a handful of schools would be capable of doing it, by finding knowledgeable staff, and offering attractive salaries. I think Lockhart understands that. His point, that doesn’t seem to get through to his critics, is, “Look. What’s being taught in schools is not math, anyway! So, it’s not as if anyone would be missing out more than they are already.” I think that’s the sticking point between him and his critics. They think that if “math” is eliminated, and only real math is taught in a handful of schools (the capacity of the available talent), that a lot of otherwise talented students would be missing out on promising careers, which they could benefit from using “math.”

An implicit point that Lockhart is probably making is that real math has a hard time getting a foothold in the education system, because “math” has such a comprehensive lock on it. If someone offered to teach real math in our school system, they would be rejected, because their method of teaching would be so outside the established curriculum. That’s something his critics should think about. Something is seriously wrong with a system when “the real thing” doesn’t fit into it well, and is barred because of that.

I’ve talked along similar lines with others, and a persistent critic on this topic, who is a parent of children who are going through school, has told me something similar to a criticism Lockhart anticipated. It goes something like, “Not everyone is capable of doing what you’re talking about. They need to obtain certain basic skills, or else they will not be served well by the education they receive. Schools should just focus on that.” I understand this concern. What people who think about this worry about is that in our very competitive knowledge economy, people want assurances that their children will be able to make it. They don’t feel comfortable with an “airy-fairy, let’s be creative!” account of what they see as an essential skill. That’s leaving too much to chance. However, a persistent complaint I used to hear from employers (I assume this is still the case) is that they want people who can think creatively out of the box, and they don’t see enough of that in candidates. This is precisely what Lockhart is talking about (he doesn’t mention employers, though it’s the same concern, coming from a different angle). The only way we know of to cultivate creative thinkers is to get people in the practice of doing what’s necessary to be creative, and no one can give them a step-by-step guide on how to do that. Students have to go through the experience themselves, though of course adult educators will have a role in that.

A couple parts of Lockhart’s account that really resonated with me was where he showed how one can arrive at a proof for the area of a certain type of triangle, and where he talked about students figuring out imaginary problems for themselves, getting frustrated, trying and failing, collaborating, and finally working it out. What he described sounds so similar to what my experience was when I was first learning to program computers, when I was 12 years old, and beyond. I didn’t have a programming course to help me when I was first learning to do it. I did it on my own, and with the help of others who happened to be around me. That’s how I got comfortable with it. And none of it was about, “To solve this problem, you do it this way.” I can agree that would’ve been helpful in alleviating my frustration in some instances, but I think it would’ve risked denying me the opportunity to understand something about what was really going on while I was using a programming language. You see, what we end up learning through exploration is that we often learn more than we bargained for, and that’s all to the good. That’s something we need to understand as students in order to get some value out of an educational experience.

By learning this way, we own what we learn, and as such, we also learn to respond to criticism of what we think we know. We come to understand that everything that’s been developed has been created by fallible human beings. We learn that we make mistakes in what we own as our ideas. That creates a sense of humility in us, that we don’t know everything, and that there are people who are smarter than us, and that there are people who know what we don’t know, and that there is knowledge that we will never know, because there is just so much of it out there, and ultimately, that there are things that nobody knows yet, not even the smartest among us. That usually doesn’t feel good, but it is only by developing that sense of humility, and responding to criticism well that we improve on what we own as our ideas. As we get comfortable with this way of learning, we learn to become good at exploring, and by doing that, we become really knowledgeable about what we learn. That’s what educators are really after, is it not, to create lifelong learners? Most valuable of all, I think, is learning this way creates independent thinkers, and indeed, people who can think out of the box, because you have a sense of what you know, and what you don’t know, and what you know is what you question, and try to correct, and what you don’t know is what you explore, and try to know. Furthermore, you have a sense of what other people know, and don’t know, because you develop a sense of what it actually means to know something! This is what I see Lockhart’s critics are missing the boat on: What you know is not what you’ve been told. What you know is what you have tried, experienced, analyzed, and criticized (wash, rinse, repeat).

On a related topic, Richard Feynman addressed something that should concern us re. thinking we know something because it’s what we’ve been told, vs. understanding what we know, and don’t know.

What seems to scare a lot of people is even after you’ve tried, experienced, analyzed, and criticized, the only answer you might come up with is, “I don’t know, and neither does anybody else.” That seems unacceptable. What we don’t know could hurt us. Well, yes, but that’s the reality we exist in. Isn’t it better to know that than to presume we know something we don’t?

The fundamental disconnect between what people think is valuable about education and what’s actually valuable in it is they think that to ensure that students understand something, it must be explicitly transmitted by teachers and instructional materials. It can’t just be left to chance in exploratory methods, because they might learn the wrong things, and/or they might not learn enough to come close to really mastering the subject. That notion is not to be dismissed out of hand, because that’s very possible. Some scaffolding is necessary to make it more likely that students will arrive at powerful ideas, since most ideas people come up with are bad. The real question is finding a balance of “just enough” scaffolding to allow as many students as possible to “get it,” and not “too much,” such that it ruins the learning experience. At this point, I think that’s more an art than a science, but I could be wrong.

I’m not suggesting just using a “blank page” approach, where students get no guidance from adults on what they’re doing, as many school systems have done (which they mislabeled “constructivism,” and which doesn’t work). I don’t think Lockhart is talking about that, either. I’m not suggesting autodidactic learning, nor is Lockhart. There is structure to what he is talking about, but it has an open-ended broadness to it. That’s part of what I think scares his critics. There is no sense of having a set of requirements. Lockhart would do well to talk about thresholds that he is aiming for with students. I think that would get across that he’s not willing to entertain lazy thinking. He tries to do that by talking about how students get frustrated in his scheme of imaginative work, and that they work through it, but he needs to be more explicit about it.

He admits in the “sequel” that his first article was a lament, not a proposal of what he wants to see happen. He wanted to point out the problem, not to provide an answer just yet.

The key point I want to make is the perception that drives not taking a risk is in fact taking a big risk. It’s risking creating people, and therefore a society that only knows so much, and doesn’t know how to exceed thresholds that are necessary to come up with big leaps that advance our society, if not have the basic understanding necessary to retain the advances that have already been made. A conservative, incremental approach to existing failure will not do.

Related post: The beauty of mathematics denied

— Mark Miller, https://tekkie.wordpress.com

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I thought I’d share the video below, since it has some valuable insights on what computer science should be, and what education should be, generally. It’s all integrated together in this presentation, and indeed, one of the projects of education should be integrating computer science into it, but not with the explicit purpose to create more programmers for future jobs, though it could always be used for that by the students. Alan Kay presents a different definition of CS than is pursued in universities today. He refers to how Alan Perlis defined it (Perlis was the one to come up with the term “computer science”), which I’ll get to below.

This thinking about CS and education provides, among other things, a pathway toward reimagining how knowledge, literature, and art can be presented, organized, dissected, annotated, and shared in a way that’s more meaningful than can be achieved with old media. (For more on that, see my post “Getting beyond paper and linear media.”) As with what the late Carl Sagan often advocated, Kay’s presentation here advocates for a general model for the education of citizens, not just professional careerists.

Another reason I thought of sharing this is several years ago I remember hearing that Kay was working on rethinking computer science curriculum. What he presents is more about suggestion, “Start thinking about it this way.” (He takes a dim view of the concept of curriculum, as it suggests setting students on a rigid path of study with the intent to create minds with a cookie cutter, though he is in favor of classical liberal arts education, with the prescriptions that entails.)

As he says in the presentation, there’s a lot to develop in the practice of education in order to bring this into fruition.

This is from 2015:

I wrote notes below for some of the segments he talks about, just because I think his presentation bears some interpretation for many readers. He uses metaphors a lot.

The bicycle

This is an analogy for how an apparatus, or a subject, is typically modified for education. We take the optimized, or the adult version of something, and add compensators, which make it so that beginners can use it without falling all over themselves. It’s seen as easier to present it this way, and as a skill-building experience, where in order to learn how to do something, you need to use “the real thing.” Beginners can put on a good show of using this sort of apparatus, or modified subject, but the problem is that it doesn’t teach a beginner how to become good at really using the real thing at its full potential. The compensators become a crutch. He said a better idea is to use an apparatus, or a component of the subject, that allows a beginner to get a feel for how to use the thing in a way that gets across significant aspects of its potential, but without the optimizations, or the way adults use it, which make it too complicated for a beginner to use under their own power. In this case, a lowbike is better. This beginner apparatus, or component, is more like the first version of the thing you’re trying to teach. Bicycles were originally more like scooters, without pedals, or a chain, where you’d sit in the seat, push it along with your legs, kind of “running while sitting,” glide, and turn by shifting your weight, and turning into the turn. Once a beginner gets a feel for that, they can deal with the optimized version, or a scaled down adult version, with pedals and a chain to drive the bike, because all that adds is the ability to get more power out of it. It doesn’t change any of the fundamentals of how you use it.

This gets to an issue of pedagogy, that learners need to learn something in components, rather than dealing with the whole thing at once. Once they learn one capacity, they can move on to the next challenge in learning the “whole thing.”

Radiation vs. nouns

He put forward a proposition for his talk, which is that he’s mixing a bunch of ideas together, because they overlap. This is a good metaphor, because most of his talk is about what we are as human beings, and how society should situate and view education. Only a little of it is actually on computer science, but all of it is germane to talking about computer science education.

He also gives a little advice to education reformers. He pointed out what’s wrong with education as it is right now, but rather than cursing it, he said one should make a deliberate effort to “build a tribe” or coalition with those who are causing the problem, or are in the midst of the problem, and suggest ways to bring them into a dignified position, perhaps by sharing in their honors, or, as his example illustrated, their shame. I draw some of this from Plato’s Cave metaphor.

Cooperation and competition in society

I once heard Kay talk about this many years ago. He said that, culturally, modern corporations are like the ancient hunter-gatherers. They exploit the resources of an area for a while, and once it’s exhausted, they move on, and that as a culture, they have yet to learn about democracy, which requires more of a “settlement” mentality toward what they’re doing. Here, he used an agricultural metaphor to talk about a cooperative society that creates the wealth that is then used by competitive forces within it. What he means by this is that the true wealth is the knowledge that’s ultimately used to develop new products and services. It’s not all developed inside the marketplace. He doesn’t talk about this, but I will. Even though a significant part of the wealth (as he said, you can think of it as “potential energy”) is generated inside research labs, what research labs tend to lack is knowledge of what the members of society can actually understand of this developed knowledge. That’s where the competitive forces in society come in, because they understand this a lot better. They can negotiate between how much of the new knowledge to put into a product, and how much it will cost, to reach as many people as possible. This is what happened in the computer industry of the past.

I think I understand what he’s getting at with the agricultural metaphor, because farmers don’t just want to reap a crop for one season. Their livelihood depends on maintaining fertility on their land. That requires not just exploiting what’s there season after season, or else you get the dust bowl. If instead, practices are modified to allow the existing land to become fertile again, or, in the case of hunter-gathering, aggressively managing the environment to create favorable grazing to attract game, then you can create a cycle of exploitation and care such that a group can stay in one area for a long time, without denying themselves any of the benefits of how they live. I think what he suggests is that if corporations would modify their behavior to a more settled, agricultural model, to use some of their profits to contribute to educating the society in which they exist, and to funding scientific research on a private basis, that would “regenerate the soil” for economic growth, which can then fuel more research, creating a cycle of renewal. No doubt the idea he’s presenting includes the businesses who would participate in doing this. They should be allowed to profit (“reap”) from what they “sow,” but the point is they’re not the only ones who can profit. Other players in the marketplace can also exploit the knowledge that’s generated, and profit as well. That’s what’s been done in the past with private research labs.

He attributes the lack of this to culture, of not realizing that the economic model that’s being used is not sustainable. Eventually, you use up the “soil,” and it becomes “infertile,” and “blows away,” and, in the case of hunter-gathering, the “good hunting grounds” are used up.

He makes a crucial point, though, that education is not just about jobs and competitiveness. It’s also about inculcating what citizenship really means. I’m sure if he was asked to drill down on this more, he would suggest a classical education for this, along with a modified math and science curriculum that would give students a sense of what those subjects really are like.

The sense I get is he’s advocating almost more of an Andrew Carnegie model of corporate stewardship, who, after he made his money, directed his philanthropy to building schools and libraries. Kay would just add science labs to that mix. (He mentions this later in his talk.)

I feel it necessary to note that not all for-profit entities would be able to participate in funding these cooperative activities, because their profit margins are so slim. I don’t think that’s what he’s expecting out of this.

What we are, to the best of our knowledge

He gives three views into human mental capacity: the way we perceive (theatrical), how much we can perceive at any moment in time (1 ± 2), and how educators should perceive ourselves psychologically and mentally (more primate and mammalian). This relates to neuroscience, and to some extent, evolutionary psychology.

The raison d’être of computer science

The primary purpose of computer science should be developing a science of systems in process, and that means all processes: mechanical processes, technological processes, social processes, biological processes, mental processes, etc. This relates to my earlier post, “Beginning the journey of becoming a computer scientist.” It’s partly about developing a new kind of mathematics for modeling processes. Alan Turing did it, with his Turing Machine concept, though he was trying to model the process of generating mathematical statements, and doing mathematical tests on them.

Shipping the design

Kay talks about how programmers today don’t have access to anything like what designers in other fields have, where they’re able to model their design, simulate it, and then have a machine fabricate a prototype that you can actually show and use.

I had an epiphany about this about 8 or 9 years ago. I was at a friend’s party, and there were a few mechanical engineers among the guests. I overheard a couple of them talking about the computer-aided design (CAD) software they were using. One talked about a “terrible” piece of CAD software he used at work. He said he had a much better CAD system at home, but it was incompatible with the data files that were used at work. As much as he would’ve loved to use it, he couldn’t. He said the system he used at work required him to group pieces of a design together as he was building the model, and once he did that, those pieces became inflexible. He couldn’t just redesign one piece of it, or separate one out individually from the model. He couldn’t move the pieces around on the model, and have them fit. Once they were grouped, that was it. It became this static thing. He said in order to redesign one piece of it, he had to take the entire model apart, piece by piece, redesign the part, and then redesign all the other pieces in the group to make the new part fit. He said he hated it, and as he talked about it, he acted like he was so disgusted with it, he wanted to throw it in the trash, like it was a piece of garbage. He said on his CAD system at home, it was wonderful, because he could separate a part from a model any time he wanted, and the system would adjust the model automatically to “make sense” out of the part being missing. He could redesign the part, and move it to a different part of the model, “attach it” somewhere, and the system would automatically adjust the model so that the new part would fit. The way he described it gave it a sense of fluidity. Whereas the system he used at work sounded rigid. It reminded me of the programming languages I had been using, where once relationships between entities were set up, it was really difficult to take pieces of it “out” and redesign them, because everything that depended on that piece would break once I redesigned it. I had to go around and redesign all the other entities that related to it to adjust to the redesign of the one piece.

I can’t remember how this worked, but another thing the engineer talked about was the system at work had some sort of “binding” mechanism that seemed to associate parts by “type,” and that this was also rigid, which reminded me a lot of the strong typing system in the languages I had been using. He said the system he had at home didn’t have this, and to him, it made more sense. Again, his description lent a sense of fluidity to the experience of using it. I thought, “My goodness! Why don’t programmers think like this? Why don’t they insist that the experience be like this guy’s CAD system at home?” For the first time in my career, I had a profound sense of just what Alan Kay talked about, here, that the computing field is retrograde. It has not advanced anywhere close to the kind of engineering that exists in other fields, where they would insist on this sort of experience. We accept so much less, whereas modern engineers have a hard time standing for it, because they know they have better options.

Don’t be fooled by large efforts below threshold

Before I begin this part, I want to share a crucial point that Kay makes, because it’s one of the big ones:

Think about what thinking is. Thinking is not being logical. Thinking is choosing the environment that you’re going to think in before you start rationalizing.

Kay had something very interesting, and startling, to say about the Apollo space program, using that as a metaphor for large reform initiatives in education generally. I recently happened upon a video of testimony he gave to a House committee on educational computing back in 1982, chaired by then-congressman Al Gore, and Kay talked about this same example back then. He said that the way the Apollo rockets were designed was a “hack.” They were not the best design for space travel, but it was the most expedient for the mission of getting to the Moon by the end of the 1960s. Here, in this presentation, he talks about how each complete rocket was the height of a 45-story building (1-1/2 football fields in length), most of it high explosives, with only a tiny capsule at the top that could fit 3 astronauts. This is not a model that could scale to what was needed for space travel.

It became this huge worldwide cultural event when NASA launched it, and landed men on the Moon, but Kay said it was really not a great accomplishment. I remember Rep. Gore said in jest, “The walls in this room are shaking!” The camera panned around a bit, showing pictures on the wall from NASA. How could he say such a thing?! This was the biggest cultural event of the century, perhaps in all of human history. He explained the same thing here: that the Apollo program didn’t advance space travel beyond the mission to the Moon. It was not technology that would get us beyond that, though, in hindsight we can say technology like it enabled launching probes throughout the Solar System.

Now, what he means by “space travel,” I’m not sure. Is it manned missions to the outer planets, or to other star systems? Kay is someone who has always thought big. So, it’s possible he was thinking of interstellar travel. What he was talking about was the problem of propulsion, getting something powerful enough to make significant discoveries in space exploration possible. He said chemical propellant just doesn’t do it. It’s good enough for launching orbital vehicles around our planet, and launching probes, but that’s really it. The rest is just wasting time below threshold.

Another thing he explained is that large initiatives which don’t cross a meaningful threshold can be harmful to efforts to advancing any endeavor, because large initiatives come with extended expectations that the investment will continue to be used, and they must be satisfied, or else there will be no cooperation in doing the initial effort. The participants will want their return on investment. He said that’s what happened with NASA. The ROI had to play out, but that ruined the program, because as that happened, people could see we weren’t advancing the state of the art that much in space travel, and the science that was being produced out of it was usually nothing to write home about. Eventually, we got what we now see: People are tired of it, and have no enthusiasm for it, because it set expectations so low.

What he was trying to do in his House committee testimony, and what he’s trying to do here, is provide some perspective that science offers, vs. our common sense notion of how “great” something is. You cannot get qualitative improvement in an endeavor without this perspective, because otherwise you have no idea what you’re dealing with, or what progress you’re building on, if any. Looking at it from a cultural perspective is not sufficient. Yes, the Moon landing was a cultural milestone, but not a scientific or engineering milestone, and that matters.

Modern science and engineering have a sense of thresholds, that there can come a point where some qualitative leap is made, a new perspective on what you’re doing is realized that is significantly better than what existed prior. He explains that once a threshold has been crossed, you can make small improvements which continue to build on that significant leap, and those improvements will stick. The effort won’t just crash back down into mediocrity, because you know something about what you have, and you value it. It’s a paradigm shift. It is so significant, you have little reason to go back to what you were doing before. From there, you can start to learn the limits of that new perspective, and at some point, make more qualitative leaps, crossing more thresholds.

“Problem-finding”/finding the goal vs. problem-solving

Problem solving begins with a current context, “We’re having a problem with X. How do we solve it?” Problem finding asks do we even have a good idea of what the problem is? Maybe the reason for the problems we’ve been seeing has to do with the fact that we haven’t solved a different problem we don’t know about yet. “Let’s spend time trying to find that.”

Another way of expressing this is a concept I’ve heard about from economists, called “opportunity cost,” which, in one context, gets across the idea that by implementing a regulation, it’s possible that better outcomes will be produced in certain categories of economic interactions, but it will also prevent certain opportunities from arising which may also be positive. The rub is these opportunities will not be known ahead of time, and will not be realized, because the regulation creates a barrier to entry that entrepreneurs and investors will find too high of a barrier to overcome. This concept is difficult to communicate to many laymen, because it sounds speculative. What this concept encourages people cognizant of it to do is to “consider the unseen,” to consider the possibilities that lie outside of what’s currently known. One can view “problem finding” in a similar way, not as a way of considering the unseen, but exploring it, and finding new knowledge that was previously unknown, and therefore unseen, and then reconsidering one’s notion of what the problem really is. It’s a way of expanding your knowledge base in a domain, with the key practice being that you’re not just exploring what’s already known. You’re exploring the unknown.

The story he tells about MacCready illustrates working with a good modeling system. He needed to be able to fail with his ideas a lot, before he found something that worked. So he needed a simple enough modeling system that he could fail in, where when he crashed with a particular model, it didn’t take a lot to analyze why it didn’t work, and it didn’t take a lot to put it back together differently, so he could try again.

He made another point about Xerox PARC, that it took years to find the goal, and it involved finding many other goals, and solving them in the interim. I’ve written about this history at “A history lesson on government R&D” Part 2 and Part 3. There, you can see the continuum he talks about, where ARPA/IPTO work led into Xerox PARC.

This video with Vishal Sikka and Alan Kay gives a brief illustration of this process, and what was produced out of it.

Erosion gullies

There are a couple metaphors he uses to talk about the lack of flexibility that develops in our minds the more we focus our efforts on coping, problem solving, and optimizing how we live and work in our current circumstances. One is erosion gullies. The other is the “monkey trap.”

Erosion gullies channel water along a particular path. They develop naturally as water erodes the land it flows across. These “gullies” seem to fit with what works for us, and/or what we’re used to. They develop into habits about how we see the world–beliefs, ideas which we just accept, and don’t question. They allow some variation in the path that’s followed, but they provide boundaries that don’t allow the water to go outside the gully (leaving aside the exception of floods, for the sake of argument). He uses this to talk about how “channels” develop in our minds that direct our thinking. The more we focus our thoughts in that direction, the “deeper” the gully gets. Keep at it too long, and the “gully” won’t allow us to see anything different than what we’re used to. He says that it may become inconceivable to think that you could climb out of it. Most everything inside the “gully” will be considered “right” thinking (no reason why), and anything outside of it will be considered “wrong” (no reason why), and even threatening. This is why he mentions that wars are fought over this. “We’re all in different erosion gullies.” They don’t meet anywhere, and my “right” is your “wrong,” and vice-versa. The differences are irreconcilable, because the idea of seeing outside of them is inconceivable.

