How to Invent the Future I - CS183F

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We have Alan Kay with us this week, he's going to do both lectures.
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Alan Kay has forgotten more about how to invent the future than progressives
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>> That would make a very bad class, Sam,
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[LAUGH] >> [LAUGH] I think Alan is the genuine
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world expert on this.
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Invented with others, the Xerox Alto, which we recently restored at
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Y Combinator with a group of incredible computer scientists.
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And I think it's the person who has been most thoughtful, probably with anyone I've
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ver met, about how you build organizations to do real innovations.
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So thank you very much for coming, I am super excited to hear this.
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So, I'm thoughtful about it because I got to watch masters do it.
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I'm basically a research scientist, but
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I was interested in the process done by far better managers than me,
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who could deal with larger sets of ideas.
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And I'll try and give you a gist here.
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Basically, I think,
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people are here because they want to do startups and make money.
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I just want to point out that if you want to make money,
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don't bother with a startup.
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Create an industry, because then you get trillions instead of billions.
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So it's about a factor of a thousand between doing invention over innovation.
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In other words, not going incrementally from the present but
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carving out a whole new set of ideas that creates an entirely new context.
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And we'll see some of the ways to go about it and
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we'll also see some of the barriers.
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And see if this, no.
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This got a bad user interface,
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because it has the back button near to the front button.
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See if that works, so.
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A lot of what these two days are about are talking
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about the place that most people naturally live, which is the present.
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But the problem with the present, it is so glittery and distracting, there's so
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That it's hard to think about anything except the present.
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And if you're thinking about the present,
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your ideas are going to be derived from the present.
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And therefore, you wind up doing innovation,
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not that you can't make money that way, but.
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Today, I'm going to do the opposite of my usual order,
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I usually like to build a lot of context first.
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But I thought in the spirit of the Harvard Business School and the equivalent at
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Stanford, I'd start off with things like results and process and methods first.
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So you have a feeling that I'm actually [COUGH] saying something that you might
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But the important talk is really on Thursday,
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I'm still struggling with making it the right size.
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So today we're going to do that, and I urge you to copy my email address.
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A lot of you emails, we're not going to have time
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to have the discussion we should have here.
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I welcome any questions and by the way, while I'm talking,
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Okay and here's why school pisses me off.
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A great man in our field, Marvin Minsky observed that this is the best place ever
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to keep you from ever thinking about anything for long enough.
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So you don't want to try and learn in a classroom, it's terrible.
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In fact, I don't like to do classroom process,
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partly because of the time constraints.
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But also because it's oral,
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we might as well be sitting around a campfire, a hundred thousand years ago.
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And almost everything that's happened that's important in the last
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several thousand of years, has been basically, literary informed.
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Okay, so one of my favorite Picasso sayings,
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and he meant a lot of different things by this.
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Part of it is the best you can ever do with any kind of representation is make
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a kind of a map, even when you're trying to make a map of a map.
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You're making a lie compared to the thing you're trying to represent.
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But if you do it right,
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people can gain some intuition about what the map is about.
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That's what science is all about.
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This talk in 50 minutes has to be a lie, it really is.
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I'm leaving out a lot of important things.
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But I think the shadow of the talk or whatever is projected by the talk is
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pretty close to the way things actually are for doing this very different process.
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And I think Picasso didn't say, but he knew and
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he meant, is art is also the lie that tells the truth that wakes you up.
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So if something wakes you up in the next two days, I will have done my job.
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And then, the two days are really about another Picasso quote which is,
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learn the rules like a pro so you can break them like an artist.
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And not doing this is probably the greatest sin of Silicon Valley
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since the 80s.
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Almost never, nobody has bothered becoming a pro particularly in software and
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user interface design.
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So when they break the rules,
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they're breaking them like a dumb child does by throwing rocks through windows.
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And it's probably the most sickening thing to see about the field.
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So another way of thinking about the mantra is,
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you have to learn everything and then find a way of forgetting it.
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So you can have your own ideas, but
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what you forget is everything except the perfume.
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So when you have an idea then your nose will pick up the right scent.
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And you'll be able to make use of all of the stuff that you've learned after you've
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I was specifically asked to talk about Xerox PARC
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because that was an example of making trillions instead of billions.
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And, so I thought what I'll do here is just show
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you a couple of results, from Xerox PARC.
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And I'll then try to give you some of what it took to get those results.
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So, Xerox PARC is known for this machine that happened, I gave one to Sam.
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He got it working with the help of some really great people.
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So this machine happened in 1973 which is 11 years before the Mac, and
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its screen was more than twice the size of the Mac.
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It was more like a Mac of 1988 or 1989.
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So it was about maybe 15 or 16 years ahead of the commercial development.
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And the commercial development,
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actually, was based on the stuff that was done on this machine.
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So this is an example of the best way to predict the future is to invent it.
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There's nothing like this before.
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Once we did it, people could see, yeah,
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there could be something like this because here it is.
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In fact, we made 2000 of these, so there's a lot to look at.
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They have bit map screens, they had pointing device.
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The famous GUI, which you're still using today.
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WYSIWYG means what you see is what you get.
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Desktop publishing and the whole media gig.
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What does that say?
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That's not helping me, anybody read that?
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>> Symmetrical reading and writing.
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>> Yeah, symmetrical reading and writing.
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Meaning what you almost never get on the web, but that you did get in
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many of the apps of the 1980s, which is when you get a document,
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the thing that you read the document with also allows you to edit the document.
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So if you think about the web, it's actually much more made for
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consumption than it is made for authoring, they're completely decoupled.
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And most of the authoring facilities are typing in tiny little windows, and
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then pushing a button to see what you did.
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we just called it object oriented programming back then.
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I made up that term.
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But what's called object oriented programming today is
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And I don't think I'll have time to really talk about the profound difference.
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So if I have to say that what we did at PARC became popular, and
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Comparison is you can go out and buy a set of designer jeans with the label Harvard
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on them, and for all I know with Stanford on them.
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Laser printer, main difference between this and
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what you have today is the first one was a page a second.
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So most people have never printed with a page a second printer.
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Peer-peer and client server, and 50% of the Internet.
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PARC did an Internet before there was the Internet.
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And we're part of that community, so we participated in the official Internet.
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So we think of that as eight and a half inventions.
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How long did it take?
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Think about that.
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Two dozen people did all of these things in about five years.
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Cost about 10 million to 12 million bucks a year in today's money.
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Return is about 35 trillion, this is an old number,
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it's probably more like 40 now.
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So that's pretty good return on investment if you think about it.
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If you like to do little spreadsheets and stuff.
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15% is good, right Sam?
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Yeah, so, and it was an industry rather than an increment.
