Speaker 1: We're going to start our story today. In nineteen sixty eight,
Speaker 1: way before I was born and probably before you were born. Alistair.
Speaker 1: Very funny, Yes, I was born after so before either
Speaker 1: of us were born. Terry Winnigrad was a young grad
Speaker 1: student in Massachusetts, and Terry had just started at m
Speaker 1: I t S brand new artificial intelligence lab. Basically, the
Speaker 1: culture was, look, nobody has ever tried doing all this
Speaker 1: kind of stuff with computers. They've done business calculations or whatever,
Speaker 1: but nobody's trying to get them to see things, or
Speaker 1: to move a robot arm or to do language. And
Speaker 1: therefore we're on the exploring cutting edge, and we're going
Speaker 1: to solve all these problems right soon. And Terry started
Speaker 1: building a computer program that turned into his dissertation, which
Speaker 1: he called shirt looke Yeah, with only one vowel, s
Speaker 1: h r d l U. We're watching a video here
Speaker 1: of a demo of shurtloo that Terry had recorded at
Speaker 1: the time. It's this silent, black and white, low rez,
Speaker 1: grainy video, and you see a virtual table top and
Speaker 1: a bunch of geometric shapes on top. It looks like
Speaker 1: a computer sketch of a really really boring form of Tetris. Yeah,
Speaker 1: chat bots are everywhere today, but Shirtlou was really one
Speaker 1: of the original chat bots in history. Using this machine
Speaker 1: called a teletype that's like a typewriter on a tray
Speaker 1: hooked up to a gigantic computer the size of a
Speaker 1: small bathroom, Terry created this interactive assistant that could help
Speaker 1: you navigate the virtual world of blocks on a table.
Speaker 1: So you could type in something like pick up the
Speaker 1: red block and stack it on top of the blue cube,
Speaker 1: and like magic, this virtual robotic arm would appear and
Speaker 1: do that stacking for you. And you could ask your
Speaker 1: lue questions too, like which cube is sitting on the table.
Speaker 1: And on the display screen you see this text show up,
Speaker 1: letter by letter, replying to your prompt. And the amazing
Speaker 1: thing about Surtly was the people into acting with it
Speaker 1: would use normal English. You didn't have to click on
Speaker 1: a bunch of buttons, you didn't have to throw in
Speaker 1: a string of numbers, and you didn't have to use
Speaker 1: any obscure computer programming language. Yeah, watching this video of
Speaker 1: the program, now it feels like you're watching two people
Speaker 1: texting each other, even though it's a person talking to
Speaker 1: a computer. And so if you and I are impressed
Speaker 1: watching this thing in action today. You can imagine how
Speaker 1: stunned people were when they saw this half a century ago.
Speaker 1: And from there the field of AI was supposed to
Speaker 1: take off like a rocket ship. Terry hunker down to
Speaker 1: bring Shortlood to life, and he wanted it to work
Speaker 1: in a universe bigger and more complicated than just a
Speaker 1: couple blocks on a table. But the deeper he got
Speaker 1: into it, he was running into more and more obstacles.
Speaker 1: And after a while he gave up on Shurtle, and
Speaker 1: he gave up on AI. He ended up leaving the
Speaker 1: field all together. Hi. This is Aki Ito and I'm
Speaker 1: Alista bar And this week on Decrypted, we're taking you
Speaker 1: to Stanford to meet one of the most influential thinkers
Speaker 1: in the history of computing. This is an early pioneer
Speaker 1: in the field of AI who built one of the
Speaker 1: most impressive reasoning machines and ultimately concluded that computers wouldn't
Speaker 1: be able to match human intelligence in his lifetime. And
Speaker 1: when he came to that conclusion, Terry Winnigrad dedicated the
Speaker 1: rest of his career to improving computers not as a
Speaker 1: replacement for human thought, but as a tool to help
Speaker 1: all of us. Terry's fingerprints are everywhere in the technologies
Speaker 1: powering modern life today, including Google Search, which started out
Speaker 1: as Larry Page's grad school research project, and this was
Speaker 1: something that Terry supervised. Now, with all these digital assistance
Speaker 1: powering our everyday lives, we're going to have Terry, the
Speaker 1: very creator of their precursor, test them all out. Yeah,
Speaker 1: consider him the great grandfather of Syrie or Amazon's Alexa.