He makes two points with this. One is that we have erosion gullies re. stories that we tell ourselves, and beliefs that we hold onto. Another is that we have erosion gullies even in our sensory perceptions that dictate what we see and don’t see. We can see things that don’t even exist, and typically do. He uses eyewitness testimony to illustrate this.

I think what he’s saying with it is we need to watch out for these “gullies.” They develop naturally, but it would be good if we had the flexibility to be able to eventually get out of our “gully,” and form a new “channel,” which I take is a metaphor for seeing the world differently than what we’re used to. We need a means for doing that, and what he proposes is science, since it questions what we believe, and tests our ideas. We can get around our beliefs, and thereby get out of our “gullies” to change our perspective. It doesn’t mean we abandon “gullies,” but just become aware that other “channels” (perspectives) are possible, and we can switch between them, to see better, and achieve better outcomes.

Regarding the “monkey trap,” he uses it as a metaphor for us getting on a single track, grasping for what we want, not realizing that the very act of being that focused, to the exclusion of all other possibilities, is not getting us anywhere. It’s a trap, and we’d benefit by not being so dogged in pursuing goals if they’re not getting us anywhere.

“Fast” vs. “slow”

He gets into some neuroscience that relates to how we perceive, what he called “fast” and “slow” response. You can train your mind through practice in how to use “fast” and “slow” for different activities, and they’re integral to our perception of what we’re doing, and our reactions to it, so that we don’t careen into a catastrophe, or miss important ideas in trying to deal with problems. He said that cognitive approaches to education deal with the “slow” systems, but not the “fast” ones, and it’s not enough to help students in really understanding a subject. As other forms of training inherently deal with the “fast” systems, educators need to think about how the “fast” systems responds to their subjects, and incorporate that into how they are taught. He anticipates this will require radically redesigning the pedagogy that’s typically used.

He says that the “fast” systems deal with the “atoms” of ideas that the “slow” system also deals with. By “atoms,” I take it he means fundamental, basic ideas or concepts for a subject. (I think of this as the “building blocks of molecules.”)

The way I take this is that the “slow” systems he’s talking about are what we use to work out hard problems. They’re what we use to sit down and ponder a problem for a while. The “fast” systems are what we use to recognize or spot possible patterns/answers quickly, a kind of quick, first-blush analysis that can make solving the problem easier. To use an example, you might be using “fast” systems now to read this text. You can do it without thinking about it. The “slow” systems are involved in interpreting what I’m saying, generating ideas that occur to you as you read it.

This is just me, but “fast” sounds like what we’d call “intuition,” because some of the thinking has already been done before we use the “slow” systems to solve the rest. It’s a thought process that takes place, and has already worked some things out, before we consciously engage in a thought process.


This is the clearest expression I’ve heard Kay make about what science actually is, not what most people think it is. He’s talked about it before in other ways, but he just comes right out and says it in this presentation, and I hope people watching it really take it in, because I see too often that people take what they’ve been taught about what science is in school and keep reiterating it for the rest of their lives. This goes on not only with people who love following what scientists say, but also in our societal institutions that we happen to associate with science.

…[Francis] Bacon wrote a book called “The Novum Organum” in 1620, where he said, “Hey, look. Our brains are messed up. We have bad brains.” He called the ways of messing up “idols.” He said we get serious errors because of our genetics. We get serious errors because of the culture we’re in. We get serious errors because of the languages we use. They don’t represent what’s actually out there. We get serious errors from the way that academia hangs on to bad ideas, and teaches them over again. These are his four “idols.” Anyone ever read Bacon? He said we need something to get around our bad brains! A set of heuristics, is the term we’d use today.

What he called for was … science, because that’s what “Novum Organum,” the rest of the title, was: “A new way of dealing with knowledge.”

Science is not the knowledge, because knowledge is in this context. What science is is a negotiation between what’s out there and what we can represent.

This is the big idea. This is the idea they don’t teach in school. This is the idea we should be teaching. It’s one of the biggest ideas of all time.

It isn’t the knowledge. It’s the relationship, because what’s out there is only knowable by a phenomena that is being filtered in every possible way. We don’t even know if our brain is capable of representing the stuff.

So, to think about science as the truth is completely wrong! It’s not the right way to look at it. But if you think about it as a negotiation between the best you can do right now and stuff that’s out there, where you’re not completely sure, you’re in a very strong position.

Science has been the most important, powerful thought system humans have ever invented, because it gave up the idea of truth, and it substituted for it a thousand variations of false, some of which are incredibly powerful. This is the big idea.

So, if we’re going to think about computing, this is one way … of thinking about, “Wow! Computers!” They are representers. We can learn about representations. We can simulate ideas. We can get a very good–much better sense of dealing with thinking about these complexities.

“Getting there”

The last part demonstrates what I’ve seen with exploration. You start out thinking you’re going to go from Point A to Point B, but you take diversions, pathways that are interesting, but related to your initial search, because you find that it’s not a straight path from Point A to Point B. It’s not as straightforward as you thought. So, you try other ways of getting there. It is a kind of problem solving, but it’s really what Kay called “problem finding,” or finding the goal. In the process, the goal is to find a better way to get to the goal, and along the way, you find problems that are worth solving, that you didn’t anticipate at all when you first got going. In that process, you’ll find things you didn’t expect to learn, but which are really valuable to your knowledge base. In your pursuit of trying to find a better way to get to your destination, you might even get through a threshold, and find that your initial goal is no longer worth pursuing, but there are better goals to pursue in this new perception you’ve obtained.

Related post: Reviving programming as literacy

—Mark Miller, https://tekkie.wordpress.com

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Several years ago, while I was taking in as much of Alan Kay’s philosophy as I could, I remember him saying that he wanted to see science be integrated into education. He felt it necessary to clarify this, that he didn’t mean teaching everything the way people think of science–as experimental proofs of what’s true–but rather science in the sense of its root word, scientia, meaning “to know.” In other words, make practices of science the central operating principle of how students of all ages learn, with the objective of learning how to know, and situating knowledge within models of epistemology (how one knows what they know). Back when I heard this, I didn’t have a good sense of what he meant, but I think I have a better sense now.

Kay has characterized the concept of knowledge that is taught in education, as we typically know it, as “memory.” Students are expected to take in facts and concepts which are delivered to them, and then their retention is tested. This is carried out in history and science curricula. In arithmetic, they are taught to remember methods of applying operations to compute numbers. In mathematics they are taught to memorize or understand rules for symbol manipulation, and then are asked to apply the rules properly. In rare instances, they’re tasked with understanding concepts, not just applying rules.

Edit 9/16/2015: I updated the paragraph below to flesh out some ideas, so as to minimize misunderstanding.

What I realized recently is missing from this construction of education are the ideas of being skeptical and/or critical of one’s own knowledge, of venturing into the unknown, and trying to make something known out of it that is based on analysis of evidence, with the goal of achieving greater accuracy to what’s really there. Secondly, it also misses on creating a practice of improving on notions of what is known, through practices of investigation and inquiry. These are qualities of science, but they’re not only applicable to what we think of as the sciences, but also to what we think of as non-scientific subjects. They apply to history, mathematics, and the arts, to name just a few. Instead, the focus is on transmitting what’s deemed to be known. There is scant practice in venturing into the unknown, or in improving on what’s known. After all, who made what is known, as far as a curriculum is concerned, but other people who may or may not have done the job of analyzing what is known very well. This isn’t to say that students shouldn’t be exposed to notions of what is known, but I think they ought to also be taught to question it, be given the means and opportunity to experience what it’s like to try to improve on its accuracy, and realize its significance to other questions and issues. Furthermore, that effort on the part of the student must be open to scrutiny and rational, principled criticism by teachers, and fellow students. I think it’d even be good to have professionals in the field brought into the process to do the same, once students reach some maturity. Knowledge comes through not just the effort to improve, but arguments pro and con on that effort.

A second ingredient Kay has talked about in recent years is the need for outlooks. He said in a presentation at Kyoto University in 2009:

What outlook does is give you a stronger way of looking at things, by changing your point of view. And that point of view informs every part of you. It tells you what kind of knowledge to get. And it also makes you appear to be much smarter.

Knowledge is ‘silver,’ but outlook is ‘gold.’ I dare say [most] universities and most graduate schools attempt to teach knowledge rather than outlook. And yet we live in a world that has been changing out from under us. And it’s outlook that we need to deal with that.

He has called outlooks “brainlets,” which have been developed over time for getting around our misperceptions, so we can see more clearly. One such outlook is science. A couple others are logic, and mathematics. And there are more.

The education system we have has some generality to it, but as a society we have put it to a very utilitarian task, and as I think is accurately reflected in the quote from Kay, we rob ourselves of the ability to gain important insights on our work, our worth, and our world by doing this. The sense I get about this perspective is that as a society, we use education better when we use it to develop how to think and perceive, not to develop utilitarian skills that apply in an A-to-B fashion to some future employment. This isn’t to say that skills used, and needed in real-world jobs are unimportant. Quite the contrary, but really, academic school is no substitute for learning job skills on the job. They try in some ways to substitute for it, but I have not seen one that has succeeded.

What I’ll call “skills of mind” are different from “skills of work.” Both are important, and I have little doubt that the former can be useful to some employers, but the point is it’s useful to people as members of society, because outlooks can help people understand the industry they work in, the economy, society, and world they live in better than they can without them. I know, because I have experienced the contrast in perception between those who use powerful outlooks to understand societal issues, and those who don’t, who fumble into mishaps, never understanding why, always blaming outside forces for it. What pains me is that I know we are capable of great things, but in order to achieve them, we cannot just apply what seems like common sense to every issue we face. That results in sound and fury, signifying nothing. To achieve great things, we must be able to see better than the skills with which we were born and raised can afford us.

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Back in 2009 I wrote a post called “Getting an education in America.” I went through a litany of facts which indicated we as a society were missing the point of education, and wasting a lot of time and money on useless activity. I made reference to a segment with John Stossel, back when he was still a host on ABC’s 20/20, talking about the obsession we have that “everyone must go to college.” One of the people Stossel interviewed was Marty Nemko, who made a few points:

  • The bachelor’s degree is the most overvalued product in America today.
  • The idea marketed by universities that you will earn a million dollars more over a lifetime with a bachelor’s than with a high school diploma is grossly misleading.
  • The “million dollar” figure is based solely on accurate stats of ambitious high achievers, who just so happened to have gone to college, but didn’t require the education to be successful. It’s misattributed to their education, and it’s unethical that universities continue to use it in their sales pitch, saying, “It doesn’t matter what you major in.”

It turns out Nemko has had his own channel on YouTube. I happened to find a video of his from 2011 that really fleshes out the points he made in the 20/20 segment. What he says sounds very practical to me, and I encourage high school students, and their parents, to watch it before proceeding with a decision to go to college.

Nemko talks about what college really is these days: a business. He talks about how this idea that “everyone must go to college” has created a self-defeating proposition: Now that so many more people, in proportion to the general population, are getting bachelors’ degrees, getting one is not a distinction anymore. It doesn’t set you apart as someone who is uniquely skilled. He advises now that if you want the distinction that used to come from a bachelor’s, you should get a master’s degree. He talks about the economics of universities, and where undergraduates fit into their cost structure. This is valuable information to know, since students are going to have to deal with these realities if they go to college.

It’s not an issue of rejecting college, but of assessing whether it’s really worth it for you. He also outlines some other possibilities that could serve you a lot better, if what motivates you is not well suited to a 4-year program.

Nemko lays out his credentials. He’s gotten a few university degrees himself, and he’s worked at high levels within universities. He’s not just some gadfly who badmouths them. I think he knows of what he speaks. Take a listen.

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The University of Colorado at Boulder started up a visiting scholar program for conservative thought last year. I had my doubts about it. I don’t like the idea of “affirmative action for certain ideologies.” One would think that if a university’s mission was to educate they wouldn’t care what one’s political leanings were. That’s a private matter. I would think a university, fully cognizant of its role in society, would look for people who are not only highly qualified, and show a dedication to academic work, but also seek a philosophical balance, not ideological. However, it has been noted many times how politically slanted university faculty is, at least in their political party registration. Looking at the stats, one would think that the institutions have in fact become bastions for one political party or another, and listening to the accounts from some scholars and students, you’d think that the arts & humanities colleges have become training grounds for political agitators and propagandists. I don’t find that encouraging. The fact that for many years universities have not used this apparent tilt toward ideological purity as an opportunity for introspection about what they are actually teaching, but seem to rather take it as a mark pride, is also troubling. All of the excuses I’ve heard over the years sound like prejudices against classical thought. I’d like to ask them, “Can you come up with anything qualitatively better” (if they’ve even thought about that), but I’m afraid I will be disappointed by the answer while they high-five each other.

Having actually witnessed a bit of the conservative thought program at CU (seeing a couple of the guest speakers), I’m pleased with it. It has an academically conservative slant, and, from what I’ve seen, avoids the “sales pitch” for itself. Instead, it argues from a philosophical perspective that is identified as conservative by society. The most refreshing thing is it’s open to dialogue.

The first professor in the program, Dr. Steven Hayward, wrote a couple excellent speeches I read on political discourse.

I thought I would highlight the profile that was written for the next professor in the program, Dr. Bradley Pirzer. He appears to be a man after my own heart on these matters. I’m looking forward to what he will present.

How would you characterize the state of political discourse in the United States today?

Terrible. Absolutely terrible. But, I must admit, I write this as a 46-year old jaded romantic who once would have given much of his life to one of the two major political parties.

Political discourse as of 2014 comes down to two things 1) loudness and 2) meaningless nothings. Oration is a dead art, and the news from CNN, Fox and other outlets is just superficial talking points with some anger and show. Radio is just as bad, if not worse. As one noted journalist, Virginia Postrel, has argued, we probably shouldn’t take anything that someone such as Ann Coulter says with any real concern, as she is “a performance artist/comedian, not a serious commentator.”

Two examples, I think, help illustrate this. Look at any speech delivered by almost any prominent American from 1774 to 1870 or so. The speeches are rhetorically complicated, the vocabulary immense, and the expectations of a well-informed audience high. To compare the speech of a 1830s member of Congress with one—perhaps even the best—in 2014 is simply gut-wrenchingly embarrassing.

Another example. The authors of the Constitution expected us to discuss the most serious matters with the utmost gravity. Nothing should possess more gravitas in a republic than the issue of war. Yet, as Americans, we have not engaged in a properly constitutional debate on the meaning of war since the close of World War II. We’ve seen massive protests, some fine songs, and a lot of bumper stickers, but no meaningful dialogue.

As a humanist, I crave answers for this, and I desire a return to true—not ideological—debate and conversation. Academia has much to offer the larger political world in this.

How do you view the value of higher education today, particularly given its rising cost and rising student-loan burden?

I’m rather a devoted patriot of and for liberal education. From Socrates forward, the goal of a liberal education has been to “liberate” the human person from the everyday details of this world and the tyranny of the moment. Our citizenship, as liberally educated persons, belongs to the eternal Cosmopolis, not to D.C. or London or. . . .

But, in our own titillation with what we can create, we often forget what came before and what will need to be passed on in terms of ethics and wisdom. The best lawyer, the best engineer, the best chemist, will be a better person for knowing the great ideas of the past: the ethics of Socrates; the sacrifice of Perpetua; and the genius of Augustine.

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See Part 1, Part 2

This post has been a long time coming. I really thought I was going to get it done about this time last year, but I got diverted into other research that I hope to convey on this blog in the future. I also found more sources to explore for the portion on Xerox PARC. I’ve been as faithful as I can to the history, but there’s always a possibility I made some mistakes. I welcome corrections from those who know better. 🙂

This is not a complete history of computing in the period I cover. I mean to convey the closing years of ARPA’s “great leaps” in ideas about computing, and then cover the attempt to continue those “great leaps” privately, at Xerox PARC.

My primary source material for this part is the book, “The Dream Machine,” by M. Mitchell Waldrop. I’ll just refer to it as “TDM.”

I’ve been dividing up this series roughly into decades, or “eras.” Part 1 focused on the 1940s and 50s. Part 2 focused on the 1960s. This part is devoted to the 1970s.

In Part 2 I covered the creation of the Advanced Research Projects Agency (ARPA) at the Department of Defense, and the IPTO (Information Processing Techniques Office, a program within ARPA focused on computer research), and the many innovative projects in which it had been engaged during the 1960s.

Decline at the IPTO

There were signs of “trouble in paradise” at the IPTO, beginning around 1967, with the acceleration of the Vietnam War. Charles Herzfeld stepped down as ARPA director that year, and was replaced by Eberhardt Rechtin. There was talk within the Department of Defense of ending ARPA altogether. Defense Secretary Robert McNamera did not like what he saw happening in the program. Money was starting to get tight. People outside of ARPA had lost track of its mission. It was recognized for producing innovative technology, but it was stuff that academics could get fascinated about. Most of it was not being translated into technologies the military could use. Its work with the academic community created political problems for it within the military ranks, because academia was viewed as being opposed to the war.

Rechtin cut a lot of programs out of ARPA. Some projects were transferred to other funders, or were spun off into their own operations. He wanted to see operational technology come out of the agency. The IPTO had some allies in John Foster at the Department of Defense Research & Engineering (DDR&E), the direct supervisor of ARPA, and Stephen Lukasik, who had had the chance to use the Whirlwind computer (which I covered in Part 1). Lukasik was very excited about the potential of computer technology. He succeeded Rechtin as ARPA director in 1970. The IPTO’s budget remained protected, mainly, it seems, due to Lukasik pitching the Arpanet to his superiors as a critical technology for the military in the nuclear age (I covered the Arpanet in Part 2).

In 1970 a rule called the Mansfield Amendment, created by Democratic Senator Mike Mansfield, came into effect. It established a rule for one year mandating that all monies spent by the Defense Department had to have some stated relevance to the country’s military mission. Wikipedia says that this rule was renewed in 1973, specifically targeting ARPA. Waldrop claims it was really a round-about way of cutting defense spending, but it didn’t mean anything in terms of the technology that was being developed at ARPA. Nevertheless, it had social reverberations within the agency’s working groups. In light of the controversy over the Vietnam War, it tarnished ARPA’s image in the academic community, because it required all projects funded through it to justify their existence in military terms. When non-military research proposals would be sent to ARPA, military “relevance statements” would be written up and slapped on them at the end of the approval process, unbeknownst to the researchers, in order to comply with the law. These relevance statements would contain descriptions of the potential, or supposed intended use of the technology for military applications. This caused embarrassing moments when students would find out about these statements through disclosures obtained through the Freedom of Information Act. They would confront ARPA-funded researchers with them. This was often the first time the researchers had seen them. They’d be left explaining that while, yes, the military was funding their project, the focus of their research was peaceful. The relevance statements made it look otherwise. Secondly, it gave the working groups the impression that Congress was looking over their shoulder at everything they did. The sense that they were protected from interference had been pierced. This dampened enthusiasm all around, and made the recruiting of new students into ARPA projects more difficult.

ARPA’s name was changed to DARPA in 1972, adding the word “Defense” to its name. It didn’t mean anything as far as the agency was concerned, but given the academic community’s opposition to the Vietnam War, it didn’t help with student recruiting.

In 1972 the consulting firm Bolt Beranek and Newman (BBN), which had built the hardware for the Arpanet, wanted to commercialize its technology–to sell it to other customers besides the Defense Department. IPTO director Larry Roberts announced in 1973 that he was leaving DARPA to work for BBN’s new networking subsidiary, named Telenet. This created a big problem for Lukasik, because Roberts hadn’t groomed a deputy director, as past IPTO directors had done, so that there would be someone who could jump right in and replace him when he left. It was up to Lukasik to find a replacement, only that wasn’t so easy. He had created a bit of a monster within the IPTO. Every year he had pushed its budget higher, while other projects within DARPA were having their budgets cut. He tried to recruit a new director from within the IPTO’s research community, but everyone he thought would be suited for it turned him down. They were enjoying their research too much. Lukasik came upon J.C.R. Licklider, the IPTO’s founding director, as his last resort.

J.C.R. “Lick” Licklider, from ARS 327

Licklider (he liked to be called “Lick”), who I talked about extensively in Part 2, had returned to MIT and ARPA in 1968. He became a tenured professor of electrical engineering. He began his own ARPA project at MIT, called the Dynamic Modeling group, in 1971. He observed that the Multics project, which I covered in Part 2, nearly overwhelmed Project MAC’s software engineers, and almost resulted in Multics being cancelled. The goal of his project was to create software development systems that would make complex software engineering projects more comprehensible. As part of his work he got into computer graphics, looking at how virtual entities can be represented on the screen. He also studied human-computer interaction with a graphics display.

Larry Roberts tried to find someone besides Lick to be his replacement, more as an act of mercy on him, but in the end he couldn’t come up with anyone, either. He contacted Lick and asked him if he’d be his replacement. He reluctantly accepted, and returned to his post as Director in 1974.

Lick became an enthusiastic supporter of Ed Feigenbaum’s artificial intelligence/expert systems research. I talked a bit about Feigenbaum in Part 2. Through his guidance, Feigenbaum founded Stanford’s Heuristic Programming Project. Feigenbaum’s work would become the basis for all expert systems produced during the 1980s. He also supported Feigenbaum’s effort to integrate artificial intelligence (AI) into medical research, granting an Arpanet connection to Stanford. This allowed a community of researchers to collaborate on this field.

The IPTO’s favored place within DARPA did not last. Lukasik didn’t get along with his new superior at DDR&E, Malcom Currie. They had different philosophies about what DARPA needed to be doing. Waldrop wrote, “Currie wanted solutions out of ARPA now,” and he wanted to replace Lukasik with someone of like mind.

Bob Taylor at the Xerox Palo Alto Research Center had his eye on Lukasik. He needed someone who understood how they did research, and how to talk to business executives. Taylor had been an IPTO director in the late ’60s (I cover his tenure there in Part 2). He brought its style of computer research to Xerox, but he could see that they were not set up to create products out of it. He thought Lukasik would be a perfect fit. Taylor had been recruiting many of DARPA’s brightest minds into PARC, and it was a source of frustration at the agency. Nevertheless, Lukasik was intensely curious to find out just what they were up to. He came out to see what they were building. PARC was accomplishing things that Lick had only dreamed of years earlier. Lukasik was so impressed, he left DARPA to join Xerox in 1975.