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People say, well, but Xerox didn't benefit from this.
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They didn't make any money for it, that's complete ****.
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It's made up by companies that don't want to invest in research.
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Xerox made about a factor of 250 over their entire
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investment in PARC, so that's 25,000% return right there.
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Xerox's bug was they didn't understand the rest of this stuff, but they
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certainly understood what a laser printer was, and they made billions from it.
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And in fact, if you look at it, it was better for
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the world that they stonewalled us,
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because no single company can handle an entire industry.
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So this is made it possible: the Japanese had to do printers slightly,
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And this, kind of heaven on Earth, lasted for about 12 years,
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Xerox finally fired the guy who made it all happen.
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You're thinking, he would have been rewarded,
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right, because he made all this stuff happen, but in fact, they hated him.
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And they hated him for
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the very reason that most companies hate people who are doing something different,
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because it makes middle management and upper management extremely uncomfortable.
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What they want to do is make a few millions in a comfortable way.
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And so this leads to an enormous problem that where
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the real job of upper management in the 20 and 21st century is to learn things,
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because change is the constant thing that's going on.
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What they tried to do is to maximize whatever they had when they got
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If you think about it, if large companies were actually rational,
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There'd be no startups, because large companies have vastly more resources for
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doing new things than any venture capitalist, right Sam?
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They've got gazillions of money, but because they refuse to get into
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new businesses and refuse to change their old businesses, you guys have a chance.
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So, just pray that they don't ever wake up.
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Now, the interesting thing is if you look at that collection of inventions,
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And what's interesting is if I were to enlarge this a bit, I would show that
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here's hardly been anything interesting invented since this funding stopped.
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So it's been a nuke, basically a nuclear winter of
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people cashing in on these inventions, and extracting the wealth from them, but
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hardly, any efforts of any interesting kind to go beyond them.
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And last point here is the lack of curiosity.
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I met Sam, because he wanted to know where'd all this stuff come from?
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I've hardly ever been asked that question.
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It's the most successful generation of wealth in computing history,
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and almost nobody wants to know, Sam did.
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And the difficult thing about today and Thursday is the world that you grew up in,
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because I don't see anybody, is anybody here older than 35?
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Okay, yeah, I see a little more reflectivity back there.
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>> [LAUGH] >> Yeah, so
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almost everybody in the room grew up in a world that was unlike the world I'm
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going to just tell you about now.
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So it's just many, many things were qualitatively different in ways that
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are sometimes difficult to explain.
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This is why lectures suck.
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This is a 500 page book done very carefully
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about the whole story of ARPA and Xerox PARC.
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Anybody who's really interested, read this book.
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And this is a tribute I wrote to this whole research
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community about I don't know 14, 15 years ago.
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And I'll hand it out at the end of the class, and
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the main thing that is interesting about it perhaps is the bibliography,
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which has a lot of references on more detailed things you can read
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about how this particular community operated, right?
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And the important thing about Xerox PARC, which is not emphasized enough,
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is that PARC was just another one of the ARPA research projects.
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So there's eight years of research before PARC happened.
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Nobody wanted to try to do this stuff inside of a company.
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everything else are really antithetical to long range thinking.
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So this stuff was all funded by Cold War funds in the public domain.
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So none of the IP was kept secret, but it was done in a better rhythm.
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So to get the 35 trillion, it actually required not five years,
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We were lucky researchers who got our PhDs in this process and
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we're the right age to go to park and finish it off.
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Right and as we'll see I'm not going to dwell on it much more but
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he circa of this way of doing things goes back,
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especially to the radar effort at MIT and
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the air defense effort and then upper and the park.
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So there is a long continuity here of kind of how do you,
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work on things that are, That are doable but where you have to
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invent several generations of technology to get to the thing that's doable.
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So most of these things are not doable with the technology that's lying around.
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That is the problem with these hard problems.
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Okay, so the general world, the normal world.
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The present, for most of this period,
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was either punch card accounting machines, or their replacement.
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After IBM said famously they'll no, there's no room for more than five or
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six computers in the entire world,
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they wound up doing the first mass produced computer, the 1401.
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To replace their punch card machines before other computer companies did.
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So they did a sort of Trump like reversal, on their belief and
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the bound up owning the 60s and much of the 70s.
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And most important thing, this is a dumb sentence because,
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you don't know what these machines were like.
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So I realized after I put it in there that, why did I even put this in, right?
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But the way I look at what's going on at any given time,
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whether it's today, 30 or 40 years ago.
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Is whatever's going on right now is just crap, by definition.
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If we know about it, except in the 1/10 of 1% or
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1000 of 1%, it's gotten mundane.
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And part of it is just because of the bell curve of normality in humans.
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Whatever it is, it gets converted to something like normal.
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No matter how exciting it is.
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And so when things are widely successful they tend to bring up the mean
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of the bell curve a little bit, for everybody, raises all boats.
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But in fact, there's also this regression to the mean on almost all of these things.
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So living in the present, man,
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you're just out of it if you're trying to think about things from what we have now.
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There's the bomb project and the radar project at MIT,
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that had roughly the same process, roughly the same difficulty.
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You have to realize the US was in World War II for only about two and
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Pearl Harbour was December of 1941 and
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so we had 42, 43, 44, three and a half years.
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And a lot of things got done the Most interesting thing is for
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the first time in history, really, a lot of really good scientists and
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a lot of really good engineers started working together and cloning each other.
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So seven Nobel Prizes came out of the building 20.
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Because they were physicists who put on their engineering hats.
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To make 185 different kinds of radar systems and
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install them in every size of building and plane and boat.
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And World War II was basically a war of supply.
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And it was the ability to a Stave
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off the German submarines that had actually won the war for us.
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Most people don't think of it that way.
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But that's what happened.
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So in the circa I'm talking about I came out of this group and
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there's a couple of good books about how these people went about doing things.
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Maybe the number one thing is what I have on right hand side is basically he said,
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It doesn't matter who you are, how smart you are,
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how smart you think you are, there's only one thing that counts here,
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is making progress and we make progress through synergy and
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they learned how to do this and they passed it on generation, after generation.
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Well the next round of this, after World War II, was the cold war.
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And the air defense system that was done in the 50s.
12:44:44.263 VM76169:53
And again at MIT, the first displays that you could interact with.
12:44:53.263 VM76169:53
That is essentially a stylus in the guy's hand, it was called a light gun.
12:44:56.262 VM76169:53
So he's pointing at something on the screen and squeezing the trigger.
12:45:02.261 VM76169:53
Like you would put a Silas down and push it down and
12:45:05.263 VM76169:53
the light gun can tell, computer can tell what the light gun is working at.