George Heilmeier, from Wikipedia

His replacement was George Heilmeier. Waldrop characterized him as hard-nosed, someone who took an applied science approach to research. He had little patience for open-ended, basic research–the kind that had been going on at DARPA since its inception. He was a solutions man. He wanted the working groups to identify goals, where they expected their research to end up at some point in time. His emphasis was not exploration, but problem solving. Though it was jarring to everybody at first, most program directors came to accommodate it. Lick, however, was greatly dismayed that this was the new rule of the day. He thought Heilmeier’s methods went against everything DARPA stood for. Lick liked the idea of finding practical applications for research, but he wanted solutions that were orders of magnitude better than older ones–world changing–not just short-term goals of improving old methods by ten percent. Further, he could see that micromanagement had truly entered the agency. It wasn’t just a perception anymore.

Heilmeier explained in a 1991 interview,

For all the wonderful technology IPTO had sponsored, [Heilmeier insisted], it was the worst mess in the agency. And artificial intelligence was the worst mess in IPTO. “You see, there was this so-called DARPA community, and a large chunk of our money went to this community. But when I looked at the so-called proposals, I thought, Wait a second; there’s nothing here. Well, Lick and I tangled professionally on this issue. He said, ‘You don’t understand. What you do is give good people the money and they go off and do good things and that’s it.’ I said, ‘Lick, I understand that. And these may be good people, but for the life of me I can’t tell you what they’re going to do. And I don’t know whether they are going to reinvent the wheel, because there’s no discussion of the current practice and there’s no discussion of the implications, so I can’t tell whether this is a wise investment for DoD or not.'” (TDM, p. 402)

Lick had a very good track record of picking research projects, using a more subjective, intuitive sense about people, and he didn’t believe you could achieve the same quality of research by trying to use a more objective method of selecting people and projects.

Bob Kahn, who had joined DARPA in 1972, explained the differences between them,

[The] fact was that … both men were right. “There were some things that were more conducive to George’s directed style,” says Kahn. “Packet radio, for example, and maybe the Internet. But speech understanding was not amenable. In fact, almost none of AI fit in. The problem was that George kept looking for a kind of road map to the field of AI. He wanted to know what was going to happen, on schedule, into the future, to make the field a reality. And he thought it was quite reasonable to ask for that road map, because he had no idea how hard it would be to produce. Suppose you were Lewis and Clark exploring the West, where you had no idea what you were going to encounter, and people wanted to know exactly what routes you were going to take, where you would camp, and what you would do out there. Well, this was just not an engineering job, where you could work out the whole plan. So George was looking for something that Lick couldn’t provide.”

Unfortunately, when Lick tried to explain that to his boss, it was a bit like Seymour Papert’s or Alan Kay’s trying to explain exploratory education to a back-to-basics hard-liner. “IPTO really didn’t have a program-management structure,” declares Heilmeier. “They had a financial management structure, and they had a cheering section.” (TDM, p. 403)

Interestingly, I found this article in EETimes that saw this from a very different perspective, saying that what DARPA had been running was a “good-old-boys network” (and it’s difficult to see how it’s not implicating Lick in this), and that Heilmeier snapped them into shape.

Lick tried to see it from Heilmeier’s perspective, that researchers should not see applications as a threat to basic research, and that they should make common cause with engineers, to bring their work into the world of people who will use their technology. He thought this could provide an avenue where researchers could receive feedback that would be valuable for their work, and would hopefully soften the antagonism that was being directed at open-ended research. Meanwhile, he’d quietly try to keep Heilmeier from destroying what he had worked so hard to achieve.

Heilmeier seemed to agree with this approach. He issued some challenges to the IPTO working groups of technical problems he saw needed to be solved. He said to them, “Look, if some of you guys would sign up for these challenges I can justify more fundamental work in AI.” Some of them did just as he asked, and got to work on the challenges.

Lick declared in 1975 that the Arpanet, a project which began at DARPA in 1967, was fully operational, and handed control of the network over to the Defense Communications Agency.

Lick had to kill funding for some projects, which was never easy for him. One in particular was Doug Engelbart’s NLS project at Stanford Research Institute. I talked about this project in Part 2, and what ended up happening with it. A lot of Engelbart’s talent had left to pursue the development of personal computing at Xerox PARC. From Lick’s and DARPA’s perspective, he just wasn’t able to innovate the way he had before. From Engelbart’s perspective, Lick had been captured by the hype around AI, and just wasn’t able to see the good he was doing. It was a sad parting of the ways for both of them. (Source: Nerd TV interview with Doug Engelbart)

Heilmeier was pleased with the changes at the IPTO. They had “gotten with the program,” and he thought they were producing good results. For Lick, however, the change was exhausting, and depressing. He left DARPA for good in late 1975, and returned to MIT, where his spirit soared again. This didn’t mean that he left computing behind. He came up with his own projects, and he encouraged students to play with computers, as he loved to do.

Bob Kahn at DARPA asked an important question. He agreed with Heilmeier’s “wire brushing” of the agency; that some projects had become self-indulgent, but he cautioned against “too much of a good thing” the other way.

ARPA’s current obsession with “relevance” had come dangerously close to destroying what made the agency so special. Remember, says Kahn, when it came to basic computer research–the kind of high-risk, high-payoff work that might not mature for a decade or more–ARPA was almost the only game in town. The computer industry itself was oriented much more toward products and services, he says, which meant that “there was actually very little research going on that was as innovative as ARPA’s.” And while there were certainly some shining exceptions to that rule–notably [Xerox] PARC, IBM and [AT&T] Bell Labs–“many of the leading scientists and researchers couldn’t be supported that way. So if universities didn’t do basic research, where would industry get its trained people?”

Yet it was ARPA’s basic research that was getting cut. “The budget for basic R&D was only about one third of what it was before Lick came in,” says Kahn. “Morale in the whole computer-science community was very low.” (TDM, p. 417)

Kahn pursued a research initiative, anticipating the future of VLSI (Very-Large-Scale Integration) design and fabrication for microchips, in order to try to revive the basic research culture at the IPTO. He won approval for it in 1977. Through it, methods were developed for creating computer languages for designing VLSI chips. Kahn was also the co-developer of TCP/IP with Vint Cerf at DARPA, the protocol that would create the internet.

Heilmeier left DARPA in 1977, and was replaced by Bob Fossum, who had a management style that was more amenable to basic research. The question was, though, “Okay. This is good, but what about the next director?” It had been common practice for people at DARPA to cycle out of administrative positions every few years. Was it too good to last?

Kahn began the Strategic Computing Initiative in 1983, a $1 billion program (about $2.3 billion in today’s money) to continue his work on advancing computer hardware design, and to advance research in artificial intelligence. Fossum was replaced by Robert Cooper that same year. Cooper took Kahn’s research goals and reimplemented them as an agency-wide program, giving every part of DARPA a piece of it. According to Waldrop, Kahn was disgusted by this, and took it as his cue to leave.

The era of revolutionary research in computing at the agency seems to come to an end at this point.

Here’s a video talking about what else was going on at DARPA during the period I’ve just covered:

Lick’s vision of the future

He would happily sit for hours, spinning visions of graphical computing, digital libraries, on-line banking and E-commerce, software that would live on the network and move wherever it was needed, a mass migration of government, commerce, entertainment, and daily life into the on-line world–possibilities that were just mind-blowing in the 1970s. (TDM, p. 413)

Lick had long been a futurist, a very reliable one. In a book published in 1979, “The Computer Age: A Twenty-Year View,” he looked into the future, to the year 2000, about what he could see happening–if he thought optimistically–with a nationwide digital network that he called “the Multinet.” The term “Internet” was not in wide use yet, though work on TCP/IP, what Lick called the “Kahn-Cerf internetworking protocol,” had been in progress for several years. The internet wouldn’t come into being for another 4 years.

“Waveguides, optical fibers, rooftop satellite antennae, and coaxial cables, provide abundant bandwidth and inexpensive digital transmission both locally and over long distances. Computer consoles with good graphic display and speech input and output have become almost as common as television sets.”

Great. But what would all those gadgets add up to, Lick wondered, other than a bigger pile of gadgets? Well, he said, if we continued to be optimists and assumed that all this technology was connected so that the bits flowed freely, then it might actually add up to an electronic commons open to all, as “the main and essential medium of informational interaction for governments, institutions, corporations, and individuals.” Indeed, he went on, looking back from the imagined viewpoint of the year 2000, “[the electronic commons] has supplanted the postal system for letters, the dial-tone phone system for conversations and tele-conferences, stand-alone batch processing and time-sharing systems for computation, and most filing cabinets, microfilm repositories, document rooms and libraries for information storage and retrieval.”

The Multinet would permeate society, Lick wrote, thus achieving the old MIT dream of an information utility, as updated for the decentralized network age: “many people work at home, interacting with coworkers and clients through the Multinet, and many business offices (and some classrooms) are little more than organized interconnections of such home workers and their computers. People shop through the Multinet, using its cable television and electronic funds transfer functions, and a few receive delivery of small items through adjacent pneumatic tube networks . . . Routine shopping and appointment scheduling are generally handled by private-secretary-like programs called OLIVERs which know their masters’ needs. Indeed, the Multinet handles scheduling of almost everything schedulable. For example, it eliminates waiting to be seated at restaurants.” Thanks to ironclad guarantees of privacy and security, Lick added, the Multinet would likewise offer on-line banking, on-line stock-market trading, on-line tax payment–the works.

In short, Lick wrote, the Multinet would encompass essentially everything having to do with information. It would function as a network of networks that embraced every method of digital communication imaginable, from packet radio to fiber optics–and then bound them all together through the magic of the Kahn-Cerf internetworking protocol, or something very much like it.

Lick predicted its mode of operation would be “one featuring cooperation, sharing, meetings of minds across space and time in a context of responsive programs and readily available information.” The Multinet would be the worldwide embodiment of equality, community, and freedom.

If, that is, the Multinet ever came to be. (TDM, p. 413)

He tended to be pessimistic that this all would come true. With the world as it was, he thought a more tightly controlled scenario was more likely, one where the Multinet did not get off the ground. He thought big technology companies would not get into networking, as it would invite government regulation. Communications companies like AT&T would see it as a threat to their business. Government, he thought, would not want to share information, and would rather use computer technology to keep proprietary files on people and corporations. He thought the only way his positive vision would come to pass was if a consensus of hundreds of thousands, or millions of people came about which agreed that an open Multinet was desirable. He felt this would require leadership from someone with a vision that agreed with this idea.

At this time there were packet switching network options offered by various technology companies, including IBM, Digital Equipment Corp. (DEC), and Xerox, but none of them offered openness. In fact, business customers wanted closed networks. They feared openness, due to possibilities of security leaks and industrial espionage. Things were not looking up for this “marketplace” vision of a future network, even in academia. Michael Dertouzos, who led the Laboratory of Computer Science (LCS) at MIT (formerly Project MAC), was very interested in Lick’s vision, but complained that his fellow academics in the program were not. In fact they were openly hostile to it. It felt too outlandish to them.

Microcomputers had taken off in 1975, with the introduction of the MITS Altair, created by electrical engineer Ed Roberts. (This was also the launching point for a small venture created by Bill Gates and Paul Allen, called “Micro Soft,” with their first product, a version of the Basic programming language for the Altair.) Lick bought an IBM PC in the early 1980s, “but it never had the resources to do what he wanted,” said his son, Tracy.

Yes, Lick knew, these talented little micros had been good enough to reinvent the computer in the public mind, which was no small thing. But so far, at least, they had shown people only the faintest hint of what was possible. Before his vision of a free and open information commons could be a reality, the computer would have to be reinvented several more times yet, becoming not just an instrument for individual empowerment but a communication device, an expressive medium, and, ultimately, a window into on-line cyberspace.

In short, the mass market would have to give the public something much closer to the system that had been created a decade before at Xerox PARC. (TDM, p. 437)

Personal computing

The major sources I used for this part of the story were Alan Kay’s retrospective on his days at Xerox, called “The Early History of Smalltalk” (which I will call “TEHS” hereafter), and “The Alto and Ethernet Software,” by Butler Lampson (which I will call “TAES”).

Parallel to the events I describe at DARPA came a modern notion of personal computing. The vision of a personal computer existed at DARPA, but as I’ll describe, the concept we would recognize today was developed solely in the private sector, using knowledge and research methods that had been developed previously through DARPA funding.

Alan Kay

Alan Kay, from Wikipedia

The idea of a computer that individuals could buy and own had been around since 1961. Wes Clark had that vision with the LINC computer he invented that year at MIT. Clark invited others to become a part of that vision. One of them was Bob Taylor. What got in the way of this idea becoming something that less technical people could use was the large and expensive components that were needed to create sufficiently powerful machines, and some sense among developers about just how ordinary people would use computers. Most of the aspects that make up personal computing were invented on larger machines, and were later miniaturized, watered down in sophistication, and incorporated into microcomputers from the mid-1970s into the 1990s and beyond.

Most people in our society who are familiar with personal computers think the idea began with Steve Jobs and Steve Wozniak. The more knowledgeable might intone, and give credit to Ed Roberts at MITS, and Bill Gates. These people had their own ideas about what personal computers would be, and what they would represent to the world, but in my estimation, the idea of personal computing that we would recognize today really began with Alan Kay, a post-graduate student with a background in mathematics and molecular biology, who had become enrolled in the IPTO’s computer science program at the University of Utah (which I talk a bit about in Part 2), in the late 1960s. A good deal of credit has to be given to Doug Engelbart as well, who was doing research during the 1960s at the Stanford Research Institute, funded by ARPA/IPTO, NASA, and the Air Force, on how to improve group knowledge processes using computers. He did not pursue personal computing, but he was the one who came up with the idea for a point-and-click interface, combining graphics and text, using a device he and his team had invented called a “mouse.” He also created the first system that enabled linked documents, which foreshadowed the web we’ve known for the last 20 years, and enabled collaborative computing with teleconferencing. Describing his work this way does not do it justice, but it’ll suffice for this discussion.

Doug Engelbart,
from ibiblio.com
Ivan Sutherland,
from Wikipedia
Seymour Papert, from MIT

Flex’s “self-portrait,” from lurvely.com

Kay began developing his ideas about personal computing in 1968. He had started on his first proof-of-concept desktop machine, called Flex, a year earlier. He was aware of Moore’s Law (from Gordon Moore), a prediction that as time passed, more and more transistors would fit in the same size space of silicon. The implication of this is that more computing functionality could fit in a smaller space, which would thereby allow machines which filled up a room at the time to become smaller as time passed. As a graduate student, he imagined miniaturized technology that seemed unfathomable to other computer scientists of his day. For example, a hard drive as small as the crook of your finger. Back then, a hard drive was the size of a floor cabinet, or medium-sized refrigerator, and was typically used with mainframes that took up the space of a room.

Kay’s ideas about what personal computing could be were heavily influenced by Doug Engelbart, Ivan Sutherland (from his Sketchpad project), Tom Ellis and Gabriel Groner at RAND Corp. (from a system called GRAIL), and Seymour Papert, Wally Feurzig, and Cynthia Soloman at BBN with their work with children and Logo, among many others. (Source: TEHS)

Engelbart thought of computing as a “vehicle” for thinking about, and sharing information, and developing group knowledge processes. Kay got a profound sense of the personal computer’s place in the world from Papert’s work with children using Logo. He realized that personal computers could not just be a vehicle, but a new medium.

People can get confused about this concept. Kay wasn’t thinking that personal computers would allow people to use and manipulate text, images, audio, and video (movies and TV)–what most of us think of as “media”–for the sake of doing so. He wasn’t thinking that they would be a new way to store, transmit, and present old media, and the ideas they expressed most easily. Rather, they would allow people to explore a whole new category of ideas and expression that the other forms of media did not express as easily, if at all. It’s not that the old media couldn’t be part of the new, but they would be represented as models, as part of a knowledge system, and operate under a person’s control at whatever grain would facilitate what they wanted to understand.

Butler Lampson described the concept this way:

Kay was pursuing a different path to Licklider’s man-computer symbiosis: the computer’s ability to simulate or model any system, any possible world, whose behavior can be precisely defined. And he wanted his machine to be small, cheap, and easy for nonprofessionals to use. (TAES, p. 2)

Kay could see from Papert’s work that using computers as an interactive medium could enable children to understand subjects that would otherwise have been difficult, if not impossible to grasp at their age, particularly aspects of mathematics and science.

Xerox PARC

Xerox enters the story in 1970. They had a different set of priorities from Kay’s, and it’s interesting to note that even though this was true, they were willing to accommodate not only his priorities, but those of other researchers they brought in.

Xerox was concerned that as the photocopier market grew, competitors would come into the space, and Xerox didn’t want to put all their “eggs” in it. They thought computers would be a good way for them to diversify their product line. Their primary goal was to…

…develop the “architecture of information” and establish the technical foundation for electronic office systems that could become products in the 1980s. It seemed likely that copiers would no longer be a high-growth business by that time, and that electronics would begin to have a major effect on office sys­tems, the firm’s major business. Xerox was a large and prosperous company with a strong commitment to basic research and a clear need for new technology in this area. (TAES, p. 2)

Xerox wanted to site their research facility near a community of computer research. They looked at a few places around the country, and ultimately decided on Palo Alto, CA, since it was near Berkeley and Stanford universities, which were centers of computer research. They wanted to bring in a research director who was familiar with the field, and would be respected by the research community. The right person for the job was not obvious. When they talked to computer researchers of the time, all paths eventually led back to Bob Taylor, who had been an ARPA/IPTO director in the late 1960s. The thing was he had no computer science research of his own under his belt. His background was in psychoacoustics, a field of psychology (though Taylor thinks of it as applied physics). What made him the right candidate in Xerox’s eyes was that everyone who was anyone in the field Xerox wanted to explore knew and respected him. So they invited Taylor in to assist in setting up their research group.  Thus was born the Xerox Palo Alto Research Center (PARC). It was totally funded by Xerox. Taylor was not officially given the job as PARC’s director, because of his lack of research background, but he was allowed to run the facility the same way that the IPTO had conducted computer research. You could say that PARC during the 1970s was “the ARPA way done privately.”

One of the research techniques used at ARPA and Xerox PARC was to anticipate the speed and memory capacities of future computers that would be in wide use. Engineers who were part of the research teams would either find hardware that fit these anticipated specifications, or would build their own, and then see what software they could develop on it that was significantly better in some capacity than the systems that were available in their present. As one can surmise, this was an expensive thing to do. In order to exceed the capacity of the machines that were in wide use, one had to not think about what was most economical, but rather go for the hardware that was only used by a relative few, if anyone was using it at all, because it would be prohibitively expensive for most to acquire. Butler Lampson described this with Xerox PARC’s Alto research system:

The Alto system was affected not only by the ideas its builders had about what kind of system to build, but also by their ideas about how to do computer systems research. In particular, we thought that it is important to predict the evolution of hardware technology, and start working with a new kind of system five to ten years before it becomes feasible as a commercial product.

Our insistence on work­ing with tomorrow’s hardware accounts for many of the differences between the Alto system and the early personal computers that were coming into existence at the same time. (TAES, p. 3)

(To get an idea of what Lampson was talking about in that last sentence, I encourage people look at another post I wrote a while back, called “Triumph of the Nerds.”)

Taylor hired a bunch of people from the failing Berkeley Computer Corp., which was started by Butler Lampson, along with students that had joined him as part of Project Genie. Among the people Taylor brought in were Chuck Thacker, Charles Simonyi, and Peter Deutsch. I talk a little about Project Genie in Part 2. He also hired many people from ARPA’s IPTO projects, including Ed McCreight from Carnegie Mellon University, and some of the best talent from Engelbart’s NLS project at the Stanford Research Institute, such as Bill English. Jerry Elkind hired people from BBN, which had been an ARPA contractor, including Danny Bobrow, Warren Teitelman, and Bert Sutherland (Ivan Sutherland’s older brother). Another major “get” was hiring Allen Newell from CMU as a consultant. A couple of Newell’s students, Stuart Card and Tom Moran, came to PARC and pioneered the field we now know as Human-Computer Interaction.

As Larry Tesler put it, Xerox told the researchers, “Go create the new world. We don’t understand it. Here are people who have a lot of ideas, and tremendous talent.” Adele Goldberg said of PARC, “People came there specifically to work on 5-year programs that were their dreams.” (Source: Robert X. Cringely’s documentary, “Triumph of the Nerds”) In a recent interview, which I’ll refer to at the end of this post, she said that PARC invited researchers in to work on anything that they thought in 5 years could have an impact on the company.

Alan Kay came to work at PARC in 1970, started the Learning Research Group (LRG), and got them thinking about what would come to be known as “portable computers,” though Kay called them “KiddieKomps.” They also got working on font technology.

In 1972 Kay committed his thoughts on personal computing to paper in a document called, “A Personal Computer for Children of All Ages.” In it he described a conceptual model he called a “Dynabook,” or “dynamic book.” I’ll quote from a blog post I wrote in 2006, called, “Great moments in modern computer history,” as it summarizes what I mean to get across about this:

Kay envisioned the Dynabook as a portable computer, 9″ x 12″ x 3/4″, about the size of a modern laptop, with its own battery, and would weigh less than 4 pounds. In fact he said, “The size should be no larger than a notebook.” He envisioned that it would use removable media for file storage (about 1 MB in size, he said), that it might have a keyboard, and that it would record and play audio files, in addition to displaying text. He said that if no physical keyboard came with the unit, a software keyboard could be brought up, and the screen could be made touch-sensitive so that the user could just type on the screen. … Oh, and he made a wild guess that it would cost no more than $500 to the consumer.

He said, “The owner will be able to maintain and edit his own files of text and programs, when and where he chooses.”

He envisioned that the Dynabook would be able to “dock” with a larger computer system at work. The user could download data, and recharge its battery while hooked up. He figured the transfer rate would be 300 Kbps.

Mock up of the Dynabook,
from history-computer.com

He imagined the Dynabook being connected to an “information utility,” like what was called at the time “the ARPA network,” which later came to be called the Internet. He predicted this would open up online access to schools and libraries of information, “stores” (a.k.a. e-commerce sites), and would bring “billboards” (a.k.a. web ads) to the user. I LOVE this quote: “One can imagine one of the first programs an owner will write is a filter to eliminate advertising!”

Another forward-looking concept he imagined is that it might have a flat-panel plasma display. He wasn’t sure if this would work, since it would draw a lot of power, but he thought it was worth trying. … He thought an LCD flat-panel screen was another good option to consider.

Kay thought it essential that the machine make it possible to use different fonts. He and fellow researchers had already done some experiments with font technology, and he showed some examples of their results in his paper.

children using the Dynabook 2

Alan Kay’s conception of children using the Dynabook

He doesn’t elaborate on this, but he hints at a graphical interface for the device. He describes in spots how the user can create and save “dynamic graphics.” In a scenario he illustrates in the paper, two children are playing a game like Space War on the Dynabook, involving graphics animation, experimenting with concepts of gravity. This scenario lays down the concept of it being a learning machine, and one that’s easy enough for children to manipulate through a programming language.

He also imagined that Dynabooks would be able to communicate with each other wirelessly, peer-to-peer, so that groups of students would be able to easily work on projects together, without needing an external network.

Working with Dan Ingalls, an electrical engineer with a background in physics, Diana Merry, and colleagues, the LRG began development of a system called “Smalltalk” in 1972. This was a first effort to create the Dynabook in software. It would come to formalize another of Kay’s concepts, of virtual objects in a computer. The idea was that entities (visual and non-visual) could be fashioned by the person using a computer, which are as versatile as tools that are used in the real world, and can be used in any combination at any time to accomplish tasks that may not have been evident when they were created.

Computer programming was an important part of Kay’s concept of how people would interact with this medium, though he developed doubts about it. What he really wanted was some way that people could build models in the computer. Programming just seemed to be a good way to do it at the time.

Objects could be networked together via. a concept called “message passing,” with the goal of having them work together for some purpose. As Smalltalk was developed over 8 years, this idea would come to include everything from the desktop interface, to windows, to text characters, to buttons, to menus, to “paint” brushes, to drawing tools, to icons, and more. His idea of overlapping windows came from his desire to allow people to work on several projects at the same time, allowing them to make the most of limited screen space. (source: TEHS)

“The best way to predict the future is to invent it”

The graphical interface he and his team developed for Smalltalk was motivated by educational research on children, which had surmised that they have a strong visual sense, and that they relate to manipulating visual objects better than explicitly manipulating symbols, as older interactive computer systems had insisted upon. A key phrase in the paragraph below is, “doing with images makes symbols,” that is, symbols in our own minds, and, if you will, in the “mind” of the computer. His concept of “doing with images” was much more expansive and varied than has typically been allowed on computers consumers have used.

All of the elements eventually used in the Smalltalk user interface were already to be found in the sixties–as different ways to access and invoke the functionality provided by an interactive system. The two major centers were Lincoln Labs [at MIT] and RAND Corp–both ARPA funded. The big shift that consolidated these ideas into a powerful theory and long-lived examples came because the LRG focus was on children. Hence we were thinking about learning as being one of the main effects we wanted to have happen. Early on, this led to a 90 degree rotation of the purpose of the user interface from “access to functionality” to “environment in which users learn by doing”. This new stance could now respond to the echos of Montessori and Dewey, particularly the former, and got me, on rereading Jerome Bruner, to think beyond the children’s curriculum to a “curriculum of the user interface.”

The particular aim of LRG was to find the equivalent of writing–that is learning and thinking by doing in a medium–our new “pocket universe”. For various reasons I had settled on “iconic programming” as the way to achieve this, drawing on the iconic representations used by many ARPA projects in the sixties. My friend Nicolas Negroponte, an architect, was extremely interested in embedding the new computer magic in familiar surroundings. I had quite a bit of theatrical experience in a past life, and remembered Coleridge’s adage that “people attend ‘bad theatre’ hoping to forget, people attend ‘good theatre’ aching to remember“. In other words, it is the ability to evoke the audience’s own intelligence and experiences that makes theatre work.

Putting all this together, we want an apparently free environment in which exploration causes desired sequences to happen (Montessori); one that allows kinesthetic, iconic, and symbolic learning–“doing with images makes symbols” (Piaget & Bruner); the user is never trapped in a mode (GRAIL); the magic is embedded in the familiar (Negroponte); and which acts as a magnifying mirror for the user’s own intelligence (Coleridge). (source: TEHS)

A demonstration of Smalltalk-80

Dan Ingalls,
from Wikipedia
Diana-Merry Shapiro,
from Linkedin
Chuck Thacker,
from Microsoft Research
Butler Lampson,
from Microsoft Research

Chuck Thacker, who has a background in physics and designing computer hardware, and a small team he assembled, created the first “Interim Dynabook,” as Kay called it, at PARC’s Computer Science Lab (CSL) in 1973. It was named “Bilbo.” It was not a handheld device, but more like the size of a desk, perhaps connected to a larger cabinet of electronics. It had a monitor with a bitmap display of 606 x 808 pixels (which gave it the profile of an 8-1/2″ x 11″ sheet of paper), and 128 kilobytes of memory. Smalltalk was brought over to it from its original “home,” a Data General Nova computer.

Thacker’s team created the first Alto models (named after Palo Alto) shortly thereafter. Butler Lampson, a computer scientist with a background in physics, and electrical engineering, led a team which created the operating system for it. The Alto computer fit under a desk. It was the size of a small refrigerator. The keyboard, mouse, and display sat on top of the desk. It had the same size display, and internal memory as “Bilbo,” ran at about 6 Mhz, and had a removable hard drive that stored 2.5 MB per disk. (source: History of Computers)

As the years passed, CSL would develop bigger, more powerful machines, with code names Dolphin, and Dorado.

Adele Goldberg, from PC Forum

Beginning in 1973, Adele Goldberg, who had a background in mathematics and information systems, and Alan Kay tried Smalltalk out on students from ages 12-15, who volunteered to take programming courses from them, and to come up with their own ideas for things to try out on it. Goldberg developed software design schema and curricular materials for these courses, and helped guide the education process as they got results.

They had some success with an approach developed by Goldberg where they had the students build more and more sophisticated models with graphical objects. They thought they were building the skill of students up to more sophisticated approaches to computing, and in some ways they were, though the influence these lessons were having on the students’ conception of computing was not as broad as they thought. Kay realized after teaching programming to some adult students that they could only get so far before they ran into a “literature” barrier. The same had been true with the teen and pre-teen students they had taught earlier, it turned out. From Kay’s description, the way I’d summarize the problem is that they required background knowledge in organizing their ideas, and they needed practice in doing this.

Kay said in retrospect that literature renders ideas. Any medium needs literature in order to be powerful. Literature’s purpose is to provide a body of ideas that can be discussed in the medium. (Source: TEHS) Without this literary background, the students were unable to write about, or discuss the more sophisticated ideas through programming code. No matter how good a tool or instrument is, what’s produced by the people using it can only be as good as the ideas they use in fashioning the product. Likewise, the quality of what is written in text or notation by an author or composer, or produced in sound by a musician, or imagery and sound for a movie, is only as good as a) the skill of the creators, b) the outlook they have on what they are expressing, and c) their knowledge, which they can apply to the effort.

Reconsidering the Dynabook

There came a “dividing point” at PARC in 1975. Kay met with other members of the LRG and discussed “starting over.” He could see that the developed ideas of Smalltalk were taking them away from the educational goals he set out to accomplish. Professional considerations had started to take hold with the group, though, and they saw potential with Smalltalk. The majority of the group wanted to stick with it. His argument for “starting over” with the educational project did not win out. He said of this point in time that while he disagreed with the decision of his colleagues, he held no ill will towards them for it. He knew them to be wonderful people. The sense I get from reading Kay’s account of this history is they saw potential perhaps in creating more sophisticated user environments that professionals would be interested in using. I infer this from the projects that PARC engaged in with Smalltalk thereafter.

Kay turned his focus to a new project, as if to pick up again his “KiddieKomps” idea. He designed a computer he called NoteTaker. Adele Goldberg said Kay’s purpose in doing this was to develop a proof of concept for the Dynabook’s hardware (see the interview with her at the end of this post). While Kay went off in this direction, Goldberg took over management of the Smalltalk project. The educational program at PARC faded away in 1976. (Source: TEHS)

The original concept for NoteTaker was a laptop design, with what Kay called a “tab mouse,” a physical control that was small, mounted on the computer, yet agile enough to allow a person using it to move a cursor around a graphical interface. This did not end up on the the machine, but NoteTaker was made useable with a keyboard, mouse, and a touch screen, and it had stereo sound output. (Source: “Joining the Mac Group,” by Bruce Horn)

Once Smalltalk-76 was done (each version of Smalltalk was named by the year it was completed), Dan Ingalls and Ted Kaehler ported it to the NoteTaker, and by around 1978 Kay had created the first “luggable” portable computer. Kay recalls it using three Intel 8086 processors (though others remember it using two Motorola 68000 processors), had 256 kilobytes of memory, and was able to run on batteries. It wasn’t a laptop (it was too big and heavy), but it could fit on a desktop. At first glance, it looks similar to the Osborne 1, the first commercially available portable computer, and there’s a reason for that. The Osborne’s case design was based on NoteTaker.

To put it through its paces, a team from PARC took the NoteTaker on an airplane trip, “running an object-oriented system with a windowed interface at 35,000 feet.”

Kay lamented that there wasn’t enough corporate will to use their own know-how to create better hardware for it (Kay considered the Intel processors barely sufficient), much less turn it into a commercial product.

The NoteTaker, from the Computer History Museum

Looking back on the experience, Kay was dismayed to see that PARC had pushed aside his educational goals, and had co-opted Smalltalk as a purely professional’s tool. The technologies that could have made his original Dynabook vision a physical reality were coming into being at just this time, but there was no will at Xerox to make it into an actual product. (source: TEHS)

He has pursued development of his educational ideas ever since. He sees computers in the same way that a few in the Middle Ages saw the invention of the printing press, enabling new ways of interacting and thinking, to, as Licklider would have said, allow people to “think as no one has ever thought before.”

Developing the office of the future

From left to right: Larry Tesler, from Twitter.com; Timothy Mott, from startupgenome.co; Charles Simonyi

A separate project at PARC also got started in the early 1970s, called POLOS (the Parc On-Line Office System), in the System Science Lab (SSL). The goal there was to develop a networked computer office system.

Edit 5/31/2016: I had suggested in the way I wrote the following paragraph that Larry Tesler had invented cut, copy, and paste actions in digital text editing. Upon further review, I think that historical interpretation is wrong. Doug Engelbart had copy and paste (and probably a “cut” action) in NLS, and there may have been earlier incarnations of it.

One of the first projects in this effort was a word processor called Gypsy, designed and implemented by Larry Tesler and Timothy Mott, both computer scientists. (Source: TAES) The unique thing about Gypsy was that Tesler tried to make it “modeless.” Its behavior was like our modern day word processors, where you always have a cursor on the screen, and all actions mean the same thing at all times. Wherever the cursor is, that’s where text is entered. He also invented drag selection/highlighting with a mouse, and which was incorporated into the a cut, copy, and paste process. This has been a familiar feature whenever we work with digital text.

Later, Charles Simonyi, an electrical engineer, and Butler Lampson began work on the first What You See Is What You Get (WYSIWYG) text processor, called Bravo. They developed their first version in 1974. It had its own graphical interface, allowed the use of fonts, and basic document elements, like italics and boldface, but it worked in “modes.” The person using it either used the computer’s keyboard to edit text, or to issue commands to manipulate text. The keyboard did something different depending on which mode was operating at any point in time. This was followed by BravoX, which was a “modeless” version. BravoX had a menu system, which allowed the use of a mouse for executing commands, making it more like what we’d recognize as a word processor today. (source: Wikipedia) Each team was trying out different capabilities of digital text, and trying to see how people worked with a text system most productively.

From left to right: Andrew Birrell, from Microsoft Research; Roy Levin, from Linkedin; Roger Needham, from ACM SIGSOFT; Michael Schroeder, from Microsoft Research

A couple graphical e-mail clients were developed by a team led by Doug Brotz, named “Laurel” and “Hardy,” which allowed easy review and filing of electronic mail messages. (sources: The Xerox “Star”: A Retrospective) From Lampson’s description, they appeared to be “e-mail terminals.” They didn’t store, or allow one to write e-mails. They just provided an organized display for them, and a means of telling a separate messaging system what you wanted to do with them, or that you wanted to create a new message to send. (Source: TAES) Andrew Birrell, Roy Levin, Roger Needham (who had a background in mathematics and philosophy), and Michael Schroeder, a computer scientist, developed a distributed service, which the e-mail clients used to compose, receive, and transmit network messages, called Grapevine. From the description, it appeared to work on a distributed peer-to-peer basis, with no central server controlling its services for an organization. Grapevine also provided authentication, file access control, and resource location services. Its purpose was to provide a way to transmit messages, provide network security, and find things like computers and printers on a network. It had its own name service by which clients could identify other systems. (source: Grapevine: An Exercise in Distributed Computing) To get a sense of the significance of this last point, the Arpanet did not have a domain name service, and the internet did not get its Domain Name Service (DNS) until the mid-1980s.

PARC had a complete office system going, with all of this, plus networked file systems, print servers (using Ethernet, created by Bob Metcalfe and Chuck Thacker at PARC), and laser printing (invented at PARC by Gary Starkweather, with software written by Butler Lampson) by 1975! A year later they had created a digital scanner.

You can see one of the e-mail clients being demonstrated, with WYSIWYG technology/laser printing, on the Alto, in this Xerox promotional video:

Clarence Ellis,
from CU Boulder
Gary Nutt,
from CU Boulder

Clarence Ellis and Gary Nutt, both computer scientists, developed OfficeTalk, a prototype office automation system, at PARC. It tracked “job” documents as they went from person to person in an organization. (source: “The Xerox ‘Star’: A Retrospective”) In addition, Ellis came up with the idea of clicking on a graphical image to start up an application, or to issue a command to a computer, rather than typing out words to do the same thing. This is something we see in all modern user interfaces. (source: Answers.com)

Why Xerox missed the boat

For this section I go back to Waldrop’s book as my main source.

One might ask if the future in PCs was invented at Xerox–sounding an awful lot like what is in use with business IT systems today–why didn’t they own the PC industry? An even bigger question, why weren’t people back then using systems like what we’re using now? We use Windows, Mac, and Linux systems today, each with their own graphical interfaces, which descended from what PARC created. We had word processing, and network LAN file servers for years, beginning in the 1980s, and later, e-mail over the internet, print servers, and later still, office “task” automation software. The future of personal computing as we would come to know it was sitting right in front of them in the mid-1970s. We can say that now, since all of these ideas have been made into products we use in the work world. If we were to look at this technology back then, we might’ve been clueless to its significance. The executives at Xerox were an example of this.

For several years management couldn’t understand the vision that was developing at PARC. The research culture there didn’t mesh that well with the executive culture, especially after the company’s founder, Joe Wilson, died in 1971. Wilson had been open to new ideas and venturing into new technologies. At the time of his death, the company was also struggling from its own growth. The demand for its copiers was outstripping its ability to produce them competently. So a new management team was brought in which understood how to manage big companies. These were “numbers men,” though, and their methodology didn’t allow them to understand how to translate the kind of deep R&D the company had been doing with computers into products, because of course they didn’t have metrics to make an accurate measurement of how much money they’d make with a technology that was totally unknown to them. The company had plenty of metrics about costs and revenue that came from copier development and sales, so it was no problem for them to make reliable estimates for a new copier model. The most profitable part of their business was copiers, so that’s where they put their product development resources. The old hands at Xerox who had set up PARC ran interference for their operation, to keep the “numbers men” from shutting down their work. The people at PARC kept trying to convince the higher-ups that what they had developed was useful for office productivity, but it was for naught. In a depressing anecdote, Waldrop wrote:

One top-level Xerox executive, after a day of being shown the wonders of PARC, had posed precisely one question to the researchers: “Where can I get some of those beanbag chairs?” (TDM, p. 408)

(A famous “feature” of PARC was their use of beanbag chairs for group discussions, called “dealer meetings.”)

Stephen Lukasik came to Xerox from DARPA in 1975. He set up what was called the Systems Development Division (SDD). Its purpose was to create new salable products out of the research that was going on inside Xerox. The problem for him was that Xerox’s executives were willing to give him the authority to set up the division, but they weren’t willing to listen to what he said was possible, nor were they willing to give him the funding to make it happen. Lukasik left Xerox in 1976. He said that though his time there was short, he valued the experience. He just didn’t see the point in staying longer. Nevertheless, SDD would become useful to Xerox. It just had to wait for the right management.

Xerox set up a big demo of its products in Boca Raton, FL. in 1977, which included the prototypes from PARC. All the company executives, their wives, family and friends were invited. The people from PARC did the best bang-up job they could to make their stuff look impressive for business computing.

Back at PARC, says [Gary] Starkweather, he and his colleagues saw this as their last, best chance. “The feeling was, ‘If they don’t get this, we don’t know what we can do.'” So, he says, with John Ellenby coordinating an all-out effort, “We stripped everything out of PARC down to the power cords and set it all up again in Boca. Computers, networks, printers–the whole thing! I built a laser printer that did color. Bill English had a word processor that did Japanese. We were going to show them space flight!”

They certainly tried. [At the event], Xerox executives and their families swarmed through the Grenada Rooms of the Boca Raton Hotel for a hands-on demonstration of WYSIWYG editing in Bravo, graphical programming in Smalltalk, E-mailing in Laurel, artistry in Paint and Draw–the works. “The idea was a mental slam-dunk!” says Starkweather. “And some people did see it.” The executives’ wives, for example–many of them former secretaries who knew all about carbon paper, Wite-Out, and having to retype whole pages to correct a single mistake–took one look at Bravo and got it. “The wives were so ecstatic they came over and kissed me,” remembers Jack Goldman. “They said, ‘Wonderful things you’re doing!’ Years later, I’d see them and they’d still remember Boca.”

Then there were the delegates from Fuji Xerox, the company’s Japanese partner: they were beside themselves over Bill English’s word processor. “Fuji clamored, ‘Give us this! We’ll manufacture it!'” recalls Goldman. In fact, he says, that was a near-universal reaction: “People from Europe, people from South America, marketing groups around the U.S.–everyone who went out of that conference was excited by what they had seen.”

Everyone, that is, except the copier executives, the real power brokers in Xerox. You couldn’t miss them; they were the ones standing in the background with the puzzled, So-What? look on their faces.

Indeed, one of the corporation’s purposes in calling the conference was to rally the troops for the coming era of ever-more-ferocious competition. In fairness, those executives in the background had to worry about defending the homeland now, not ten years from now. Or maybe they were simply too bound by the culture of the executive suite, vintage 1977. The xerographers lived in a world in which typing was women’s work and keyboards were for secretaries. It was a rare executive who would even deign to touch one. (TDM, p. 408)

There was a change in leadership in 1978 which recognized that the management style they had been using wasn’t working. The Japanese had entered the copier market, and were taking market share away from them. This is just what Joe Wilson had anticipated eight years earlier. Secondly, the new management recognized the vision at PARC. They wanted to create products from it. Work on the Xerox Star system began that year at SDD.

SDD had previously created a machine called Dandelion, Xerox’s most powerful computer to date. The Star would be the Dandelion translated into a business system. (Source: TAES)

Xerox and Apple

There’s been increasing awareness about the history of Xerox and Apple as time has passed, but there are some misconceptions about it that deserve to be cleared up.

Xerox and Apple developed a brief business relationship in 1979. Apple got ideas about how to create the user experience with their Lisa and Macintosh computers from Xerox PARC. The misperceptions are in how this happened. This is how the meeting between Xerox and Apple in 1979 has been perceived among those who are generally familiar with this history (from the 1999 TNT made-for-TV movie “Pirates of Silicon Valley”):

There’s some truth to this, but some of it is myth. One could almost say it was trumped up to bolster Steve Jobs’s image as a visionary. It’s a story that’s been propagated for many years, including by yours truly. So I mean to correct the record.

Much of Waldrop’s account of what happened between Xerox and Apple is a retelling of an account from Michael Hiltzik’s book, “Dealers of Lightning.” From what he says, the Apple team’s visit at PARC was not a total revelation about graphical user interfaces, but it was an inspiration for them to improve on what they had developed.

The idea of a graphical user interface on a computer was already out there. People at Apple knew of it prior to visiting Xerox. Xerox had been publishing information about the concept, and had been holding public demos of some of the graphical technology PARC had developed. So the idea of a graphical user interface was not a secret at all, as has been portrayed. However, this does not mean that every aspect of Xerox’s user interface development was made public, as I’ll discuss below.

Apple had been working on a computer called Lisa since 1978. It was a 16-bit machine that had a high-resolution bitmapped display. The Lisa team called it a “graphical computer.” Apple was approached by the Xerox Development Corporation (XDC) to take a look at what PARC had developed. The director of XDC felt that the technology needed to be licensed to start-ups, or else it would languish. Steve Jobs rebuffed the offer at first, but was convinced by Jef Raskin at Apple to form a team to go to PARC for a demonstration.

Apple and Xerox made a trade. Xerox bought a stake in Apple that was worth about $1 million, in exchange for Apple getting access to PARC’s technology. Waldrop says that Jobs and a team of Apple engineers made two visits to PARC in December 1979. According to an article from Stanford University, Jobs was not with them for the first visit. Waldrop notes that by this point Apple had already added a graphical user interface to the Lisa, but that it was clunky. By Larry Tesler’s account, there were more visits by the Apple team than Waldrop talks about. He has a different recollection of some of the details of the story as well. I include these different sources to give a “spectrum” of this history.