12:45:11.263 VM76169:53
So you can do all of the stuff, that you're used to today.
12:45:14.262 VM76169:53
To give you an idea of what these guys did,
12:45:17.263 VM76169:53
I can't really see what this is right?
12:45:23.264 VM76169:53
It's just like it's obviously four floors of a building.
12:45:29.263 VM76169:53
And if you notice the second floor, it says computer A and computer B.
12:45:32.262 VM76169:53
Each one of those computers had 50,000 vacuum tubes.
12:45:35.263 VM76169:53
12:45:38.263 VM76169:53
Both running the same programs at the same time and many
12:45:41.264 VM76169:53
other interesting things I don't have time to talk about but, I'll just say that when
12:45:47.263 VM76169:53
one of these computers started crashing it took it three or four days to crash.
12:45:50.262 VM76169:53
And the reason is, it was running diagnostics on itself.
12:45:53.263 VM76169:53
And every time an instruction failed, it would patch
12:45:59.264 VM76169:53
in a simulated instruction based on what instructions were still working.
12:46:02.262 VM76169:53
So what would happen is the machine would just get slower and
12:46:05.263 VM76169:53
more software-like.
12:46:08.263 VM76169:53
More like a Turing machine and usually they could fix it before
12:46:11.262 VM76169:53
it came all the way down, meanwhile the other one was still working.
12:46:14.262 VM76169:53
But, even though I shouldn't be digressing,
12:46:17.263 VM76169:53
there's a fun thing here, there were 32 of these concrete bunkers made.
12:46:23.263 VM76169:53
So the two interesting things, what happened to this stuff, anybody know?
12:46:29.264 VM76169:53
Does this look like anything you've ever seen before?
12:46:35.264 VM76169:53
>> No, that's, but- >> Airline meter, sorry.
12:46:38.263 VM76169:53
>> Yeah, yeah, this actually was the invention of our,
12:46:44.264 VM76169:53
air traffic control system.
12:46:47.263 VM76169:53
That's what it was designed to do, except that it was designed to control
12:46:50.264 VM76169:53
traffic of both American and Russian bombers and
12:46:56.263 VM76169:53
in fact, if many of the displays before they replace them with flat screen.
12:47:02.263 VM76169:53
Displays is basically the same big round.
12:47:05.263 VM76169:53
So this whole system last until 1982.
12:47:11.264 VM76169:53
When the last one was finally decommissioned and 50,000 vacuum tubes.
12:47:17.263 VM76169:53
They're like incandescent light bulbs.
12:47:20.264 VM76169:53
They blow out, so they're always blowing out.
12:47:23.265 VM76169:53
So, of course, there are redundant ones there.
12:47:26.263 VM76169:53
Where did we get the vacuum tubes from?
12:47:32.263 VM76169:53
Think about it, all the way through the 70s.
12:47:35.265 VM76169:53
12:47:38.263 VM76169:53
no transistors in these machines.
12:47:41.264 VM76169:53
No transistors, these are- >> Russia.
12:47:47.265 VM76169:53
I heard it before.
12:47:50.264 VM76169:53
>> Yeah.
12:47:53.264 VM76169:53
>> [LAUGH] >> That's good, and you remember it.
12:47:56.263 VM76169:53
>> For the last 12 or 18 years of this defense system,
12:48:02.263 VM76169:53
which was never used against the Russians ever, because they never tried to bomb us.
12:48:05.265 VM76169:53
And was actually obsolete very soon because of ICBMs, it couldn't track them.
12:48:11.265 VM76169:53
There's a whole other system for doing that, yeah.
12:48:14.263 VM76169:53
But we kept it going and there are reasons why it was kept going.
12:48:20.265 VM76169:53
We bought while we're still contending in the Russia for
12:48:23.265 VM76169:53
the cold war, we buying vacuum tubes from them too.
12:48:26.264 VM76169:53
By the way, people still keep buying vacuum tubes from the Russians for
12:48:29.265 VM76169:53
12:48:32.265 VM76169:53
Getting old 50s guitar amp sounds.
12:48:35.265 VM76169:53
Those overdriven sounds.
12:48:38.264 VM76169:53
And this guy, who was one of the inventors of artificial intelligence, and
12:48:44.264 VM76169:53
the inventor of the programming language LISP, John McCarthy, looked at one
12:48:47.266 VM76169:53
of these in the 50s and said, Everybody is going to have one in their home someday.
12:48:53.265 VM76169:53
Because he didn't give a **** about
12:48:59.265 VM76169:53
Because what he thought was, yeah, this is like a electric power generating station.
12:49:08.265 VM76169:53
Nobody ever sees them but they're out there.
12:49:11.265 VM76169:53
We have wires going to the home for,
12:49:14.264 VM76169:53
it's like where we get our water from, it's where we get our gas from.
12:49:17.265 VM76169:53
The utilities, so he thought there would be an information utility, and
12:49:23.265 VM76169:53
it will actually be a human right, like the telephone, to have
12:49:29.265 VM76169:53
one of these things In your home that is connected to all the worlds information.
12:49:32.264 VM76169:53
12:49:35.264 VM76169:53
So that was one of the earliest and most influential ideas.
12:49:41.265 VM76169:53
1962, I'm going to show you a system done on one of these super computers.
12:49:47.265 VM76169:53
This is done on the test computer at Lincoln Labs for this whole SAGE system so
12:49:53.266 VM76169:53
this computer was close to the size of this entire building.
12:49:59.266 VM76169:53
With one guy on it, yeah at 3 o'clock in the morning.
12:50:02.264 VM76169:53
So, take a look at this.
12:50:05.266 VM76169:53
This is Ivan Sutherland.
12:50:11.266 VM76169:53
So, it doesn't really even have a display.
12:50:14.265 VM76169:53
It's actually simulating a computer display here.
12:50:20.266 VM76169:53
So, what Ivan wants to do, is to draw a flange and so he says,
12:50:26.265 VM76169:53
Now take these guys and make them all mutually perpendicular and
12:50:29.266 VM76169:53
12:50:32.265 VM76169:53
wow, sketch pad just solved that problem.
12:50:35.266 VM76169:53
So it's a dynamic problem solver in there.
12:50:38.266 VM76169:53
First window then clipped.
12:50:41.266 VM76169:53
Now it wants to put a hole in the flange.
12:50:44.266 VM76169:53
So these are guidelines.
12:50:47.266 VM76169:53
and the first thing he does is said, okay, I want to make this parallel.
12:50:50.266 VM76169:53
And you see, sketch pad keeps them on the line there and lines them up.
12:50:56.266 VM76169:53
And now, he's using them as guidelines to draw a dash lines.