Going by Waldrop’s account, at their first visit in December ’79, they got a “standard” demo of the Alto, given by Adele Goldberg, that many other visitors to PARC had already seen. The group from Apple saw it being used with a mouse, the Bravo word processor, some drawing programs, etc., and then they left, apparently satisfied with what they had seen. It should be noted that this demo did not show the Xerox “desktop interface” in the sense of what people have come to know on personal computers and laptops. Each program they saw was a separate entity, which took over the entire operation of the machine, using the Alto’s graphical capabilities to show graphics and text. The Apple team did not see the desktop metaphor, which was in Smalltalk, and which was being developed for the Xerox Star. Goldberg considered Smalltalk proprietary. Jobs and the team eventually realized that they hadn’t really seen “the good stuff.”

The Apple team came back to PARC, unannounced. This is the visit that’s usually talked about in Apple-PARC lore, and is portrayed in the Pirates of Silicon Valley clip above. Jobs demanded to see the Smalltalk system *NOW*. A heated argument ensued between PARC and Xerox headquarters over this. Xerox’s executives ordered the PARC team to demonstrate Smalltalk to the Apple team, citing the partnership with Apple brokered by XDC. Bob Taylor was out of town at the time. He later said that he had no respect for Steve Jobs, and if he had been there, he would’ve kicked the Apple team out of the building, no if’s, and’s, or but’s! He figured Xerox would’ve fired him for it, but that would’ve been fine with him.

This time the people from Apple were prepared. They asked very detailed technical questions, and they got to see what Smalltalk was capable of. They saw educational software written by Goldberg, programming tools written by Larry Tesler, and animation software written by Diana Merry that combined graphics with text in a single document. They got to see multiple tasks on the screen at the same time with overlapping windows, in a “desktop” metaphor. Jobs was beside himself. He exploded, “Why hasn’t this company brought this to market?! What’s going on here? I don’t get it!” This was when Jobs had his epiphany about the graphical user interface, though Waldrop doesn’t delve into what that was. The breakthrough for him may have been the desktop metaphor, how it allowed multiple, different views of information, and multiple tasks to be worked on at the same time, along with everything else he’d seen. The way Waldrop portrays it is that Jobs realized that using a computer should be a fulfilling and fun experience for the person using it.

According to Hiltzik, the partnership between Xerox and Apple, of which the Apple stock trade had been a part, quickly fell apart after this, due to a culture clash. The Lisa’s chief programmer, Bill Atkinson, had to go off of what he remembered seeing at PARC, since he no longer had access to their detailed technical information.

So the idea that Apple (and Microsoft for that matter) “stole” stuff from Xerox is not accurate, though there was trepidation at PARC that Apple would steal “the kitchen sink.” While the visit gave the Lisa team ideas about how to make computer interaction better, and what the potential of the GUI was, Hiltzik said it wasn’t that big of an influence on the Lisa, or the Macintosh, in terms of their overall system design.

What this account means is that the visits to PARC were tangentially influential on Apple’s version of the idea of a graphical user interface. They did not go in ignorant of what the concept was, and it did not give them all of the ideas they needed to create one. It just helped make their idea better.

The microcomputer “revolution”

The people at PARC were aware of the nascent microcomputer phenomenon that was occurring under their noses. Some of them went to the Homebrew Computer Club meetings, where Steve Jobs and Steve Wozniak used to hang out. They read the upstart computer press that was raving about what Bill Gates and Steve Jobs were doing with their new companies, Microsoft and Apple Computer. According to Waldrop, it galled them that this upstart industry was getting so much attention and adoration that they felt it didn’t deserve.

“It had never occurred to us that people would buy crap,” declares Alan Kay, who considered the hobbyists in their garages down the hill to be very bright and very creative ignoramuses–undisciplined kids who didn’t read and didn’t have a clue about what had already been done. They were successful only because their customers were just as unsophisticated. “What none of us was thinking was that there would be millions of people out there who would be perfectly happy with the McDonald’s hamburger approach.” (TDM, p. 437)

Steve Jobs was somewhat of an exception. At least he got a hint of “what had already been done.” It took some prodding, but once he got it, he paid attention. Still, he didn’t fully understand the significance of the research that went into what he saw. For example, Jobs was very hostile to the idea of computer networking at the time, because he thought that would deprive the personal computer user of their autonomy. Freedom from dependency on larger computer systems was a notion he held to ideologically. When he railed against IBM as “big brother” it wasn’t just for show. If only he had been aware of Licklider’s vision for the internet (which was published at the time), the notion of a distributed network, with independent units, and the DARPA work that was creating it, perhaps he would’ve cottoned to it the way he had the graphical user interface. We’ll never know. He came to understand the importance of the network features that had been developed at PARC about 6 years later when he left Apple and started up NeXT.

There’s a famous quote from Jobs about his visits to PARC that illustrates what I’m talking about, from the PBS mini-series, “Triumph of the Nerds” (I wrote a post about this mini-series here):

They showed me, really, three things, but I was so blinded by the first one that I didn’t really ”see” the other two. One of the things they showed me was object-oriented programming. They showed me that, but I didn’t even “see” that. The other one they showed me was really a networked computer system. They had over 100 Alto computers all networked, using e-mail, etc., etc. I didn’t even “see” that. I was so blinded by the first thing they showed me, which was the graphical user interface. I thought it was the best thing I had ever seen in my life. Now, remember it was very flawed. What we saw was incomplete. They had done a bunch of things wrong, but we didn’t know that at the time. Still, though, the germ of the idea was there, and they had done it very well. And within ten minutes it was obvious to me that all computers would work like this, someday.

Too much, too late for Xerox

Xerox released the Star in 1981 as the 8010 Information System.

It had a Smalltalk-like graphical user interface, anywhere from 384 kilobytes of memory up to 1.5 MB, an 8″ floppy drive, a 10 to 40 MB hard disk, a monitor measuring 17″ diagonally, a mouse, a bevy of system features, Ethernet, and laser printing. (source: The Xerox “Star”: A Retrospective) The 8010 cost $16,500 per unit, though a customer couldn’t purchase just the computer. It was designed to be an integrated system, with networking and laser printing included. A minimal installation cost $100,000 (about $252,438 in today’s money). Xerox was going for the Fortune 500, which could afford expensive, large-scale systems. Even though the designers thought of it as a “personal computer” system, the 8010 was marketed under the old mainframe business model.

The designers at SDD put the kitchen sink into it. It had every cool thing they could think of. The system was designed so that no third-party software could be installed on it at all. All of the hardware and software came from Xerox, and had to be installed by Xerox employees. This was also par for the course with typical mainframe setups.

In the Star operating system, all of the software was loaded into memory at boot-up, and kept memory-resident (very much like Smalltalk). Users didn’t worry about what applications to run. All they had to focus on was their documents, since all documents and data were implicitly linked to the appropriate software. It presented an object-oriented approach to information. It didn’t say to you, “You’re working with a word processor.” Instead, you worked with your document. The system software just accommodated the document by surreptitiously activating the appropriate part of the system for its manipulation, and presenting the appropriate interface.

Since the Xerox brass recognized they knew nothing about computers, they turned the Star’s design totally over to SDD. There appeared to have been no input from marketing. Even so, as Steve Jobs said of the executives, “They were copier-heads. They just had no clue about a computer, what it could do.” It’s unclear whether input from marketing would’ve helped with product development. I have a feeling “the innovator’s dilemma” applies here.

The designers at SDD were also told that the cost of the product was no object, since their target market was used to paying hundreds of thousands of dollars for large-scale systems. The Xerox executives miscalculated on this point. While it’s true that their target market had no problem paying the system’s price tag, personal computing was a new, untested concept to them, and they were wary about risking that much money on it. Xerox didn’t have a low-risk, low-cost entry configuration to offer. It was all or nothing. Microcomputers were a much easier sell in this environment, because their individual cost, by comparison, went unnoticed in corporate budgets. Corporate managers were able to sneak them in, buying them with petty cash, even though IT managers (who believed wholeheartedly in mainframes) tried their darnedest to keep them out.

The microcomputer market, which just about everyone at Xerox saw as a joke, was eating their lunch. Bob Frankston and Dan Bricklin at VisiCorp (both alumni of the Project MAC/Multics project) had come out with VisiCalc, the world’s first commercial spreadsheet software, in 1979, and it was only available for micros. Xerox didn’t have an equivalent on the 8010 when it was released. They had put all of their “eggs” into word processing, databases, and e-mail. These things were needed in business, but it was the same thing the researchers at PARC had run into when they tried to show the Alto to the Xerox brass in years passed: People in the corporate hierarchy saw themselves as having certain roles, and they thought they needed different tools than what Xerox was offering. Word processing was what stenographers needed, in the minds of business customers. Managers didn’t want it. They wanted spreadsheets, which were the “killer applications” of the era. They would buy a microcomputer just so they could run a spreadsheet on it.

The designers at SDD didn’t understand their target market. The philosophy at PARC was “eat your own dog food” (though I imagine they used a different term for it). From what I’ve heard, listening to people who were part of the IPTO in the 1960s, it was Doug Engelbart who invented this development concept. The problem for Xerox was this applied to product development as well as to overall quality control. From the beginning, they wanted to use all of the technology they developed, internally, with the idea that if they found any deficiencies, there would be no running away from them. It would motivate them to make their systems great.

Thacker and Lampson mentioned in a retrospective, which I refer to at the end of this post, that someone at PARC had come up with a spreadsheet for the Alto, but that nobody there wanted to use it. They were not accountants. Xerox eventually released a spreadsheet for the 8010, once they saw the demand for it, but the writing was already on the wall by then.

It’s hard for me to say whether this was intentional, but the net effect of the way SDD designed the Star resulted in an implicit assumption that customers in their target market would want the capabilities that the designers thought were important. Their focus was on building a complete integrated system, the likes of which no one had ever seen. The problem was nothing in their strategy accounted for the needs and perceptions that business customers would assert. They may have assumed that customers would be so impressed by the innovativeness of the system that it would sell itself, and people would adjust their roles to it in a McLuhan-esque “the medium is the message” sort of way.

The researchers at PARC recognized that producing a microcomputer had always been an option. In 1979, Bob Belleville suggested going with a 16-bit microcomputer design, maybe using the Motorola 68000, or the Intel 8086 processor, instead of going forward with the Star. He built a prototype and showed that it could work, but the team working on the Star didn’t have the patience for it. They would’ve had to throw out everything they had done, hardware and software, and start over. Secondly, they wouldn’t have been able to do as much with it as they were able to do with the approach they had been pursuing. Thacker and Lampson saw as well that building a microcomputer would be more expensive than going ahead with their approach. It was, however, a fateful decision, because the opposite would be true two years later, when the 8010 Information System was released.

Lampson said, ironically, that this time, “the problem wasn’t a shortage of vision at headquarters. If anything, it was an excess of vision at PARC.”

When Apple released their Lisa computer in 1983, Xerox realized that they had missed their chance. The future belonged to IBM, Apple, and Microsoft.


From about 1980 on, people left PARC to join other companies. They were bleeding talent. Taylor was seen as part of the problem. He had an overriding vision, and it was his way, or the highway, so others have said. He understood full well where computing was going. He was just ahead of his time. He could be very supportive, if you agreed with his vision, but he had utter contempt for any ideas he didn’t approve of, and he would “take out the flamethrower” if you opposed him. This sounds a lot like what people once complained about with Steve Jobs, come to think of it. The director of PARC kindly asked Taylor to change his attitude. He took it as an insult, and left in frustration in 1983, taking some of PARC’s best engineers with him.

From looking at the story, it appears the failure of the Star project was “the last straw” for Xerox’s foray into computer research. After Taylor left, the era of innovative computer research ended at PARC.

The visions at Xerox were grandiose, and were too much, too late for the market. I don’t mean to take anything away from what they did by saying this. What they had was pretty good. It represented the future, but it was too far ahead of where the business computing market was. Competitors had grabbed the attention of customers, and they preferred what the competition was offering.

The PARC researchers got it both ways. When they had the chance to develop products in 1975, ahead of the explosion in the microcomputer market, their vision wasn’t recognized by the company that housed them. By the time it was recognized, the market had changed such that much less powerful machines, backed by more innovative business models, won out.

Even though Alan Kay brought the idea of a small, handheld computer to PARC, something that ordinary people would love to use, he valued computing power over the size of the machine. As he would later say about that period, the idea of the Dynabook was more of a service metaphor. The size and form of the hardware was incidental to the concept. (source: An Interview with Computing Pioneer Alan Kay) Thacker and Lampson, the designers of the 8010, shared that value as well. By the late 1970s the market was thinking “small is beautiful,” and they were willing to tolerate clunky, low-powered machines, because their economies of scale met immediate needs, and they created less friction from corporate politics.

Apple and Microsoft used ideas developed at PARC to further develop the personal computer market, and so watered down pieces of that vision got out to customers over about 20 years. Today we live in a world that resembles what Bob Taylor envisioned, with individual computers, networking, and digital printing, using graphical systems.

PARC’s legacy

The outside world would come to know the desktop graphical user interface metaphor because of Smalltalk, though the metaphor was repurposed away from Alan Kay’s powerful ideas about a modeling system, into a system that made it easy to run applications, and emulated integrating older media together into virtual paper documents.

To me, the most interesting contributor to the desktop interface was Diana Merry. She was originally hired at PARC as a secretary. She just happened to understand Alan Kay’s goals, and so she was invited into the LRG. She and Kay created the first implementation of overlapping windows, in Smalltalk. She also wrote a lot of the basic software Smalltalk used to display and animate graphics. (Sources: “The Mouse and the Desktop — Designing Interactions,” by Bill Moggridge, and TEHS)

The Paint, Draw, and Write applications that appeared on the Apple Lisa and the first Macintosh systems were inspired by similar works that had been created years earlier at PARC.

Microsoft Windows, and Microsoft Word benefitted from the research that was done at PARC, though some inspiration came from the Macintosh as well. The look and feel of the first versions of Windows owed more to the X/Window system on Unix. Where Microsoft copied Apple was in how people interacted with Windows (icons and menus), and the application suite that came with the system. (source: The Secret Origin of Windows)

Software developers who have been working on apps. for Apple products in the most recent generations of systems may be interested to know that the Objective-C language they’ve used was first created by a company called StepStone in the early 1980s. The design of the language and its runtime were somewhat influenced by the Smalltalk system.

Several companies licensed a version of Smalltalk, called Smalltalk-80, from Xerox during the 1980s. Among them were Tektronix, Hewlett-Packard, Digital Equipment Corp., and Apple. (source: “Smalltalk-80: Bits of History, Words of Advice”) Apple got a version running on the Macintosh XL in 1985. (source: The Long View) Apple’s version of Smalltalk was updated in the mid-1990s by Alan Kay and some of the original Learning Research Group gang from Xerox. They got Apple’s permission to release it into the public domain, and it has since been ported to many platforms. Professional developers call it by its new name, “Squeak.” An educational version also exists, maintained by the Squeakland Foundation, which goes by the name “Etoys.”

Charles Simonyi joined Microsoft. He brought his knowledge from developing Bravo with him, and it went into creating Microsoft Word. He became the lead developer on their Multiplan, and Excel spreadsheet software.

Where are they now?

Stephen Lukasik became a Chief Scientist at the FCC from 1979-1982. He is a member of the International Institute for Strategic Studies, the American Physical Society, and the American Association for the Advancement of Science. He is a founder of The Information Society journal, and has served on the Boards of Trustees of Harvey Mudd College and Stevens Institute of Technology. (Source: Georgia Tech)

Larry Roberts was chief executive at Telenet until 1980. Telenet was sold to GTE in 1979 and subsequently became the data division of Sprint. In 1983 he became the CEO of NetExpress, an Asynchronous Transfer Mode (ATM) equipment company. Roberts then became president of ATM Systems from 1993 to 1998. He then went back to packet networking, founding Caspian Networks, which focused on IP flow management (IP as in “Internet Protocol”), until 2004. He founded Anagran in effort to do what Caspian did, but more efficiently. (Source: Larry Roberts’s home page)

J.C.R. Licklider – In the late 1970s Lick visited Xerox PARC regularly. He served on the Committee on Government Relations at the Association of Computing Machinery (ACM), and as deputy chairman of the Social Security Administration’s Data Management System. He spent a year in 1978 on a task force commissioned by the Carter Administration, which examined the government’s data-processing needs. He served as president of the Boston Computer Society. He was an investor and advisor to a company called Infocom in 1979, which was founded by eight of his former students from his Dynamic Modeling group at MIT. He spent part of his time working at VisiCorp. He continued to work at MIT until he retired in 1985. He died on June 26, 1990.

Ed Feigenbaum – In 1984 he became a Fellow at the American College of Medical Informatics. From 1994 to 1997 he served as Chief Scientist of the U. S. Air Force. He founded the Knowledge Systems Laboratory at Stanford University, and is now professor emeritus at Stanford. He was co-founder of several start-ups, such as IntelliCorp, Teknowledge, and Design Power Inc. He has served on the National Science Foundation Computer Science Advisory Board, on the National Research Council’s Computer Science and Technology Board, and as a member of the Board of Regents of the National Library of Medicine. He is a Fellow at the the Association for the Advancement of Artificial Intelligence, the American Institute of Medical and Biological Engineering, and of the American Association for the Advancement of Science. He is a member of the National Academy of Engineering and of the American Academy of Arts and Sciences. (Source: Feigenbaum’s Turing Award citation at the ACM)

George Heilmeier became vice president at Texas Instruments in 1977. In 1983 he was promoted to Senior Vice President and Chief Technical Officer. In his current position, he is responsible for all TI research, development, and engineering activities. From 1991-1996 he also served as president and CEO of Bellcore (now Telcordia), ultimately overseeing its sale to Science Applications International Corporation (SAIC). He served as the company’s chairman and CEO from 1996-1997, and afterwards as its chairman emeritus. He serves on the board of trustees of Fidelity Investments and of Teletech Holdings, and the Board of Overseers of the School of Engineering and Applied Science of the University of Pennsylvania. He is a member of the National Academy of Engineering, the Defense Science Board, and is Chairman of the Technical Advisory Board of Southern Methodist University. (sources: Wikipedia, and IEEE Global History Network)

It should be noted that Heilmeier is credited with being the inventor of the Liquid Crystal Display (LCD), a technology Alan Kay hoped to use for his Dynabook in the 1970s, and which became the basis for the digital display most of us use today.

Alan Kay left PARC on sabbatical in 1980, and never came back. He came to Atari in 1981, as Chief Scientist, doing research on interactive computing (you can see some samples of it here). He then joined Apple as a research Fellow in 1984, where he worked on improving education in conjunction with technology. He joined Disney as a Fellow and Imagineer from 1996 to 2001. He founded Viewpoints Research Institute in 2001. He then became a research fellow at Hewlett-Packard from 2002 to 2005. During that time he was a Visiting Professor at Kyoto University, an adjunct professor of Computer Science at MIT, and was involved with the development of the XO Laptop, developed by the One Laptop Per Child program at MIT. Today he is an adjunct professor of Computer Science at UCLA, and he continues his work at Viewpoints. He is also on the advisory board of TTI/Vanguard. Since 2006 Viewpoints has been working on a project, sponsored by the National Science Foundation, to reinvent personal computing. (sources: The New York TimesChap. 12 from Howard Rheingold’s “Tools For Thought,” Wikipedia, Bio. on Alan Kay at Answers.comVPRI: Inventing Fundamental New Computing Technologies)

Bob Taylor went on to found the Systems Research Center (SRC) at Digital Equipment Corp. (DEC) in 1984. He retired from DEC in 1996.

Butler Lampson also left PARC with Taylor, and joined him at Digital Equipment Corp. He became a Fellow of the ACM in 1992. He now works at Microsoft Research, and is an adjunct professor at MIT. He became a Fellow at the Computer History Museum in 2008.

Dan Ingalls left PARC to work at Apple in 1984. Beginning in 1987 he helped run the Homestead Hotel, a family business, until 1993. He stayed on with Apple until 1996. From 1996 to 2001 he was Principal Staff Director at Disney Imagineering. He worked as a consultant for Hewlett-Packard and at Viewpoints Research Institute from 2001 to 2005. He joined Sun Labs in 2005, where he developed his newest project, called Lively Kernel. He left Sun in 2010 to become a Fellow at SAP, where he continues to work today. He continues development of his Lively Kernel Project at the Hasso Plattner Institute. (Sources: Dan Ingalls’s Linkedin page, and his Wikipedia page)

Diana Merry – After leaving Xerox in 1986, she has continued her work in Smalltalk with various employers. You can see her complete work history at her LinkedIn page.

Chuck Thacker left PARC the same time Bob Taylor did, and joined DEC as a founder of its Systems Research Center. He then joined Microsoft in 1997 to help found Microsoft Research in Cambridge, UK. He returned to the U.S., and developed the technology which was used in Microsoft’s Tablet PC. He is currently working at Microsoft Research in Silicon Valley on computer architecture.

Adele Goldberg became president of the Association of Computing Machinery (ACM) from 1984 to 1986, and continued to have a long association with the organization, taking on various roles. She continued on at PARC until 1987. She wanted to make sure that Smalltalk technology got out to a wider audience, so she worked out a technology exchange agreement with Xerox in the late 1980s and founded ParcPlace Systems, which commercialized a version of Smalltalk. She served as CEO and chairwoman of ParcPlace until 1995, when the company merged with Digitalk, another Smalltalk vendor, to become ParcPlace-Digitalk. In 1997 the company changed its name to ObjectShare. In 1999 ObjectShare was sold to Cincom, which has continued to develop and sell a Smalltalk development suite. Goldberg was inducted as a Fellow at the ACM in 1994. She co-founded Neometron in 1999, and now also works at Bullitics. She is a board member of Cognito Learning Media. She has continued her interest in education, formulating computer science courses at community colleges in the U.S. and abroad.

Charles Simonyi went to work at Microsoft in 1981. He led their development efforts to create Word, Multiplan, and Excel. He left Microsoft in 2002 to found Intentional Software, where he’s been at work on what I’d call his “Domain-Oriented Development” software and techniques.

Larry Tesler went to work at Apple in 1980, becoming Vice President of the Advanced Technology Group, and Chief Scientist. He worked on the team that developed the Lisa computer. In 1990 he led the effort to develop the Apple Newton, one of the first of what we’d recognize as a personal digital assistant. Tesler left Apple in 1997 to co-found a company called Stagecast Software. In 2001 he joined Amazon.com as its Vice President of Shopping Experience. In 2005 he joined Yahoo! as Vice President of its User Experience and Design group. In 2008 he went to work for 23andMe, a personal genetics information company. Since 2009 he’s worked as an independent consultant.

Timothy Mott left Xerox PARC to co-found Electronic Arts in 1982. In 1990 he became Director at Electronic Arts, and co-founded Macromedia, staying with them until 1994. In 1995 he co-founded Audible.com, and stayed with them until 1998. He stayed on with Electronic Arts until 2007. You can see his complete work profiles at Bloomberg BusinessWeek and CrunchBase.

Doug Brotz – It’s been difficult finding much information on him. All I have is that he joined Adobe and was a co-developer of the PostScript laser printer control language.

Andrew Birrell – You can find a summary of his work history here.

Roy Levin became a senior researcher at the DEC Systems Research Center in 1984. In 1996 he became its director. In 2001 he co-founded Microsoft Research in Silicon Valley, became a Distinguished Engineer, and its Managing Director. He continues work there today.

Roger Needham worked as a consultant at Xerox PARC from 1977-1984. He then worked at DEC’s Systems Research Center from 1984-1997, and at Hitachi Advanced Research Laboratory from 1994-1997. He became a Fellow at the Royal Academy of Engineering in 1993, and of the ACM in 1994. He was a Fellow at Wolfson College, in Cambridge, UK, from 1966-2002. He joined Microsoft Research in 1997, and founded their European Research Labs. He was a longtime member of the International Association for Cryptographic Research, the IEEE Computer Society Technical Committee on Security and Privacy, and the University Grants Committee, an advisory committee to the British government. He died on March 1, 2003. (source: Wikipedia)

Michael Schroeder – I haven’t found detailed information on Schroeder, except to say that as a professor at MIT he worked on the Multics project (I covered this project in Part 2 of this series), before coming to Xerox PARC, and that after PARC he worked at DEC’s Systems Research Center. He then came to Microsoft Research in 2001, where he continues to work today. He became a Fellow of the ACM in 2004.

Clarence Ellis – After leaving Xerox PARC, he became head of the Groupware Research Program at the Microelectronics Computer Consortium in Austin, TX. He also worked at Los Alamos Labs, and Argonne National Laboratory. He held academic positions at Stanford, the University of Texas, MIT, and the Stevens Institute of Technology. In 1991 he became the chief architect of the FlowPath workflow product at Bull S.A. In 1992 he came to the University of Colorado at Boulder as a professor of computer science, and, with Gary Nutt, formed the Collaborative Technology Research Group (CTRG) to work on project workflow systems. He became professor emeritus of computer science at CU in 2010. He was on the editorial board of various journals. He was a member of the National Science Foundation (NSF) Computer Science Advisory Board, the University of Singapore ISS International Advisory Board, and the NSF Computer Science Education Committee. He died on May 17, 2014. (sources: Computer Scientists of the African DiasporaCTRG Groupware and Workflow Research, the Daily Camera)

A note I found in Ellis’s bio. also says that he was the first African American to receive a Ph.D. in computer science, in 1969, from the University of Illinois at Urbana-Champaign. He did his post-graduate work developing the Illiac IV supercomputer, an ARPA/IPTO project, and one of the first massively parallel computer systems.

Gary Nutt worked at Xerox PARC from 1978-1980. He worked on collaboration systems at Bell Labs in Denver, CO. from 1980-81. He took a couple executive roles at technology firms in Boulder, CO. from 1981-1986. He returned to the University of Colorado at Boulder in 1986 as a CS professor (he was previously an associate CS professor at CU Boulder from 1972-1978). While on sabbatical in 1993, he worked for Group Bull in Paris, France, developing collaboration products. In 2000 he worked as VP of Engineering for Bookface.com, managing their intellectual property. He went to Inktomi in 2001 to work on content and media distribution over the internet. He also advised on managing their intellectual property. He retired from CU Boulder in 2010, and is now professor emeritus. You can see his work history in more detail at his web page, which I’ve linked to here.

Steve Jobs – I wrote about Jobs’s work in a separate post. He died on October 5, 2011.

Bob Belleville – I couldn’t find much on him. What I have is that he joined Apple in 1981, becoming the chief engineer on the Lisa, and later the Macintosh project. He later joined Silicon Graphics’s R&D Dept. (Source: Good-bye Woz and JobsMaking the Macintosh)

You can watch a retrospective from 2001 given by Chuck Thacker and Butler Lampson on their days at PARC, and what they did, here, if you’re interested. It gets pretty technical, and is an hour and 20 minutes long.

You can see a 2010 interview with Adele Goldberg at the Computer History Museum, where she reviews her academic, educational, and business career here. It’s about an hour and 30 minutes.

Here’s a presentation given by the University of Texas at Austin in 2010, with Mitchell Waldrop, the author of “The Dream Machine,” and Michael Hiltzik, the author of “Dealers of Lightning,” along with an interview with Bob Taylor. It’s a nice bookend to the history I’ve discussed here.


The closing chapter in Waldrop’s book is called, “Lick’s Kids.” It talks about the people who were mentored by Licklider, either through ARPA, or at MIT, and who went out into the world to bring their ideas to life. It also talked about his waning years. He lived to see “the wheel reinvented” with microcomputers. The companies that were going gangbusters with them were repeating many of the lessons he and his researchers had learned in the 1960s, working at ARPA. The implication being that if they had merely taken time to look at what had already been learned 20 years earlier they would’ve avoided the same mistakes. He also saw the first glimmers of his vision of the Multinet come into being with the internet.

When reflecting on his life’s work, Lick was humble. He didn’t give himself much credit for creating our digital world. He thought of himself as just happening to be at the right place at the right time while some very bright people did the real work. But those who were mentored, and funded by him gave him a great deal of credit. They said if it wasn’t for him, their ideas would not have gotten off the ground, and often he was the only one who could see promise in them. He was indeed in the right place at the right time, but what was important was that he was in the right position to give them the support they needed to bring their dreams into reality.

I’ll talk more about Bob Kahn, and the development of the internet, in Part 4.

—Mark Miller, https://tekkie.wordpress.com

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I came across this interview with Lesley Chilcott, the producer of “An Inconvenient Truth” and “Waiting For Superman.” Kind of extending her emphasis on improving education, she produced a short 9-minute video selling the idea of “You should learn to code,” both to adults and children. It addresses two points: 1) the anticipated shortage of programmers needed to write software in the future, and 2) the increasing ubiquity of programming in all sorts of fields where people would think it wouldn’t exist, such as manufacturing and agriculture.

The interview gets interesting at 3 minutes 45 seconds in.

Michelle Fields, the interviewer, asked what I thought were some insightful questions. She started things off with:

It seems as though the next generation is so fluent in technology. How is it that they don’t know what computer programming is?

Chilcott said:

I think the reason is, you know, we all use technology every day. It’s surrounding us. Like, we can debate the pro’s and con’s of technology/social media, but the bottom line is it’s everywhere, right? So I think a lot of people know how to read it. They grow up playing with an iPhone or something like that, but they don’t know how to write it. And so when you say, “Do you know what this is,” specifically, or what this job is–and you know, those kids are in first, second, fifth grade–they know all about it, but they don’t know what the job is.

I found this answer confusing. She’s kind of on the right track, thinking of programming as “writing.” I cut her some slack, because as she admits in the interview, she’s just started programming herself. However, as I’ve said before, running software is not “reading.” It’s really more like being read to by a machine, like listening to an audio book, or someone else reading to you. You don’t have to worry about the mental tasks of pronunciation, sentence construction, or punctuation. You can just listen to the story. Running software doesn’t communicate the process that the code is generating, because there’s a lot that the person using it is not shown. This is on purpose, because most people use software to accomplish some utilitarian task unrelated to how a computer works. They’re not using it to understand a process.

The last sentence came across as muddled. I think what she meant was they know all about using technology, but they don’t know how to create it (“what the job is”).

Fields then asked,

There was this study which found that 56% of students would rather eat broccoli than learn math. Do you think that since computer programming is somewhat related to math, that that’s the reason children and students shy away from it?

Chilcott said:

It could be. That is one of the myths that exist. There is some, you know, math, but as Bill Gates and some other people said, you know, addition, subtraction–It’s much more about problem solving, and I think people like to problem-solve, they like mysteries, they like decoding things. It’s much more about that than complicated algorithms.

She’s right that there is problem solving involved with programming, but she’s either mistaken or confusing math with arithmetic when she says that the relationship between math and programming is a “myth.” I can understand why she tries to wave it off, because as Fields pointed out, most students don’t like math. I contend, as do some mathematicians, this is due to the way it’s taught in our schools. The essence of math gets lost. Instead it’s presented as a tool for calculation, and possibly a cognitive development discipline for problem solving, both of which don’t communicate what it really is, and remove a lot of its beauty.

In reality math is pervasive in programming, but to understand why I say this you have to understand that math is not arithmetic–addition, subtraction, like she suggests. This confusion is common in our society. I talk more about this here. Having said this, it does not mean that programming is hard right off the bat. The math involved has more to do with logic and reasoning. I like the message in the video below from a couple of the programmers interviewed: “You don’t have to be a genius to know how to code. … Do you have to be a genius to do math? No.” I think that’s the right way to approach this. Math is important to programming, but it’s not just about calculating a result. While there’s some memorization, understanding a programming language’s rules, and knowing what different things are called, that’s not a big part of it.

The cool thing is you can accomplish some simple things in programming, to get started, without worrying about math at all. It becomes more important if you want to write complex programs, but that’s something that can wait.

My current understanding is the math in programming is about understanding the rules of a system and what statements used in that system imply, and then understanding the effects of those implications. That sounds complicated, but it’s just something that has to be learned to do anything significant with programming, and once learned will become more and more natural. I liken it to understanding how to drive a car on the road. You don’t have to learn this concept right away, though. When first starting out, you can just look at and enjoy the effects of trying out different things, exploring what a programming environment offers you.

Where Chilcott shines in the interview above is when she becomes the “organizer.” She said that even though 95% of the schools have computers and internet access, only 10% have what she calls a “computer science” course. (I wish they’d go back to calling it a “programming course.” Computer science is more than what most of these schools teach, but I’m being nit-picky.) The cool thing about Code.org, a web site she promotes, is that it tries to locate a school near you that offers programming courses. If there aren’t any, no problem. You can learn some basics of programming right inside your browser using the online tools that it offers on the site.

The video Chilcott produced is called “Code Stars” in the above interview, but when I went looking for it I found it under the name “the Code.org film,” or, “What Most Schools Don’t Teach.”

Here is the full 9-minute video:

If you want the shorter videos, you can find them here.

The programming environment you see kids using in these videos is called “Scratch.”

Gabe Newell said of programming:

When you’re programming, you’re teaching possibly the stupidest thing in the entire universe–a computer–how to do something.

I see where Newell is going with this, but from my perspective it depends on what programming environment you’re using. Some programming languages have the feel of you “teaching” the system when you’re programming. Others have the feel of creating relationships between simple behaviors. Others, still, have the feel of using relationships to set up rules for a new system. Programming comes in a variety of approaches. However, the basic idea that Newell gets across is true, that computers only come with a set of simple operations, and that’s it. They don’t do very much by themselves, or even in combination. It’s important for those new to programming to learn this early on. Some of my early experiences in programming match those of new programmers even today. One of them is, when using a programming language, one is tempted to assume that the computer will infer the meaning of some programming expression from context. There is some context used in programming, but not much, and it’s highly formalized. It’s not intuitive. I can remember the first time I learned this it was like the joke where, say, someone introduces his/her friend to a dumb, witless character in a skit. He/she says, “Say hi to my friend, Frank,” and the dummy says, “Hi to my friend Frank.” And the guy/gal says, “NO! I mean…say hello,” making a hand gesture trying to get the two to connect, and the dummy might look at the friend and say, “Hello,” but that’s it. That’s kind of a realization to new programmers. Yeah, the computer has to have almost everything explained to it (or modeled), even things we do without thinking about it. It’s up to the programmer to make the connections between the few things the computer knows how to do, to make something larger happen.

Jack Dorsey talked about programming in a way that I think is important. His ultimate goal when he started out was to model something, and make the model malleable enough that he could manipulate it, because he wanted to use it for understanding how cities work.

Bill Gates emphasized control. This is a common early motivation for programmers. Not necessarily controlling people, but controlling the computer. What Gates was talking about was what I’d call “making your own world,” like Dorsey was saying, but he wanted to make it real. When I was in high school (late 1980s) it was a rather common project for aspiring programming students to create “matchmaking” programs, where boys and girls in the whole school would answer a simple questionnaire, and a computer program that a student had written would try to match them up by interests, not unlike some of the online dating sites that are out there now. I never heard of any students finding their true love through one of these projects, but it was fun for some people.

Vanessa Hurst said, “You don’t have to be a genius to know how to code. You need to be determined.” That’s pretty much it in a nutshell. In my experience everything else flowed from determination when I was learning how to do this. It will drive you to learn what you need to learn to get it, even if sometimes it’s subject matter you find tedious and icky. You learn to just push through it to get to the glorious feeling at the end of having accomplished what you set out to do.

Newell said at the end of the video,

The programmers of tomorrow are the wizards of the future. You’re going to look like you have magic powers compared to everybody else.

That’s true, but this has been true for a long time. In my professional work developing custom database solutions for business customers I had the experience of being viewed like a magician, because customers didn’t know how I did what I did. They just appreciated the fact that I could do it. I really don’t mean to discourage anyone, because I still enjoy programming today, and I want to encourage people to learn programming, but I feel the need to say something, because I don’t want people to get disillusioned over this. This status of “wizard,” or “magician” is not always what it’s cracked up to be. It can feel great, but there is a flip side to it that can be downright frustrating. This is because people who don’t know a wit of what you know how to do can get confused about what your true abilities are, and they can develop unrealistic expectations of you. I’ve found that wherever possible, the most pleasurable work environment is working among those who also know how to code, because we’re able to size each other up, and assign tasks appropriately. I encourage those who are pursuing software development as a career to shoot for that.

A couple things I can say for being able to code are:

  • It makes you less of a “victim” in our technology world. Once you know how to do it, you have an idea about how other programs work, and the pitfalls they can fall into that might compromise your private information, allow a computer cracker to access it, or take control of your system. You don’t have to feel scared at the alarming “hacking” or phishing reports you hear on the news, because you can be choosey about what software you use based on how it was constructed, what it’s capable of, how much power it gives you (not someone else), and not just base a decision on the features it has, or cool graphics and promotion. You can become a discriminating user of software.
  • You gain the power to create the things that suite you. You don’t have to use software that you don’t like, or you think is being offered on unreasonable terms. You can create your own, and it can be whatever you want. It’s just a matter of the knowledge you’re willing to gather and the amount of energy you’re willing to put into developing the software.

Edit 5-20-2013: While I’m on this subject, I thought I should include this video by Mitch Resnick, who has been involved in creating Scratch at MIT. Similar to what Lesley Chilcott said above, he said, “It’s almost as if [users of new technologies] can read, but not write,” referring to how people use technology to interact. I disagreed with the notion, above, that using technology is the same as reading. Resnick hedged a bit on that. I can kind of understand why he might say this, because by running a Scratch program, it is like reading it, because you can see how code creates its results in the environment. This is not true, however, of much of the technology people use today.

Mark Guzdial asked a question a while back that I thought was important, because it brings this issue down to where a lot of people live. If the kind of literacy I’m going to talk about below is going to happen, the concept needs to be able to come down “out of the clouds” and become more pedestrian. Not to say that literacy needs to be watered down in toto (far from it), but that it should be possible to read and write to communicate everyday ideas and experiences without being super sophisticated about it. What Mark asked was, in the context of a computing medium, what would be the equivalent of a “note to grandma”? I remember suggesting Dan Ingalls’s prop-piston concept from his Lively Kernel demos as one candidate. Resnick provided what I thought were some other good ones, but in the context of Mother’s Day.

Context reversal

The challenge that faces new programmers today is different from when I learned programming as a child in one fundamental way. Today, kids are introduced to computers before they enter school. They’re just “around.” If you’ve got a cell phone, you’ve got a computer in your pocket. The technology kids use presents them with an easy-to-use interface, but the emphasis is on use, not authoring. There is so much software around it seems you can just wish for it, and it’s there. The motivation to get into programming has to be different than what motivated me.

When I was young the computer industry was still something new. It was not widespread. Most computers that were around were big mainframes that only corporations and universities could afford and manage. When the first microcomputers came out, there wasn’t much software for them. It was a lot easier to be motivated to learn programming, because if you didn’t write it, it probably didn’t exist, or it was too expensive to get (depending on your financial circumstances). The way computers operated was more technical than they are today. We didn’t have graphical user interfaces (at first). Everything was done from some kind of text command line interface that filled the entire screen. Every computer came with a programming language as well, along with a small manual giving you an introduction on how to use it.

PC-DOS, from Wikipedia

It was expected that if you bought a computer you’d learn something about programming it, even if it was just a little scripting. Sometimes the line between what was the operating system’s command line interface, and what was the programming language was blurred. So even if all you wanted to do was manipulate files and run programs, you were learning a little about programming just by learning how to use the computer. Some of today’s software developers came out of that era (including yours truly).

Computer and operating system manufacturers had stopped including programming languages with their systems by the mid-1990s. Programming languages had also been taken over by professionals. The typical languages used by developers were much harder to learn for beginners. There were educational languages around, but they had fallen behind the times. They were designed for older personal computer systems, and when the systems got more sophisticated no one had come around to update them. That began to be remedied only in the last 10 years.

Computer science was still a popular major at universities in the 1990s, due to the dot-com craze. When that bubble burst in 2000, that went away, too. So in the last 18 years we’ve had what I’d call an “educational programming winter.” Maybe we’ll see a revival. I hope so.

Literacy reconsidered

I’m directing the rest of this post to educators, because there are some issues around a programming revival I’d like to address. I’m going to share some more detailed history, and other perspectives on computer programming.

What many may not know is that we as a society have already gone through this once. From the late 1970s to the mid-1980s there was a major push to teach programming in schools as “computer literacy.” This was the regime that I went through. The problem was some mistakes were made, and this caused the educational movement behind it to collapse. I think the reason this happened was due to a misunderstanding of what’s powerful about programming, and I’d like educators to evaluate their current thinking in light of this, so that hopefully they do not repeat the mistakes of the past.

As I go through this part, I’ll mostly be quoting from a Ph.D. thesis written by John Maxwell in 2006 called Tracing the Dynabook: A Study of Technocultural Transformations.” (h/t Bill Kerr)

Back in the late 1970s microcomputers/personal computers were taking off like wildfire with Apple II’s, and Commodore VIC-20’s, and later, Commodore 64’s, and IBM PCs. They were seen as “the future.” Parents didn’t want their children to be “left behind” as “technological illiterates.” This was the first time computers were being brought into the home. It was also the first time many schools were able to grant students access to computers.

Educators thought about the “benefits” of using a computer for certain cognitive and social skills.  Programming spread in public school systems as something to teach students. Fred D’Ignazio wrote in an article called “Beyond Computer Literacy,” from 1983:

A recent national “computers in the schools” survey conducted by the Center for the Social Organization of Schools at Johns Hopkins University found that most secondary schools are using computers to teach programming. … According to the survey, the second most popular use of computers was for drill and practice, primarily for math and language arts. In addition, the majority of the teachers who responded to the survey said that they looked at the computer as a “resource” rather than as a “tool.”

…Another recent survey (conducted by the University of Maryland) echoes the Johns Hopkins survey. It found that most schools introduce computers into the curriculum to help students become literate in computer technology. But what does this literacy entail?

Because of the pervasive spread of computers throughout our society, we have all become convinced that computers are important. From what we read and hear, when our kids grow up almost everyone will have to use computers in some aspect of their lives. This makes computers, as a subject, not only important, but also relevant.

An important, relevant subject like computers should be part of a school’s curriculum. The question is how “Computers” ought to be taught.

Special computer classes are being set up so that students can play with computers, tinker with them, and learn some basic programming. Thus, on a practical level, computer literacy turns out to be mere computer exposure.

But exposure to what? Kids who are now enrolled in elementary and secondary schools are exposed to four aspects of computers. They learn that computers are programmable machines. They learn that computers are being used in all areas of society. They learn that computers make good electronic textbooks. And (something they already knew), they learn that computers are terrific game machines.

… According to the surveys, real educational results have been realized at schools which concentrate on exposing kids to computers. … Kids get to touch computers, play with them, push their buttons, order them about, and cope with computers’ incredible dumbness, their awful pickiness, their exasperating bugs, and their ridiculous quirks.

The main benefits D’Ignazio noted were ancillary. Students stayed at school longer, came in earlier, and stayed late. They were more attentive to their studies, and the computers fostered a sense of community, rather than competition and rivalry. If you read his article, you get a sense that there was almost a “worship” of computers on the part of educators. They didn’t understand what they were, or what they represented, but they were so interesting! There’s a problem there… When people are fascinated by something they don’t understand, they tend to impose meanings on it that are not backed by evidence, and so miss the point. The mistaken perceptions can be strengthened by anecdotal evidence (one of the weakest kinds). This is what happened to programming in schools.

The success of the strategy of using computers to try to improve higher-order thinking was illusory. John Maxwell’s telling of the “life and death of Logo” (my phrasing) serves as a useful analog to what happened to programming in schools generally. For those unfamiliar with it, the basic concept of Logo was a programming environment in which the student manipulates an object called a “turtle” via. commands. The student can ask the turtle to rotate and move. As it moves it drags a pen behind it, tracing its trail.  Other versions of this language were created that allowed more capabilities, allowing further exploration of the concepts for which it was created. The original idea Seymour Papert, who taught children using Logo, had was to teach young children about sophisticated math concepts, but our educational system imposed a very different definition and purpose on it. Just because something is created on a computer with the intent of it being used for a specific purpose doesn’t mean that others can’t use it for completely different, and possibly less valuable purposes. We’ve seen this a lot with computers over the years; people “misusing” them for both constructive and destructive ends.