12:51:08.266 VM76169:53
You see here, he misses Whoop, okay.
12:51:14.269 VM76169:53
They're still there.
12:51:17.266 VM76169:53
The constraint was co-linearity there.
12:51:23.266 VM76169:53
And he has a knob to continuously zoom.
12:51:26.286 VM76169:53
So this is the first computer graphics ever.
12:51:32.265 VM76169:53
And he wants some rivets to go along with that flange, and so again he,
12:51:35.266 VM76169:53
12:51:38.265 VM76169:53
this is why this system is called a sketch pad because you just casually draw.
12:51:41.266 VM76169:53
Just going to use that as the center for the ark here.
12:51:50.266 VM76169:53
And again he's going to say, take these guys and
12:51:53.267 VM76169:53
make them mutually perpendicular and here sketch pad solves that problem.
12:51:56.266 VM76169:53
So you wind up with a symmetric object.
12:51:59.266 VM76169:53
And he can change things and
12:52:05.267 VM76169:53
he'll get another solution.
12:52:11.266 VM76169:53
He could have constrained the side links to be ratios of each other.
12:52:20.266 VM76169:53
And the kind of problem solving this system could do,
12:52:23.266 VM76169:53
well included non linear problems.
12:52:29.267 VM76169:53
And what's cool about this, that is a master rivet,
12:52:32.267 VM76169:53
what we'd call a class in object oriented programming.
12:52:38.267 VM76169:53
So this is not that rivet, but an instance of that master rivet.
12:52:47.266 VM76169:53
>> Well it's putting up every dot individually,
12:52:50.266 VM76169:53
at about half the power of this super computer is being used to just do that.
12:52:56.266 VM76169:53
So here another instance, another rivet.
12:52:59.267 VM76169:53
Here is another one, here's another one.
12:53:05.267 VM76169:53
And he says whoops I didn't want to have those crossbars there, so
12:53:08.266 VM76169:53
I'll go back to the master rivet and make the crossbars invisible.
12:53:11.266 VM76169:53
They're still there but invisible, and we see the instances.
12:53:17.267 VM76169:53
Maybe nicer than you've seen it.
12:53:23.266 VM76169:53
Get rid of those guys and now,
12:53:26.268 VM76169:53
that construction that he made, he's made it into a master.
12:53:29.268 VM76169:53
12:53:32.268 VM76169:53
So he can make instances of it.
12:53:35.266 VM76169:53
Okay, get the idea?
12:53:38.266 VM76169:53
>> I had a question.
12:53:41.268 VM76169:53
>> Yes. >> [INAUDIBLE] Sort of regressed
12:53:44.268 VM76169:53
from here >> Well anybody in this class ever seen
12:53:53.268 VM76169:53
Okay, so that's part of the answer.
12:53:59.268 VM76169:53
Yeah we'll go along, it's sort of the larger question that we have.
12:54:02.267 VM76169:53
It's worthwhile bringing up the question He's rotating three of them.
12:54:11.268 VM76169:53
So, this is the most shocking thing I;d ever seen when I went to graduate
12:54:14.268 VM76169:53
This was, the system was only three years old at that time.
12:54:23.267 VM76169:53
And I've been programming for five years and just for
12:54:26.268 VM76169:53
conventional programming and seeing this, it blew my mind.
12:54:29.268 VM76169:53
Because it was exactly different than everything I thought about computing.
12:54:32.268 VM76169:53
12:54:35.269 VM76169:53
As soon as I saw I, I realized yeah, of course you can do that.
12:54:41.268 VM76169:53
But I didn't think about that.
12:54:50.267 VM76169:53
Okay so Sketchpad, interactive computer graphics in a way we recognize today.
12:54:56.269 VM76169:53
For the first time, objects, masters and instances.
12:55:02.268 VM76169:53
The programming was not done by the kind of programming today, but
12:55:05.269 VM76169:53
by problem solving, which I think you can imagine is much nicer.
12:55:08.269 VM76169:53
Like it will find the solution.
12:55:11.267 VM76169:53
And it creates automatic dynamics simulations,
12:55:17.267 VM76169:53
When you draw the bridge in, it knows how to simulate the bridge and
12:55:26.268 VM76169:53
So I said to Ivan, Ivan you did all of this and what is this PhD thesis?
12:55:29.269 VM76169:53
12:55:32.264 VM76169:53
Said, you did all this in one year by yourself, how could you possibly do it?
12:55:35.261 VM76169:53
And he said, well, I didn't know it was hard.
12:55:38.256 VM76169:53
>> [LAUGH] >> He just went after what the problem is.
12:55:44.252 VM76169:53
because you can see there are things that are of absolute goodness still today.
12:55:47.249 VM76169:53
It's not just relatively good for being done more than 50 years,
12:55:53.247 VM76169:53
And his thesis is, every other page is an apology because it doesn't do more.
12:55:59.244 VM76169:53
He wasn't working out what you could do, he was working on the problem.
12:56:05.243 VM76169:53
Yeah, so sketch pad was a bombshell in
12:56:11.243 VM76169:53
because right away, you were looking at the future.
12:56:17.242 VM76169:53
You just had to believe that a building sized computer was going to
12:56:23.241 VM76169:53
wind up on a laptop, or even something in a desk.
12:56:29.241 VM76169:53
So the main player here back then was a guy by the name of Lick.
12:56:32.240 VM76169:53
12:56:35.241 VM76169:53
Licklider, he got given some money in 1962 by ARPA before the day,
12:56:41.241 VM76169:53
and when anybody asked him what are you going to do, this is what he would say.
12:56:47.240 VM76169:53
Computers are destined to become interactive intellectual amplifiers for
12:56:53.241 VM76169:53
everyone in the world, universally networked worldwide.
12:56:59.241 VM76169:53
Simply gave out money.
12:57:05.240 VM76169:53
but this is a perfect class I think to put bullet points in.
12:57:11.240 VM76169:53
And I found 16, that will tell you just what you have to do.
12:57:20.241 VM76169:53
So the first thing, was just picking an idea that's worth dedicating your life,
12:57:23.240 VM76169:53
if necessary to.
12:57:29.240 VM76169:53
So, this stuff was human destiny,
12:57:32.241 VM76169:53
fixing big human problems, like we can't think very well.
12:57:35.240 VM76169:53
We need to make things to help us to think,
12:57:38.239 VM76169:53
we need to make things to help us cooperate.
12:57:41.241 VM76169:53
So these were save the world kind of ideas and they were done when the Russians where
12:57:47.240 VM76169:53
starting to test hydrogen bombs and things did not look so good.
12:57:56.241 VM76169:53
the problem is that goals tend to be much more idiosyncratic to individual humans.