As I go forward with this, I just want to put out a disclaimer that I don’t have answers to the problems I point out here. I point them out to make people aware of them, to get people to pause with the pursuit of putting people through this again, and to point to some people who are working on trying to find some answers. I present some of their learned opinions. I encourage interested readers to read up on what these people have had to say about the use of computers in education, and perhaps contact them with the idea of learning more about what they’ve found out.

I ask the reader to pay particular attention to the “benefits” that educators imposed on the idea of programming during this period that Maxwell talks about, via. what Papert called “technocentrism.” You hear this being echoed in the videos above. As you go through this, I also want you to notice that Papert, and another educator by the name of Alan Kay, who have thought a lot about what computers represent, have a very different idea about the importance of computers and programming than is typical in our school system, and in the computer industry.

The spark that started Logo’s rise in the educational establishment was the publication of Papert’s book, “Mindstorms: Children, Computers, and Powerful Ideas” in 1980. Through the process of Logo’s promotion…

Logo became in the marketplace (in the broad sense of the word) [a] particular black box: turtle geometry; the notion that computer programming encourages a particular kind of thinking; that programming in Logo somehow symbolizes “computer literacy.” These notions are all very dubious—Logo is capable of vastly more than turtle graphics; the “thinking skills” strategy was never part of Papert’s vocabulary; and to equate a particular activity like Logo programming with computer literacy is the equivalent of saying that (English) literacy can be reduced to reading newspaper articles—but these are the terms by which Logo became a mass phenomenon.

It was perhaps inevitable, as Papert himself notes (1987), that after such unrestrained enthusiasm, there would come a backlash. It was also perhaps inevitable given the weight that was put on it: Logo had come, within educational circles, to represent computer programming in the large, despite Papert’s frequent and eloquent statements about Logo’s role as an epistemological resource for thinking about mathematics. [my emphasis — Mark] In the spirit of the larger project of cultural history that I am attempting here, I want to keep the emphasis on what Logo represented to various constituencies, rather than appealing to a body of literature that reported how Logo “didn’t work as promised,” as many have done (e.g., Sloan 1985; Pea & Sheingold 1987). The latter, I believe, can only be evaluated in terms of this cultural history. Papert indeed found himself searching for higher ground, as he accused Logo’s growing numbers of critics of technocentrism:

“Egocentrism for Piaget does not mean ‘selfishness’—it means that the child has difficulty understanding anything independently of the self. Technocentrism refers to the tendency to give a similar centrality to a technical object—for example computers or Logo. This tendency shows up in questions like ‘What is THE effect of THE computer on cognitive development?’ or ‘Does Logo work?’ … such turns of phrase often betray a tendency to think of ‘computers’ and ‘Logo’ as agents that act directly on thinking and learning; they betray a tendency to reduce what are really the most important components of educational situations—people and cultures—to a secondary, faciltiating role. The context for human development is always a culture, never an isolated technology.”

But by 1990, the damage was done: Logo’s image became that of a has-been technology, and its black boxes closed: in a 1996 framing of the field of educational technology, Timothy Koschmann named “Logo-as-Latin” a past paradigm of educational computing. The blunt idea that “programming” was an activity which could lead to “higher order thinking skills” (or not, as it were) had obviated Papert’s rich and subtle vision of an ego-syntonic mathematics.

By the early 1990s … Logo—and with it, programming—had faded.

The message–or black box–resulting from the rise and fall of Logo seems to have been the notion that “programming” is over-rated and esoteric, more properly relegated to the ash-heap of ed-tech history, just as in the analogy with Latin. (pp. 183-185)

To be clear, the last part of the quote refers only to the educational value placed on programming by our school system. When educators attempted to formally study and evaluate programming’s benefits on higher-order thinking and the like, they found it wanting, and so most schools gradually dropped teaching programming in the 1990s.

Maxwell addresses the conundrum of computing and programming in schools, and I think what he says is important to consider as people try to “reboot” programming in education:

[The] critical faculties of the educational establishment, which we might at least hope to have some agency in the face of large-scale corporate movement, tend to actually disengage with the critical questions (e.g., what are we trying to do here?) and retreat to a reactionary ‘humanist’ stance in which a shallow Luddism becomes a point of pride. Enter the twin bogeymen of instrumentalism and technological determinism: the instrumentalist critique runs along the lines of “the technology must be in the service of the educational objectives and not the other way around.” The determinist critique, in turn, says, ‘the use of computers encourages a mechanistic way of thinking that is a danger to natural/human/traditional ways of life’ (for variations, see, Davy 1985; Sloan 1985; Oppenheimer 1997; Bowers 2000).

Missing from either version of this critique is any idea that digital information technology might present something worth actually engaging with. De Castell, Bryson & Jenson write:

“Like an endlessly rehearsed mantra, we hear that what is essential for the implementation and integration of technology in the classroom is that teachers should become ‘comfortable’ using it. […] We have a master code capable of utilizing in one platform what have for the entire history of our species thus far been irreducibly different kinds of things–writing and speech, images and sound–every conceivable form of information can now be combined with every other kind to create a different form of communication, and what we seek is comfort and familiarity?”

Surely the power of education is transformation. And yet, given a potentially transformative situation, we seek to constrain the process, managerially, structurally, pedagogically, and philosophically, so that no transformation is possible. To be sure, this makes marketing so much easier. And so we preserve the divide between ‘expert’ and ‘end-user;’ for the ‘end-user’ is profoundly she who is unchanged, uninitiated, unempowered.

A seemingly endless literature describes study after study, project after project, trying to identify what really ‘works’ or what the critical intercepts are or what the necessary combination of ingredients might be (support, training, mentoring, instructional design, and so on); what remains is at least as strong a body of literature which suggests that this is all a waste of time.

But what is really at issue is not implementation or training or support or any of the myriad factors arising in discussions of why computers in schools don’t amount to much. What is really wrong with computers in education is that for the most part, we lack any clear sense of what to do with them, or what they might be good for. This may seem like an extreme claim, given the amount of energy and time expended, but the record to date seems to support it. If all we had are empirical studies that report on success rates and student performance, we would all be compelled to throw the computers out the window and get on with other things.

But clearly, it would be inane to try to claim that computing technology–one of the most influential defining forces in Western culture of our day, and which shows no signs of slowing down–has no place in education. We are left with a dilemma that I am sure every intellectually honest researcher in the field has had to consider: we know this stuff is important, but we don’t really understand how. And so what shall we do, right now?

It is not that there haven’t been (numerous) answers to this question. But we have tended to leave them behind with each surge of forward momentum, each innovative push, each new educational technology “paradigm” as Timothy Koschmann put it. (pp. 18-19)

The answer is not a “reboot” of programming, but rather a rethinking of it. Maxwell makes a humble suggestion: that educators stop being blinded by “the shiny new thing,” or some so-called “new” idea such that they lose their ability to think clearly about what’s being done with regard to computers in education, and that they deal with history and historicism. He said that the technology field has had a problem with its own history, and this tends to bleed over into how educators regard it. The tendency is to forget the past, and to downplay it (“That was neat then, but it’s irrelevant now”).

In my experience, people have associated technology’s past with memories of using it. They’ve given little if any thought to what it represented. They take for granted what it enabled them to do, and do not consider what that meant. Maxwell said that this…

…makes it difficult, if not impossible, to make sense of the role of technology in education, in society, and in politics. We are faced with a tangle of hobbles–instrumentalism, ahistoricism, fear of transformation, Snow’s “two cultures,” and a consumerist subjectivity.

An examination of the history of educational technology–and educational computing in particular–reveals riches that have been quite forgotten. There is, for instance, far more richness and depth in Papert’s philosophy and his more than two decades of practical work on Logo than is commonly remembered. And Papert is not the only one. (p. 20)

Maxwell went into what Alan Kay thought about the subject. Kay has spent almost as many years as Papert working on a meaningful context for computing and programming within education. Some of the quotes Maxwell uses are from “The Early History of Smalltalk,” (h/t Bill Kerr) which I’ll also refer to. The other sources for Kay’s quotes are included in Maxwell’s bibliography:

What is Literacy?

“The music is not in the piano.” — Alan Kay

The past three or four decades are littered with attempts to define “computer literacy” or something like it. I think that, in the best cases, at least, most of these have been attempts to establish some sort of conceptual clarity on what is good and worthwhile about computing. But none of them have won large numbers of supporters across the board.

Kay’s appeal to the historical evolution of what literacy has meant over the past few hundred years is, I think, a much more fruitful framing. His argument is thus not for computer literacy per se, but for systems literacy, of which computing is a key part.

That this is a massive undertaking is clear … and the size of the challenge is not lost on Kay. Reflecting on the difficulties they faced in trying to teach programming to children at PARC in the 1970s, he wrote that:

“The connection to literacy was painfully clear. It is not just enough to learn to read and write. There is also a literature that renders ideas. Language is used to read and write about them, but at some point the organization of ideas starts to dominate the mere language abilities. And it helps greatly to have some powerful ideas under one’s belt to better acquire more powerful ideas.”

Because literature is about ideas, Kay connects the notion of literacy firmly to literature:

“What is literature about? Literature is a conversation in writing about important ideas. That’s why Euclid’s Elements and Newton’s Principia Mathematica are as much a part of the Western world’s tradition of great books as Plato’s Dialogues. But somehow we’ve come to think of science and mathematics as being apart from literature.”

There are echoes here of Papert’s lament about mathophobia, not fear of math, but the fear of learning that underlies C.P. Snow’s “two cultures,” and which surely underlies our society’s love-hate relationship with computing. Kay’s warning that too few of us are truly fluent with the ways of thinking that have shaped the modern world finds an anchor here. How is it that Euclid and Newton, to take Kay’s favourite examples, are not part of the canon, unless one’s very particular scholarly path leads there? We might argue that we all inherit Euclid’s and Newton’s ideas, but in distilled form. But this misses something important … Kay makes this point with respect to Papert’s experiences with Logo in classrooms:

“Despite many compelling presentations and demonstrations of Logo, elementary school teachers had little or no idea what calculus was or how to go about teaching real mathematics to children in a way that illuminates how we think about mathematics and how mathematics relates to the real world.” (Maxwell, pp. 135-137)

Just a note of clarification: I refer back to what Maxwell said re. Logo and mathematics. Papert did not use his language to teach programming as an end in itself. His goal was to use a computer to teach mathematics to children. Programming with Logo was the means for doing it. This is an important concept to keep in mind as one considers what role computer programming plays in education.

The problem, in Kay’s portrayal, isn’t “computer literacy,” it’s a larger one of familiarity and fluency with the deeper intellectual content; not just that which is specific to math and science curriculum. Kay’s diagnosis runs very close to Neil Postman’s critiques of television and mass media … that we as a society have become incapable of dealing with complex issues.

“Being able to read a warning on a pill bottle or write about a summer vacation is not literacy and our society should not treat it so. Literacy, for example, is being able to fluently read and follow the 50-page argument in [Thomas] Paine’s Common Sense and being able (and happy) to fluently write a critique or defense of it.” (Maxwell, p. 137)

Extending this quote (from “The Early History of Smalltalk”), Kay went on to say:

Another kind of 20th century literacy is being able to hear about a new fatal contagious incurable disease and instantly know that a disastrous exponential relationship holds and early action is of the highest priority. Another kind of literacy would take citizens to their personal computers where they can fluently and without pain build a systems simulation of the disease to use as a comparison against further information.

At the liberal arts level we would expect that connections between each of the fluencies would form truly powerful metaphors for considering ideas in the light of others.

Continuing with Maxwell (and Kay):

“Many adults, especially politicians, have no sense of exponential progressions such as population growth, epidemics like AIDS, or even compound interest on their credit cards. In contrast, a 12-year-old child in a few lines of Logo […] can easily describe and graphically simulate the interaction of any number of bodies, or create and experience first-hand the swift exponential progressions of an epidemic. Speculations about weighty matters that would ordinarily be consigned to common sense (the worst of all reasoning methods), can now be tried out with a modest amount of effort.”

Surely this is far-fetched; but why does this seem so beyond our reach? Is this not precisely the point of traditional science education? We have enough trouble coping with arguments presented in print, let alone simulations and modeling. Postman’s argument implicates television, but television is not a techno-deterministic anomaly within an otherwise sensible cultural milieu; rather it is a manifestation of a larger pattern. What is wrong here has as much to do with our relationship with print and other media as it does with television. Kay noted that “In America, printing has failed as a carrier of important ideas for most Americans.” To think of computers and new media as extensions of print media is a dangerous intellectual move to make; books, for all their obvious virtues (stability, economy, simplicity) make a real difference in the lives of only a small number of individuals, even in the Western world. Kay put it eloquently thus: “The computer really is the next great thing after the book. But as was also true with the book, most [people] are being left behind.” This is a sobering thought for those who advocate public access to digital resources and lament a “digital divide” along traditional socioeconomic lines. Kay notes,

“As my wife once remarked to Vice President Al Gore, the ‘haves and have-nots’ of the future will not be caused so much by being connected or not to the Internet, since most important content is already available in public libraries, free and open to all. The real haves and have-nots are those who have or have not acquired the discernment to search for and make use of high content wherever it may be found.” (Maxwell, pp. 138-139)

I’m still trying to understand myself what exactly Alan Kay means by “literature” in the realm of computing. He said that it is a means for discussing important ideas, but in the context of computing, what ideas? I suspect from what’s been said here he’s talking about what I’d call “model content,” thought forms, such as the idea of an exponential progression, or the concept of velocity and acceleration, which have been fashioned in science and mathematics to describe ideas and phenomena. “Literature,” as he defined it, is a means of discussing these thought forms–important ideas–in some meaningful context.

In prior years he had worked on that in his Squeak environment, working with some educators. They would show children a car moving across the screen, dropping dots as it went, illustrating velocity, and then, modifying the model, acceleration. Then they would show them Galileo’s experiment, dropping heavy and light balls from the roof of a building (real balls from a real building), recording the ball dropping, and allowing the children to view the video of the ball, and simultaneously model it via. programming, and discovering that the same principle of acceleration applied there as well. Thus, they could see in a couple contexts how the principle worked, how they could recognize it, and see its relationship to the real world. The idea being that they could grasp the concepts that make up the idea of acceleration, and then integrate it into their thinking about other important matters they would encounter in the future.

Maxwell quoted from an author named Andrew diSessa to get deeper into the concept of literacy, specifically what literacy in a type of media offers our understanding of issues:

The hidden metaphor behind transparency–that seeing is understanding–is at loggerheads with literacy. It is the opposite of how media make us smarter. Media don’t present an unadulterated “picture” of the problem we want to solve, but have their fundamental advantage in providing a different representation, with different emphases and different operational possibilities than “seeing and directly manipulating.”

What’s a good goal for computing?

The temptation in teaching and learning programming is to get students familiar enough with the concepts and a language that they can start creating things with it. But create what? The typical cases are to allow students to tinker, and/or to create applications which gradually become more complex and feature-rich, with the idea of building confidence and competence with increasing complexity. The latter is not a bad idea in itself, but listening to Alan Kay has led me to believe that starting off with this is the equivalent of jumping to a conclusion too quickly, and to miss the point of what’s powerful about computers and programming.

I like what Kay said in “The Early History of Smalltalk” about this:

A twentieth century problem is that technology has become too “easy.” When it was hard to do anything whether good or bad, enough time was taken so that the result was usually good. Now we can make things almost trivially, especially in software, but most of the designs are trivial as well. This is inverse vandalism: the making of things because you can. Couple this to even less sophisticated buyers and you have generated an exploitation marketplace similar to that set up for teenagers. A counter to this is to generate enormous dissatisfaction with one’s designs using the entire history of human art as a standard and goal. Then the trick is to decouple the dissatisfaction from self worth–otherwise it is either too depressing or one stops too soon with trivial results.

Edit 4-5-2013: I thought I should point out that this quote has some nuance to it that people might miss. I don’t believe Kay is saying that “programming should be hard.” Quite the contrary. One can observe from his designs that he’s advocated the opposite. Not that technology should mold itself to what is “natural” for humans. It might require some training and practice, but once mastered, it should magnify or enhance human capabilities, thereby making previously difficult or tedious tasks easier to accomplish and incorporate into a larger goal.

Kay was making an observation about the history of technology’s relationship to society, that the effect on people of useful technology being hard to build has generally caused the people who created something useful to make it well. What he’s pointing out is that people generally take the presence of technology as an excuse to use it as a crutch, in this case to make immediate use of it towards some other goal that has little to do with what the technology represents, rather than an invitation to revisit it, criticize its design, and try to make it better. This is an easy sell, because everyone likes something that makes their lives easier (or seems to), but we rob ourselves of something important in the process if that becomes the only end goal. What I see him proposing is that people with some skill should impose a high standard for design on themselves, drawing inspiration for that standard from how the best art humanity has produced was developed and nurtured, but guard against the sense of feeling small, inadequate, and overwhelmed by the challenge.

Maxwell (and Kay) explain further why this idea of “literacy” as being able to understand and communicate important ideas, which includes ideas about complexity, is something worth pursuing:

“If we look back over the last 400 years to ponder what ideas have caused the greatest changes in human society and have ushered in our modern era of democracy, science, technology and health care, it may come as a bit of a shock to realize that none of these is in story form! Newton’s treatise on the laws of motion, the force of gravity, and the behavior of the planets is set up as a sequence of arguments that imitate Euclid’s books on geometry.”

The most important ideas in modern Western culture in the past few hundred years, Kay claims, are the ones driven by argumentation, by chains of logical assertions that have not been and cannot be straightforwardly represented in narrative. …

But more recent still are forms of argumentation that defy linear representation at all: ‘complex’ systems, dynamic models, ecological relationships of interacting parts. These can be hinted at with logical or mathematical representations, but in order to flesh them out effectively, they need to be dynamically modeled. This kind of modeling is in many cases only possible once we have computational systems at our disposal, and in fact with the advent of computational media, complex systems modeling has been an area of growing research, precisely because it allows for the representation (and thus conception) of knowledge beyond what was previously possible. In her discussion of the “regime of computation” inherent in the work of thinkers like Stephen Wolfram, Edward Fredkin, and Harold Morowitz, N. Katherine Hayles explains:

“Whatever their limitations, these researchers fully understand that linear causal explanations are limited in scope and that multicausal complex systems require other modes of modeling and explanation. This seems to me a seminal insight that, despite three decades of work in chaos theory, complex systems, and simulation modeling, remains underappreciated and undertheorized in the physical sciences, and even more so in the social sciences and humanities.”

Kay’s lament too is that though these non-narrative forms of communication and understanding–both in the linear and complex varieties–are key to our modern world, a tiny fraction of people in Western society are actually fluent in them.

“In order to be completely enfranchised in the 21st century, it will be very important for children to become fluent in all three of the central forms of thinking that are now in use. […] the question is: How can we get children to explore ways of thinking beyond the one they’re ‘wired for’ (storytelling) and venture out into intellectual territory that needs to be discovered anew by every thinking person: logic and systems ‘eco-logic?'” …

In this we get Kay’s argument for ‘what computers are good for’ … It does not contradict Papert’s vision of children’s access to mathematical thinking; rather, it generalizes the principle, by applying Kay’s vision of the computer as medium, and even metamedium, capable of “simulating the details of any descriptive model.” The computer was already revolutionizing how science is done, but not general ways of thinking. Kay saw this as the promise of personal computing, with millions of users and millions of machines.

“The thing that jumped into my head was that simulation would be the basis for this new argument. […] If you’re going to talk about something really complex, a simulation is a more effective way of making your claim than, say, just a mathematical equation. If, for example, you’re talking about an epidemic, you can make claims in an essay, and you can put mathematical equations in there. Still, it is really difficult for your reader to understand what you’re actually talking about and to work out the ramifications. But it is very different if you can supply a model of your claim in the form of a working simulation, something that can be examined, and also can be changed.”

The computer is thus to be seen as a modeling tool. The models might be relatively mundane–our familiar word processors and painting programs define one end of the scale–or they might be considerably more complex. [my emphasis — Mark] It is important to keep in mind that this conception of computing is in the first instance personal–“personal dynamic media”–so that the ideal isn’t simulation and modeling on some institutional or centralized basis, but rather the kind of thing that individuals would engage in, in the same way in which individuals read and write for their own edification and practical reasons. This is what defines Kay’s vision of a literacy that encompasses logic and systems thinking as well as narrative.

And, as with Papert’s enactive mathematics, this vision seeks to make the understanding of complex systems something to which young children could realistically aspire, or that school curricula could incorporate. Note how different this is from having a ‘computer-science’ or an ‘information technology’ curriculum; what Kay is describing is more like a systems-science curriculum that happens to use computers as core tools:

“So, I think giving children a way of attacking complexity, even though for them complexity may be having a hundred simultaneously executing objects–which I think is enough complexity for anybody–gets them into that space in thinking about things that I think is more interesting than just simple input/output mechanisms.” (Maxwell, pp. 132-135)

I wanted to highlight the part about “word processors” and “paint programs,” because this idea that’s being discussed is not limited to simulating real world phenomena. It could be incorporated into simulating “artificial phenomena” as well. It’s a different way of looking at what you are doing and creating when you are programming. It takes it away from asking, “How do I get this thing to do what I want,” and redirects it to, “What entities do we want to make up this desired system, what are they like, and how can they interact to create something that we can recognize, or otherwise leverages human capabilities?”