12:58:02.239 VM76169:53
So research wants to be a vision.
12:58:05.241 VM76169:53
So notice there aren't any goals in the ARPA dream.
12:58:08.241 VM76169:53
And that allowed Licklider to fund 15 or 20 super
12:58:14.239 VM76169:53
smart people who thought they had ways of approaching the dream.
12:58:17.241 VM76169:53
And some of these people didn't [BLANK AUDIO] agree, and
12:59:05.241 VM76169:53
some of them hated each other.
12:59:08.241 VM76169:53
And Lick didn't give a ****, he just wanted smart people working on this dream.
12:59:14.239 VM76169:53
So fund people, not projects.
12:59:17.241 VM76169:53
ARPA never decided.
12:59:20.240 VM76169:53
And we'll see a slight modification of this.
12:59:23.241 VM76169:53
But basically, fund people, not projects.
12:59:32.241 VM76169:53
So, if you know, MacArthur grants are for individuals.
12:59:35.241 VM76169:53
But this funding was funding groups.
12:59:38.240 VM76169:53
Like you fund in MacArthur, just five years, forget it.
12:59:44.241 VM76169:53
Nobody at MacArthur asks, you don't get a second MacArthur grant.
12:59:47.239 VM76169:53
Here, they would get another grant if they'd done something good in five years,
12:59:50.241 VM76169:53
but basically the idea of MacArthur is throw away.
12:59:56.241 VM76169:53
We've identified this person of extreme potential, let's just give him five years
12:59:59.240 VM76169:53
of funding and we won't cry if they don't do anything.
13:00:02.239 VM76169:53
It turns out most MacArthur people do something because the people that
13:00:08.239 VM76169:53
tract attention are people who are not working for money.
13:00:14.240 VM76169:53
they are people like artists are people who do their art because they must.
13:00:17.241 VM76169:53
13:00:23.240 VM76169:53
Community not a project, have to fund problem finding,
13:00:32.241 VM76169:53
Most of the time, when you are working on hard problems,
13:00:35.240 VM76169:53
you don't know what the right problem is.
13:00:41.240 VM76169:53
If you pick a problem too early you might be picking it out of the current context.
13:00:47.239 VM76169:53
And therefore, you're going to be hampered unless you are incredibly lucky.
13:00:50.240 VM76169:53
So ARPA put a lot of money into just people thinking around with stuff.
13:00:56.241 VM76169:53
Milestones not deadlines.
13:01:02.239 VM76169:53
Meaning, when you lose a stroke in golf, you cry.
13:01:08.241 VM76169:53
When, you strike out in baseball, you better not cry, because you're going to do
13:01:11.241 VM76169:53
it a lot, and this is what Licklider said to them.
13:01:14.240 VM76169:53
13:01:17.241 VM76169:53
He said look, if you're batting 3.50 in baseball, you're really doing well.
13:01:23.241 VM76169:53
And if you look at what we're funding if we bat 3.50,
13:01:26.240 VM76169:53
That's what happened.
13:01:29.239 VM76169:53
Nobody cares about all the stuff that didn't work.
13:01:32.239 VM76169:53
And people said, well what about the 65% failure?
13:01:35.239 VM76169:53
And they said, well it's not failure in baseball, it's overhead.
13:01:41.241 VM76169:53
Hitting a ball is hard, when you're doing something really, really hard, the times
13:01:47.241 VM76169:53
you don't do it well is just overhead for doing it the times you do well.
13:01:50.240 VM76169:53
And this is probably the biggest distinction that business people do
13:01:53.241 VM76169:53
not understand.
13:01:56.241 VM76169:53
because what they want is actually teeny,
13:01:59.241 VM76169:53
little uninteresting projects that are guaranteed for your success.
13:02:05.241 VM76169:53
Sports is really hard, and most sports people are not succeeding all the time.
13:02:11.240 VM76169:53
13:02:14.239 VM76169:53
What's the thing in baseball, it's called an error?
13:02:17.240 VM76169:53
Like what?
13:02:20.241 VM76169:53
>> [INAUDIBLE] >> No, just fielding.
13:02:23.241 VM76169:53
>> Yeah, what's an error in baseball?
13:02:26.240 VM76169:53
Not catching a fly ball.
13:02:29.242 VM76169:53
How good are the average fielders?
13:02:32.239 VM76169:53
They're 98.5% effective.
13:02:38.240 VM76169:53
So, really good ones are like one percent error and so an error and
13:02:41.239 VM76169:53
technical stuff is designed to build a computer system and failing to build it.
13:02:47.241 VM76169:53
Like anybody should be, decide to build a software and failing to build it.
13:02:53.240 VM76169:53
But you're in this other range when you're trying to do design.
13:02:59.240 VM76169:53
So, here's a memo Lick wrote in 63,
13:03:05.241 VM76169:53
shortly after he got this initial money from ARPA to members and
13:03:11.240 VM76169:53
affiliates of the Intergalactic Computing Network.
13:03:17.240 VM76169:53
He said, well engineers always give you the minimum, and I want a network that
13:03:23.240 VM76169:53
spans the entire planet, so I'm asking for an intergalactic one.
13:03:26.239 VM76169:53
And when they scale it down, we'll still get, so
13:03:29.241 VM76169:53
that's where the internet came from, literally.
13:03:32.239 VM76169:53
The original name of the Internet was the intergalactic computing,
13:03:35.241 VM76169:53
nobody knew how to do it back then.
13:03:38.240 VM76169:53
For instance, packet switching had not been invented in 63.
13:03:44.240 VM76169:53
And here's a nice line in that memo, if we succeed in making an intergalactic
13:03:47.239 VM76169:53
network, then our our main problem will be learning to communicate with aliens.
13:03:50.241 VM76169:53
And he meant this in the biggest possible way, and
13:03:53.241 VM76169:53
I don't have time to really explain it, it's really interesting.
13:03:56.241 VM76169:53
People who are interested in this should write me an email.
13:04:02.240 VM76169:53
He meant other software, other computers.
13:04:05.242 VM76169:53
He meant what does it mean to communicate when you scale things up?
13:04:11.242 VM76169:53
So this guy was a big thinker.
13:04:14.241 VM76169:53
That's of course referring to Washington, DC.
13:04:17.241 VM76169:53
But this is a general principle.
13:04:20.241 VM76169:53
They asked him why, and he said, well, because there's too much noise.
13:04:23.240 VM76169:53
There's just too much politics.
13:04:26.242 VM76169:53
And nobody does research in Washington, DC.
13:04:32.241 VM76169:53
So the last thing we want to do, as our profounders, is to try and
13:04:35.241 VM76169:53
Our job is to get money out of the government and pass it along.
13:04:38.242 VM76169:53
And so a solution to that would say, hey let's not be here for
13:04:44.240 VM76169:53
And so every two years, so Lick set up the process to get his successor,
13:04:50.240 VM76169:53
happened that Ivan Sutherland got drafted into the army at just the right time.
13:04:56.241 VM76169:53
at age 26 Ivan ran this whole show and man was he good.
13:05:05.240 VM76169:53
He was a second lieutenant sharing meetings with generals and
13:05:11.241 VM76169:53
one of the most famous things recalled of that.
13:05:14.240 VM76169:53
Some general was going on and on at some point Ivan said,
13:05:17.241 VM76169:53
general you have just two minutes to make your point if you have one.
13:05:23.241 VM76169:53
That's about the simplest way of describing Ivan.
13:05:26.241 VM76169:53
Bob Taylor and Larry Roberts.
13:05:32.241 VM76169:53
Taylor is special because he is also the guy who set up park.
13:05:38.239 VM76169:53
Licklider was intuitively wonderful,
13:05:41.241 VM76169:53
Taylor was a student of what was wonderful about Licklider.
13:05:44.241 VM76169:53
And he could explain everything that Licklider could do and why it worked.
13:05:50.239 VM76169:53
So here's a couple things, I'm not going to go about, but
13:05:56.241 VM76169:53
basically the idea here is it isn't like lets look around and
13:05:59.241 VM76169:53
see what is available and what we can do with it.
13:06:02.239 VM76169:53
The idea was stick with the vision and
13:06:05.241 VM76169:53
just make every frigging thing that's necessary.
13:06:11.241 VM76169:53
If we have to make a new kind of integrated circuit, we'll do it.
13:06:14.239 VM76169:53
All of these things were done.
13:06:17.241 VM76169:53
So it's very much like these other cultures where nobody worried about
13:06:23.241 VM76169:53
whether you had half centimeter radar in building 20 at MIT, they just did it.
13:06:29.240 VM76169:53
And here's one that is really a bug today.
13:06:32.241 VM76169:53
And if you don't have the chops, yeah you shouldn't make your tools,
13:06:35.241 VM76169:53
you shouldn't make your own operating system.
13:06:38.239 VM76169:53
You shouldn't make your own programming language because that's not what you're
13:06:41.241 VM76169:53
If you do have the chops, and you better not do his unless you have the chops,
13:06:47.240 VM76169:53
then you have to make your own hardware and software and
13:06:50.240 VM76169:53
operating system in the programming languages.
13:06:53.241 VM76169:53
Otherwise you're working in the past on some vendor's bad idea of what
13:06:59.241 VM76169:53
So part of this deal, remember what Picasso said.
13:07:02.239 VM76169:53
You have to get really much, much better than
13:07:05.241 VM76169:53
what most people want to today, and here's another reversal.
13:07:11.240 VM76169:53
Today people buy hardware and
13:07:14.239 VM76169:53
put software on it basically to make the hardware look good.
13:07:17.241 VM76169:53
The ARPA community in part did exactly the opposite.
13:07:23.241 VM76169:53
Using super computers to simulate the thing.
13:07:29.241 VM76169:53
then you'd design the computer that would optimize and
13:07:35.241 VM76169:53
That is what Sketchpad was.
13:07:38.241 VM76169:53
Nobody thought that the next graphic systems were going to be done on
13:07:47.241 VM76169:53
Okay, and then make a bunch of them.
13:07:50.239 VM76169:53
So almost every project in the ARPA project,
13:07:53.241 VM76169:53
they actually made enough of them so they could be used as tools.
13:07:56.241 VM76169:53
They weren't just demos.
13:07:59.241 VM76169:53
And so part of this invention process had to require a kind of limited engineering.
13:08:05.241 VM76169:53
instance, had a thing where whatever you did you had to make 100 of them.
13:08:08.240 VM76169:53
Made an Ethernet, it had to run 100 machines.
13:08:11.241 VM76169:53
If you made a time sharing system, it had run 100 users.
13:08:14.239 VM76169:53
If you made a personal computer, you had to be able to build 100 of them.
13:08:20.241 VM76169:53
Learn how to argue.
13:08:26.239 VM76169:53
Argue for clarity, not to win.
13:08:29.240 VM76169:53
People are always contending with each other, trying to be winners and losers,
13:08:35.240 VM76169:53
and that's one of the problems with Washington,
13:08:41.241 VM76169:53
Now what you have to do is understand these complicated things.
13:08:44.241 VM76169:53
Here's a biggie, every researcher at Park was a second or
13:08:50.241 VM76169:53
third generation PhD that ARPA had created.
13:08:53.241 VM76169:53
Right, eight years.
13:08:56.241 VM76169:53
It wasn't just the stuff in the past.
13:08:59.240 VM76169:53
I'll show you a couple more of those, wow we're getting closed to, but
13:09:02.239 VM76169:53
it was creating the next generation.
13:09:08.240 VM76169:53
Baseball you have to develop talent going all the way the down to little league.
13:09:11.241 VM76169:53
13:09:14.239 VM76169:53
And they did and the other thing is a little difficult to,
13:09:17.240 VM76169:53
13:09:20.241 VM76169:53
some of you will understand this readily.
13:09:23.241 VM76169:53
That the reward of doing this stuff wasn't the reward of making it happen
13:09:26.239 VM76169:53
because a lot of times it didn't happen.
13:09:29.241 VM76169:53
It was a reward of actually being funded to work on what the actual problems were.
13:09:38.241 VM76169:53
I can't emphasize this too much.
13:09:41.246 VM76169:53
Working on what the problems actually are rather than something that's going to get
13:09:47.240 VM76169:53
a paper, something that's going to make you money, but taking big human problems.
13:09:53.241 VM76169:53
Could be something like drinkable water.
13:09:56.241 VM76169:53
For the 70% of the planet that doesn't have it.
13:09:59.241 VM76169:53
If you pick one of those things that's a great one, worth putting your life into.
13:10:05.241 VM76169:53
Okay, so background was partly tinkering.
13:10:14.239 VM76169:53
A lot of people came from New York, down where the world trade center was.
13:10:17.240 VM76169:53
Before the world trade center, there was about a mile across Manhattan Island that
13:10:23.241 VM76169:53
was nothing but electronic surplus stores.
13:10:26.239 VM76169:53
Mow a few lawns, put a dime into the subway and
13:10:29.241 VM76169:53
you could go down there and buy almost anything to mess around with.
13:10:35.241 VM76169:53
And that group got used to, everybody was broke.
13:10:41.241 VM76169:53
There was a kit to build your own oscilloscope.