Maxwell said that computer science is not the important thing. Rather, what’s important about computer science is what it makes possible: “the study and engagement with complex or dynamic systems–and it is this latter issue which is of key importance to education.” Think about this in relation to what we do with reading and writing. We don’t learn to read and write just to be able to write characters in some sequence, and then for others to read what we’ve written. We have events and ideas, perhaps more esoteric to this subject, emotions and poetry, that we write about. That’s why we learn to read and write. It’s the same thing with computer science. It’s pretty worthless, if we as a society value it for communicating ideas, if it’s just about learning to read and write code. To make the practice something that’s truly valuable to society, we need to have content, ideas, to read and write about in code. There’s a lot that can be explored with that idea in mind.

Characterizing Alan Kay’s vision for personal computing, Maxwell talked about Kay’s concept of the Dynabook:

Alan Kay’s key insight in the late 1960s was that computing would become the practice of millions of people, and that they would engage with computing to perform myriad tasks; the role of software would be to provide a flexible medium with which people could approach those myriad tasks. … [The] Dynabook’s user is an engaged participant rather than a passive, spectatorial consumer—the Dynabook’s user was supposed to be the creator of her own tools, a smarter, more capable user than the market discourse of the personal computing industry seems capable of inscribing—or at least has so far, ever since the construction of the “end-user” as documented by Bardini & Horvath. (p. 218)

Kay’s contribution begins with the observation that digital computers provide the means for yet another, newer mode of expression: the simulation and modeling of complex systems. What discursive possibilities does this new modality open up, and for whom? Kay argues that this latter communications revolution should in the first place be in the hands of children. What we are left with is a sketch of a possible new literacy; not “computer literacy” as an alternative to book literacy, but systems literacy—the realm of powerful ideas in a world in which complex systems modelling is possible and indeed commonplace, even among children. Kay’s fundamental and sustained admonition is that this literacy is the task and responsibility of education in the 21st century. The Dynabook vision presents a particular conception of what such a literacy would look like—in a liberal, individualist, decentralized, and democratic key. (p. 262)

I would encourage interested readers to read Maxwell’s paper in full. He gives a rich description of the problem of computers in the educational context, giving a much more detailed history of it than I have here, and what the best minds on the subject have tried to do to improve the situation.

The main point I want to get across is if we as a society really want to get the greatest impact out of what computers can do for us, beyond just being tools that do canned, but useful things, I implore educators to see computers and programming environments more as apparatus, instruments, media (the computers and programming environments themselves, not what’s “played” on computers, and languages and metaphors, which are the media’s means of expression, not just a means to some non-expressive end), rather than as agents and tools. Sure, there will be room for them to function as agents and tools, but the main focus that I see as important in this subject area is in how the machine helps facilitate substantial pedagogies and illuminates epistemological concepts that would otherwise be difficult or impossible to communicate.

—Mark Miller, https://tekkie.wordpress.com

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Bret Victor, a former designer at Apple, is working on a way to use a computer to make math more meaningful. I can see that he really gets the representational aspect, that the symbols are not the math, just a way to represent it, and it’s not a particularly good way to represent it. This is not the whole of math encapsulated into something that’s easy to understand (math is about assertions and inferences of relationships, which are then proved or disproved), but it’s an alternative to using symbols for representing complex relationships.

Here’s an article talking about Bret’s work on an early version of something he’s working on for the iPad.

Great stuff, and I congratulate him on finding a good use for a computer!

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This is from Bryan Magee’s 15-part series The Great Philosophers, broadcast on BBC2 in 1987. Here he talks with Hilary Putnam about the philosophy of science.

Alan Kay has talked about how most people don’t understand how science worked in the 20th century, much less the present century. Our schools still teach science in a 19th century fashion in terms of how people can approach knowledge. This episode of the show amply demonstrates this disconnect.

As I studied what was said here about scientific thinking up until the late 19th century, it reminded me a lot of how I was taught computer science. It was thought to be a process of adding on to existing knowledge, and sorting out any inconsistencies. With rare exceptions, alternatives to existing models were not discussed, or even made known to students.

I’m going to put the notes I took from this episode below each section of video, because I found it hard to follow the arguments without going through them slowly, and looking at what Magee and Putnam said explicitly.

Magee: The religious world view has been largely replaced by a world view purportedly derived from science, since the 17th century.

Science was seen as a process of “adding” and sorting for 300 years, up until the late 19th century. The view of knowledge was that it grew by accumulation. Science was seen as getting its success from an inductive method. For 300 years educated people thought of the Universe as just matter in motion. They thought if we went on long enough, we’d find out everything about the Universe there was to know. This whole idea has been abandoned by science, but non-scientists think that scientists still think this way.

Kant challenged the “correspondence” view of truth, that theories correspond directly to reality without nuances, that the world makes its truths apparent, and scientists just find out what that is and write it down. Kant said there’s a contribution of the thinking mind. Truth depends on what exists, and on the mind of the observer. Scientists have come to a similar view, that theories are not merely dictated to us by the “facts.”

The categories and interpretations we use, the ideas within which we organize our observations, are our contributions. The world that’s perceived by us is partly contributed to by external effects, and is partly made up of categories and ways of seeing things that come from us. [Mark says: I think Plato’s notion of “forms” also applies here, in terms of our theories. We could equate a theory of a phenomenon to its form in our own minds, and we could recognize that if a phenomenon is unfamiliar enough, different people will create different forms of it in their own minds.]

In the late 19th century the views of scientists changed to realize that the old theories could be wrong. The more modern view is that not only is our view of reality partly mind-dependent, but there are alternatives, and the concepts we impose on the world may not be the right ones, we may have to change them, and there is an interaction between what we contribute and what we find out. It was realized that there were alternative descriptions of some things that were equally as valid.

The new conception of science is that theories are constantly being replaced by newer and better ones, which are richer, that explain phenomena more fully, and there’s a mystery to our universe which will never be completely discovered. The view about “truth” that Putnam said was coming into use more and more in science is the idea that there cannot be a separation between what’s considered true and what our standards of assertability are. So the way that mind-dependence comes in is the fact that what’s true and what’s false is partly a function of what our standards of truth and falsity are. That depends on our interests, which change over time. Putnam defined “interests” in a cultural context, such as, in our modern world we’d recognize that there’s a policeman on the corner. Someone from a tribal culture, which may have no formal social services, wouldn’t recognize a policeman, but would instead see “someone in blue” on the corner.

Even facts within theories can have alternatives, as was evidenced by the theory of relativity.

A scientific theory can be useful even if nobody really understands what it means (quantum mechanics). [Mark says: You could also say the same about Newton’s theory of gravity.]

Putnam thinks the way in which the old scientific view (pre-19th century) was destructive was that since it saw scientific findings as objective facts which were accumulated over time, then everything else was considered non-knowledge, that couldn’t even be considered true or false.

Real interesting! Putnam and Magee talk about how computer science, through an interaction with computer simulations, has created a growth of knowledge about the human mind. This relates to what I’ve read about Licklider’s use of computers at MIT in the 1950s, in Waldrop’s book, “The Dream Machine.”

Edit 5-4-2011: I had a brief conversation with Alan Kay about the main theme of this program, and I put particular focus on this part, where Magee and Putnam discussed the role that computer science has played in the advancement of knowledge in other areas of research. He zeroed in on this part, saying that it was a misperception of what really happened, at least in relation to Licklider (and Engelbart). He said the advances in knowledge from computer science have been paradigm shifts, not advancements by interaction. He pointed out that the theory of evolution did not begin in the field of biology, as Magee asserts. He also said that computers did not begin as “a self-conscious analogy to the human mind,” but rather as machines to run calculations. I knew that. I thought Magee’s description of this was rather romantic, and other historical accounts have agreed with his assessment, so I let it slide, but I have since had second thoughts about that.

Magee: Most people with college degrees have no idea what Einstein’s theory of relativity is all about, more than 70 years after it was published. It’s done very little to influence their view of the world. Isn’t there a danger that science and mathematics are racing ahead, and the whole range of insight that that’s giving us into the Universe simply isn’t filtering through to the layman?

Putnam: There was a text on Special Relativity called “Space-Time Physics” that was designed for the first month of the first college physics course, and the authors hoped that someday it would be taught in high schools.

The question and answer above really struck a chord with me. First of all, I didn’t encounter Einstein’s theory of relativity in my first semester physics course in college. All we covered, that I remember, was Newtonian mechanics, though at a more detailed and advanced level than what we got in high school.

The discussion that Magee and Putnam had about General Relativity, and the risk we take with science “racing past” the rest of society, I think, makes a good case for Alan Kay’s efforts to teach more advanced math concepts to children, because without that, they’re never going to understand it. To put this in perspective, take a look at General Relativity, and think about the understanding of mathematics that would be required to understand it.

Strangely enough, I got more exposure to Einstein’s theories of relativity when I was in Jr. high school (1982-’85) than I got at any other time. We watched parts of Carl Sagan’s Cosmos series. In it Sagan talked about Einstein’s theories of relativity, and included Einstein’s notion of gravity as warped space-time. Farther back than that, I had been fascinated by the idea of black holes, since I was in 4th grade. I read all I could on them, and I learned some very strange things: that not even light could escape from them, and that matter was destroyed upon entering them, for all intents and purposes, though I think the evidence still supports the idea of conservation of matter. It’s just that the matter gets separated into its component parts, and some of the matter is converted into energy.

I got exposed a bit to a theory of gravity that was different from Einstein’s or Newton’s outside of school, by a toy inventor, of all people. So I knew there were other theories besides those that were commonly accepted. What became clear to me after exposure to these ideas was that gravity is a fascinating mystery. We didn’t know what caused it then, and we don’t know now, not really, except for mass. The question that would not go away for me was what about mass causes it? The idea that mass causes it just because it exists never satisfied me. The other forces were explained through phenomena I could relate to, but gravity was different.

(Update 12-15-2013: I’ve updated the paragraph below with what I think is a more accurate description of the theory, and I’ve added a couple paragraphs to further explain. I was mistaken in saying that astrophysicists think that the distortion in space-time causes us to “slide” in towards the gravity well.)

What Einstein’s theory explains is that gravity is not a force in the way that we think about other forces. His theory said that matter warps space-time, and it is this warping which creates the perception of a force acting on objects and energy. The way Sagan explained it is that we are all “sliding” being pushed inward towards the center of the gravity well, on by this distortion, and what keeps us from going to the center of the well is the outward force exerted by the earth we stand on. Scientists have tried to explain it using an analogy of a large ball setting on a piece of taut fabric, which creates a dip in it. If you toss a marble onto the fabric, it will fall towards the larger ball, due to the anomaly in the fabric. This is not a great analogy, because in it, gravity is pulling the larger ball down creating the dip in the fabric. If you can disregard that fact, what they are trying to get across is its the distortion of the fabric that alters the path of the marble, not any force. Another way this analogy is imperfect is it doesn’t illustrate how space-time is being drawn inward by something that is not yet explained. What Einstein’s theory says is that there is something about matter that causes this distortion in space-time. That could be attributed to a force, but from everything I’ve studied about the theory so far, Einstein didn’t say that. That’s left as “a problem for the reader,” so to speak.

The strange thing about the theory that might seem confusing is it explains that while we are “pushed” inward towards the well, it’s not a push in the Newtonian sense. This “push” is coming from what is called “space-time” itself. It’s a push on everything that constitutes matter and energy, down to our body’s subatomic particles, and photons of light.

Looking at how it is we stand on earth, rather than all matter being drawn into the well, it’s rather like standing on stretchy material that’s constantly being drawn into something, with a “wall” in our way (which does not “catch” the fabric). The closer the material gets to whatever is pulling on it, the more stretched it becomes. As it becomes stretched, so are we, and all the matter around us, because, as we understand, matter is “situated” in space-time. There may be some “friction,” if you will, between us and the material (space-time), that causes us to be drawn in with it, but it’s not total, allowing this “material” to slide past us, while we are repelled outward by the “wall” (forces in the matter we stand on). Hopefully my attempt to explain this isn’t too confusing. I’m groping at understanding it myself.

An example of the way school gets science wrong

High school physics was odd to me, because we talked about stuff like how electrons had both the property of a particle and a wave (as Putnam discussed above), a very interesting idea, but when it came to gravity, all we focused on was Galileo’s and Newton’s notions of it, particularly Newton’s Universal Law of Gravitation. One of the things I remember being emphasized was that “this law is the same throughout the Universe.” For the sake of argument, from our perspective, being on Earth, I was willing to accept that claim, but I remember being a bit skeptical that it was really true. I knew that we hadn’t really explored the whole universe, and that we hadn’t even come close to testing this notion everywhere. I was open to the idea that someday we might find an exception to this notion if we were to theoretically explore the Universe, which is something that may never happen (I didn’t even know about Mercury’s orbit, which doesn’t fit Newton’s theory as well as the orbits of the other planets). So, for practical purposes, we could assume that it’s “universal.”

We talked about Einstein’s theory of Special Relativity, and what was really fascinating about that was E = mc2, that there’s a relationship between matter and energy that’s only “separated” by the square of the speed of light. My memory, though, is we hardly talked about General Relativity at all, except perhaps in a historical context. Looking back on this, it’s rather obvious to see why. In high school science we were expected to get a little more into the details of scientific theories, and work with the math concepts more. The school system hadn’t prepared us to work with the notion of General Relativity at that level. So in effect we skipped it, but the conceit that was presented in class was that Newton’s law of gravity was “the truth.”

I remember being asked a question on a physics test that asked, “If aliens visited Earth, would we find that they have the same knowledge of gravity as we do?” I paused. This was an interesting question to me, because I thought it was asking me to consider what understanding another race of intelligent beings would have about this phenomenon. I asked myself, if space aliens existed that were intelligent enough to build craft for interstellar travel, would they have the same ideas about gravity as we do? I answered, “Maybe.” I added something about how since the aliens had managed to make the journey from whatever star system they came from, that their technology was probably more advanced than ours (I mean, we haven’t tried this yet, so that was a good guess), and maybe they had a better understanding of gravity than we did, particularly what caused it. I hedged a bit, but I guessed that there was probably a link between technological development and greater scientific understanding of our universe. Granted, this was a totally speculative answer, but it was a speculative question, as far as I was concerned.

My physics teacher marked this answer wrong. I was floored! I wondered, “What did she expect?” I asked her about it after class, and she said the answer she expected was something along the lines of, “Yes, because the Law of Gravity is universal.” I was so disappointed (in her). It immediately hit me that, “Oh, yeah. I remember we talked about that.” I could’ve almost kicked myself for thinking that she had asked a thought-provoking question, and falling for it! I was supposed to remember to recall what we had talked about in class. I wasn’t supposed to think on it! Duh! How could I have been so stupid? That’s really how perverse and offensive this was. It brings to mind the fictional short story of “Harrison Bergeron,” now that I think about it… However, trying not to see that she was telling me not to think, I tried to talk her through my reasoning, because I thought I gave a legitimate answer. I told her about the other notions of gravity I knew about, and the questions they raised for me. She wouldn’t hear of it. I think I said in a final protest, “Do you really think we’ve discovered everything there is to know about gravity?!” In any case, she didn’t answer me. I walked out of the classroom exasperated. It was one of the most disillusioning experiences of my life. It made me fume!

Looking back on things like this, she probably didn’t even understand what a good question she had asked. Secondly, there were other instances where this happened in my schooling. Sometimes I wanted to think through things and come up with original answers, not merely regurgitate what I had been fed, and I got penalized for it. She and I had not been getting along for most of the time while I was in her class, and I think it was over issues like this. So this was nothing new, but this incident revealed a disturbing fact to me in a way that was so obvious, I couldn’t just brush it off as a misunderstanding between us: Her approach to science was that we were supposed to accept what she said as truth. We were not supposed to think about it, or question it. The only thinking we were supposed to do was in calculating results from experiments, but a lot of that was applying the “correct” formulas. More memorization. Nevertheless, I got an “A” in her class.

Looking at this from a “mountaintop” view, I think this example shows the split between 20th century scientific thinking, and the 19th century thinking that’s been used to teach science in schools. I saw a discussion recently where Alan Kay talked about this with the No Child Left Behind policy, that for students who were developing an understanding of scientific thinking, they had to, on the one hand, gain real understanding, and on the other, remember to answer “wrongly” on the test. That summed up the experience I describe above! I’ve used my example sometimes when I’ve heard people complain about this, because I can say to them I got the same treatment when I was in my high school science classes, more than 20 years ago. As far as I’m concerned, this policy is just taking that idea of instruction, which has been around for years, to its logical conclusion. It’s now metastasized throughout the public education system, at least in the areas that are tested for proficiency, whereas in my day there were exceptions.

—Mark Miller, https://tekkie.wordpress.com

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The title of this post is from a verbal gaffe that Dan Quayle committed when he gave a speech at the United Negro College Fund (now called “UNCF,” their slogan being, “A mind is a terrible thing to waste”) when he was Vice-President. I use it as a symbolic way of introducing this subject.

I came upon the following videos on YouTube. It is a dramatization of Ayn Rand’s thoughtful rant (nay, “indictment” is more like it) of our society’s promotion and acceptance of irrationality, through her character named John Galt, in her novel, “Atlas Shrugged.” It’s called “This is John Galt speaking…,” performed by Christopher Hurt, with video added by Richard Gleaves.

I am not wholeheartedly endorsing Rand’s Objectivist philosophy, but I agree strongly with her criticism of our society in the broadest sense. At times I have felt like screaming some of these criticisms, because I have seen the ignorance described, which seems impermeable, and I understand some things about the destructiveness it can produce. Screaming about it does little good, though. I am reminded of what Adlai Stevenson said of Eleanor Roosevelt, that she’d rather light a candle than curse the darkness.

John Galt’s speech is provocative, but it is provocation with a purpose, to get people to think about what has produced our modern world, and its problems, to think about the causes, not just the effects, and to perish the thought that it all comes about by magic, or should be taken for granted. That’s always valuable, to get a reality check. The reason I feature this rant is not to sway people towards a particular point of view, but to say that even though in our private lives we may find it valuable to hold beliefs in the supernatural, whether they be based in religious or secular views, they have real consequences in the health of our society when they are brought into the realm of politics, because they influence policy in unhealthy directions.

I am not putting all parts of Galt’s monologue here (the original dramatization has 18 parts), but certain key parts that I found thought-provoking, and valuable to share. I have long been interested in what creates and sustains modern civilization, and I think the Objectivist philosophy, as portrayed here, is an important piece of that, but I found it too limiting to be all-encompassing. In my encounters with philosophy, I’ve always found that materialism of any sort is too limiting as a singular governing principle for society. I would classify Objectivism as a “libertarian materialism.” I see it as just something to think about and consider.

Rand goes after all purveyors of irrationality in her time, but she seems to reserve particular scorn for mystics of all stripes, and catholicism. I find her criticism valuable from an anthropological perspective. If you take out the labels of different political systems and religions, and just look at their characteristics, it’s easier to see why those characteristics are probably destructive, as opposed to thinking that a particular instance of those characteristics, with a label, is destructive. That’s missing the forest for the trees.

Richard Gleaves used his own imagery and audio to illustrate what Galt was talking about. I do not agree with all of the imagery used, particularly regarding religion. It gives one the sense that all religion is like what is portrayed. I can say from experience that it’s not. Not all sects demand thoughtless obedience and sacrifice, though some popular forms of religion do promote this, and I agree with the specific criticism against that.

Rand seems to attack most forms of authority, a view I don’t agree with. I would just promote the idea of skepticism of authority.

The premise of this monologue is the society in Rand’s fictional tale has collapsed, and a character named John Galt, whom people in the story have wondered about, reveals himself to the world, telling everyone why society has collapsed, and how to bring it back to life.

What’s amazing to note is that Rand wrote all this in 1957, and that the concepts she talked about apply much more today than they did then. Though it was fictional, she wrote the story as an allegory, a warning to America. She said she saw troubling trends when she wrote it that she predicted would grow in impact on this country as time passed. I think she was right to see it that way.

Edit 11-28-2013: Gleaves deleted the videos I had been using here, and created a new series on the same monologue. So I’ve updated the videos I’ve used here with his new set of videos.

Part 1: This is John Galt Speaking

Part 4: The Standard of Morality

Part 5: Free Will

Part 5 is my favorite out of the whole series. Gleaves uses clips from the movie, “The Miracle Worker.” The way this was put together is poetic. As I watched it, I reflected on myself. At times I feel like Helen Keller’s teacher, trying to reach others. At other times I feel like Helen herself, going for long stretches feeling lost, mystified, and babbling about nothing of much value. Then I have experiences that feel like her at the water pump. The connection is made, and POW! Realization! The joy I feel afterward is like her running around, seeing a little better, taking it all in with a voracious hunger. Wonderful.

Part 7: Emotions

Part 13: Death Worship

Part 14: Utopia and Objectivity

Part 15: The Mystics of Muscle

Part 16: Nihilism

Part 17: Who is John Galt

Part 18: Necessary Evil & Paradise Lost

In a way, this post is a follow-up to an interview with Judy Shelton I featured on here about a year ago. She expressed concern that with the bent the U.S. government has now, that business owners, the people who create wealth, will eventually go on strike, or “go Galt,” because society no longer appreciates the personal risks they take to create products, services, and jobs.

What I really like about this is it doesn’t just complain about society, but illustrates the difference between a non-thinking society and a thinking society, and that this difference matters a great deal. The hope is that people will “wake up” and realize that this “dream” of certainty they’ve been in is not all its cracked up to be. While there will always be things we don’t know, there’s a lot less that’s “unknowable” than people think, and it would behoove us to find out as much about what’s really going on as possible, because it DOES affect us.

Like I said, this philosophy is not all-inclusive in terms of the important things that make up a functioning modern society. One thing it neglects is the fact that “intellectual life” is not just in the private sector. It’s also in our universities, at least in some holdouts. There is a healthy element of competition in this system, but in a well functioning system of this sort, the goal should not just be profit. An unfortunate fact I’ve been reading about is that universities are increasingly seeing profit as a primary goal. This narrows the focus of academic study significantly, and not always to good ends. It’s not just happening here. It’s happening in the UK as well.

So while I think Objectivism provides a valuable message to consider, I think it’s good to keep in mind that it is a vantage point from which one can be jarred, and see reality a little better, but that there are other valuable intellectual perspectives to explore and keep in mind as well.

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