13:10:47.241 VM76169:53
And if you wanted an oscilloscope and you were broke,
13:10:50.239 VM76169:53
you could get one of these on time for $30 or $40 bucks and build it.
13:10:56.241 VM76169:53
And I'm going to just
13:10:59.241 VM76169:53
put, these are just washed out anyway so I'm just going to go past.
13:11:08.241 VM76169:53
Okay, so Moore's Law, this was the original thing in Moore's paper.
13:11:11.241 VM76169:53
13:11:14.239 VM76169:53
Doubling every year.
13:11:17.240 VM76169:53
He picked MOS silicon which is too slow to make things out of, but
13:11:23.241 VM76169:53
Here's doubling every two years, and what happened.
13:11:29.241 VM76169:53
The prediction was 30 years and so what happened was, very in line
13:11:32.240 VM76169:53
with doubling every two years to every 18 months, and there is physics behind this.
13:11:41.240 VM76169:53
This wasn't just an engineering aspiration, and so
13:11:44.241 VM76169:53
if you believe this you had something really worthwhile.
13:11:47.241 VM76169:53
>> Set it up so I see it like that, that leaves a corner.
13:11:53.239 VM76169:53
>> I think you should see a little of this though,
13:11:56.241 VM76169:53
because the same time the mouse was invented this was done at Rand.
13:12:02.240 VM76169:53
well at midnight they will go through people's waste paper baskets.
13:12:08.240 VM76169:53
To see how they worked, what were they throwing away?
13:12:11.264 VM76169:53
And what people were doing when they were working was making all
13:12:14.241 VM76169:53
kinds of diagrams and little flowcharts and all this stuff.
13:12:17.241 VM76169:53
So they said, okay, well let's invent the first tablet,
13:12:20.240 VM76169:53
As good as most of the tablets you've ever used today.
13:12:26.240 VM76169:53
It's the hallmark of these people is they generally did it good enough and here's-
13:12:29.241 VM76169:53
>> You may start to edit the flow diagram.
13:12:35.241 VM76169:53
First we erase the flow arrow, then move the connector out of the way, so
13:12:38.239 VM76169:53
that we may draw a box in it's place.
13:12:44.241 VM76169:53
The printing in the box is being used as commentary only in this case.
13:12:50.239 VM76169:53
The box is slightly too large, so we may change its size.
13:12:56.241 VM76169:53
Then, draw a flow from the connector to the box.
13:13:02.240 VM76169:53
Attach a decision element to the box, and draw a flow from it to scan.
13:13:08.241 VM76169:53
We then erase the flow- >> Okay so this is about 1968 or so.
13:13:14.240 VM76169:53
Again on a big room sized mainframe, with one guy.
13:13:17.241 VM76169:53
13:13:20.241 VM76169:53
But, this system really, you could ask your question even more about this.
13:13:23.242 VM76169:53
This system was [LAUGH] one of the best systems I've ever used in every.
13:13:29.241 VM76169:53
It felt so intimate, it was so different from the mouse.
13:13:32.241 VM76169:53
It was one of the things that we actually looked at.
13:13:38.240 VM76169:53
I guess I do have to show the next thing here.
13:13:44.240 VM76169:53
Just so you think that VR wasn't something done recently.
13:13:47.240 VM76169:53
But the second thing Ivan did after he came back,
13:13:50.241 VM76169:53
the thing that when it came back from Arpo was to do these.
13:13:53.241 VM76169:53
I worked on this one when I was a graduate student.
13:13:56.241 VM76169:53
You could grab things and have the thing in your hand, grab things and
13:13:59.240 VM76169:53
move them around it.
13:14:02.239 VM76169:53
It's a little exciting because instead of the thing that you used to today.
13:14:05.241 VM76169:53
In the art had two CRTs with 15,000 volts right above your ears crackling away.
13:14:11.239 VM76169:53
So it was a very exciting thing to do and, of course,
13:14:14.239 VM76169:53
one of the hardest things was to do good head positioning back then.
13:14:17.241 VM76169:53
There are many ways of doing it.
13:14:20.240 VM76169:53
One of the things they did here, I won't explain what it is, but
13:14:23.241 VM76169:53
it was hooked to a crane.
13:14:26.241 VM76169:53
So as you walked around this big room at MIT,
13:14:29.240 VM76169:53
a crane would automatically follow you with the positioning thing over your head.
13:14:32.241 VM76169:53
To find out what this was about.
13:14:38.241 VM76169:53
And, man, I'm getting killed because I should stop right now.
13:14:44.240 VM76169:53
But I really have two more things to do, if you could give me two more minutes,
13:14:47.241 VM76169:53
First one here is, this is something you have to think about.
13:14:53.241 VM76169:53
Let time go one way, and progress go the other way.
13:14:59.241 VM76169:53
Yay boo, yay boo, it goes up it goes down.
13:15:02.240 VM76169:53
This is the way people tend to measure things.
13:15:05.241 VM76169:53
If you put in a threshold, then the only things above the threshold count.
13:15:11.241 VM76169:53
Like if these are reading scores, Nothing counts.
13:15:14.240 VM76169:53
13:15:17.241 VM76169:53
We, of course, never get above the threshold.
13:15:23.241 VM76169:53
And, what is actually needed changes over in time.
13:15:26.241 VM76169:53
You need more, and if you're measuring to a baseline, which people usually do.
13:15:32.241 VM76169:53
One of the ways of improving things, or
13:15:35.241 VM76169:53
making it look like you're not doing such a bad job is to lower the base line.
13:15:38.241 VM76169:53
For example, Apple completely lowered the base line on what
13:15:41.241 VM76169:53
constitutes reasonable user interface for iPhones and iPads.
13:15:47.239 VM76169:53
Interfaces on the system beforehand had an undo, and these don't.
13:15:50.240 VM76169:53
And I can name 15 more things.
13:15:56.241 VM76169:53
So what they did is they decided,
13:15:59.241 VM76169:53
well we're just not going to work on all of those problems anymore.
13:16:02.240 VM76169:53
We're going to condition the unsophisticated public
13:16:05.241 VM76169:53
to do work at the level of a two year old, or a 92 year old.
13:16:11.241 VM76169:53
And we're just going to eliminate everything in between.
13:16:14.239 VM76169:53
And the public has bought it,
13:16:17.241 VM76169:53
because you don't want to count noses when you're looking for quality.
13:16:20.239 VM76169:53
People can be talked into anything.
13:16:23.241 VM76169:53
So what you have to do on this stuff is you have to pick
13:16:26.239 VM76169:53
something that is absolutely above that line.
13:16:29.241 VM76169:53
We call that MacCready "Sweet Spot", he's the guy who did manpowered flight.
13:16:35.241 VM76169:53
And once you have achieved that, it opens up a whole region
13:16:38.241 VM76169:53
that you can explore, and that's what we did at park.
13:16:44.241 VM76169:53
At Moon Shot, was set space travel back 50 years.
13:16:50.241 VM76169:53
No we don't need a moon shot.
13:16:53.241 VM76169:53
You just don't do space travel with chemical rocketry.
13:16:56.241 VM76169:53
And what’s interesting about the people who like Jeff Besolson.
13:17:05.241 VM76169:53
Yeah, Elan, [LAUGH] they don't get it.
13:17:11.241 VM76169:53
Every child who read science fiction in the 50s knew this.
13:17:14.241 VM76169:53
13:17:17.241 VM76169:53
What that means is, you either have to carry a shitload of reaction mass with
13:17:23.241 VM76169:53
you, if you're doing chemicals, because you can't get the velocity high enough.
13:17:26.240 VM76169:53
So you have to put out a lot of mass, and if you do that you have to lift that mass.
13:17:29.241 VM76169:53
So you wind up with 45 story building rockets just to get into orbit.
13:17:35.241 VM76169:53
That is nuts.
13:17:38.240 VM76169:53
Anyway, I will not go on that.
13:17:41.239 VM76169:53
So, but the thing that was a tragedy was that they were very good proposals for
13:17:47.241 VM76169:53
how to get that high exhaust velocity Using various forms of atomic power.
13:17:53.241 VM76169:53
And it was something that the public was not interested in,
13:17:56.241 VM76169:53
the moon shot was not about space travel.
13:17:59.241 VM76169:53
Okay, so, I'll skip past this.
13:18:05.241 VM76169:53
Because I want to end with, yeah, here's, so
13:18:08.240 VM76169:53
this tablet computer I thought up in 1968.
13:18:11.241 VM76169:53
13:18:14.240 VM76169:53
One was the familiar tablet, and
13:18:17.241 VM76169:53
the other one was what Ivan's had mount of display was inevitable with Moore's Law.
13:18:23.241 VM76169:53
And then, Nicholas Negroponte had this idea of wearing a watch and
13:18:26.240 VM76169:53
communicating with the rest of the world.
13:18:29.241 VM76169:53
Okay, so here's the last little segment, and then I'll let you go.
13:18:35.241 VM76169:53
Wayne Gretzky, you know who Wayne Gretzky, is I'll bet.
13:18:38.240 VM76169:53
Greatest hockey player who ever lived.
13:18:41.241 VM76169:53
And he was just a little guy, he tried to avoid fights.
13:18:47.241 VM76169:53
They asked him why he took so many shots on goal.
13:18:50.241 VM76169:53
And he says, well you miss 100% of the shots you don't take.
13:18:56.241 VM76169:53
He scored more goals than anybody in history by a thousand.
13:18:59.241 VM76169:53
So, the fact that his percentage of
13:19:05.241 VM76169:53
misses was also high was irrelevant.
13:19:08.241 VM76169:53
And they asked him, why he was better than anybody else?
13:19:11.240 VM76169:53
And he said, well, a good hockey player goes to where the puck is,
13:19:14.240 VM76169:53
a great one goes to where the puck is going to be.
13:19:17.241 VM76169:53
And he didn't mean tracking the puck.
13:19:20.241 VM76169:53
He meant getting into a place where somebody could pass him the puck where
13:19:26.240 VM76169:53
So what he did is he looked at the entire configuration,
13:19:29.241 VM76169:53
saw where the future was going to be, and went to that place in the.
13:19:32.240 VM76169:53
So you can make a game to invent the future out of this.
13:19:38.239 VM76169:53
And so you start off with a cosmic goodness intuition.
13:19:41.241 VM76169:53
Like for me that tablet one was good.
13:19:47.240 VM76169:53
You identify a favorable exponential, like Moore's Law.
13:19:50.240 VM76169:53
You take the cosmic intuition out 30 years,
13:19:53.241 VM76169:53
and you ask, can we say, wouldn't it be ridiculous if we didn't have this.
13:19:59.240 VM76169:53
And say 30 year out, or 30 years away, well of course, we'll have one.
13:20:02.241 VM76169:53
That's what that exponential means.
13:20:05.241 VM76169:53
And bring it back to the 10, 15 year point.
13:20:11.241 VM76169:53
And that point, you can just pay money because that's also what Moore's law and
13:20:14.241 VM76169:53
13:20:17.241 VM76169:53
Is, if you pay a lot of money now,
13:20:20.241 VM76169:53
you can make the commodity computer of 10, 15 years in the future.
13:20:26.241 VM76169:53
It was about 50 times faster than what you got out of the time sharing terminal.
13:20:32.241 VM76169:53
And make a bunch of them, And you intertwine it, so, run the software.
13:20:38.240 VM76169:53
And then you can do two kinds of computing in the future.
13:20:41.241 VM76169:53
One kind of computing is zillions of experiments to get a user interface
13:20:47.241 VM76169:53
that would be universal for.
13:20:50.240 VM76169:53
Right now, 5 billion people, and deal with
13:20:53.241 VM76169:53
millions of people who were doing applications you've never seen before.
13:20:59.241 VM76169:53
the thing on the right there is Microsoft Word as it was at Xerox Park in 1974.
13:21:05.241 VM76169:53
If you optimize the code,
13:21:08.241 VM76169:53
you can get what the applications are going to be ten years out.
13:21:11.241 VM76169:53
So those are the two things you can do by doing this super computer thing.
13:21:14.240 VM76169:53
And it cost money.
13:21:17.241 VM76169:53
In today's dollars those altas cost about 125K a piece,
13:21:20.241 VM76169:53
and we made two thousand of them.
13:21:23.241 VM76169:53
Got to be a funder who's serious.
13:21:29.241 VM76169:53
Xerox went bat **** when we did it.
13:21:32.241 VM76169:53
They really went bat **** when we wanted to cash them in.
13:21:35.240 VM76169:53
But I'll just leave you this since I'm over time now.
13:21:38.244 VM76169:53
I'll leave you this as one of the ways of escaping the present.
13:21:44.241 VM76169:53
Have a glimmer of an idea, take it so far out that you don't have to worry about how
13:21:50.240 VM76169:53
you're going to get there, and then you just bring it back.
13:21:53.241 VM76169:53
So instead of innovating out from the present,
13:21:56.241 VM76169:53
what you want to do is to invent the future from the future.
13:21:59.239 VM76169:53
You are living the future, and bring the future back.
13:22:02.240 VM76169:53
Thank you. >> [APPLAUSE]