Speaker 1: You might be surprised by Terry's conclusion on these devices
Speaker 1: on how far he thinks the field has come since
Speaker 1: he unveiled Shirtly to the world almost fifty years ago.
Speaker 1: And he'll give us his thoughts on where these devices
Speaker 1: are headed too. Don't worry, I have a long way
Speaker 1: to go before I become smarter than you. Okay, so
Speaker 1: let's rewind way back to a time before the days
Speaker 1: of personal computers. It's Elvis Presley is on the radio.
Speaker 1: Russia had launched Sputnik only three years ago. Russia I
Speaker 1: had blasted a man made moon into and it would
Speaker 1: be nine years until the US put a man on
Speaker 1: the moon. Terry, meanwhile, is in high school in Greely, Colorado.
Speaker 1: The physics teacher said, you know, you don't really need
Speaker 1: to sit through these lectures in my physics class. I'll
Speaker 1: give you a workshop up in the attic. It was
Speaker 1: in the attic of the school, and you wanted to
Speaker 1: build something interesting. And this is when Terry first encounter
Speaker 1: as a computer. And my father owned a what was
Speaker 1: a steel business, but had grown up from being a
Speaker 1: junkyard and had a huge collection of old junk stuff.
Speaker 1: They picked up a governments plus sales and so I
Speaker 1: took spare parts from the junkyard and I built a
Speaker 1: little computer. A box looked like a bread box. Uh.
Speaker 1: In fact, the case I put it in may have
Speaker 1: actually been a bread box. Uh. And it did a
Speaker 1: very simplistic computation, but it was it worked. He attended
Speaker 1: Colorado College, a liberal arts university, where he was a
Speaker 1: math major. But what he really fell in love with
Speaker 1: there was linguistics, the science of how language it's structured
Speaker 1: and understood. And he spent a year in London after
Speaker 1: he graduated, studying linguistics too. And after that, in the
Speaker 1: late nineteen sixties, Terry started his PhD at m I. T.
Speaker 1: S AI Lab, led by Marvin Minsky himself. Minsky is
Speaker 1: one of the godfathers of AI. Everyone felt the promise
Speaker 1: and it was you know, they say that people doing
Speaker 1: it were like undergraduates. I wasn't. I was an old
Speaker 1: person right in that group. So there was a sort
Speaker 1: of youthful sense of where the new generation we're going
Speaker 1: to make things happen. So Terry got to work on
Speaker 1: surely the Intelligent Seeming software that we introduced earlier. It
Speaker 1: was grueling work piecing together different parts of of software,
Speaker 1: but it gradually came together. I think one of the
Speaker 1: most striking things about the program, in addition to this
Speaker 1: direct visual you can see what it was doing you
Speaker 1: could talk about JOIN, is that I attempted to deal
Speaker 1: with some of the interesting properties of language in that
Speaker 1: you don't say everything that's explicitly so I used to
Speaker 1: I mean, take the most obviously ample there, put it
Speaker 1: next to a red one. A read what what is
Speaker 1: a one? And to know what you mean, you of
Speaker 1: course have to go back into the context. You have
Speaker 1: to know that previously you said find a blue block,
Speaker 1: put it next to a red one. So now you're
Speaker 1: gonna go back a sentence and find that you meant block,
Speaker 1: or you could say pick up another one, What does
Speaker 1: another mean the whole and pronounce put it? If I say,
Speaker 1: now put it in the box. It has to figure
Speaker 1: out which of the things from the previous world you
Speaker 1: meant by it. So it had a very natural flow
Speaker 1: to it. When Terry revealed short lude to the World
Speaker 1: in two in the form of an entire issue of
Speaker 1: the Journal of Cognitive Psychology, the world was amazed. We're
Speaker 1: starting here at zero. We don't know what we can do.
Speaker 1: When you make a big first step, you say, hey,
Speaker 1: I'm gonna keep going up right, I mean, and so
Speaker 1: I think that the fact they could do as much
Speaker 1: as it did certainly gave people like marvit Minsk a
Speaker 1: lot of confidence. I mean, he was I think a
Speaker 1: bigger booster of my program than I was. The confidence
Speaker 1: Terry's referring to here. That's confidence in the progress scientists
Speaker 1: were making to develop computers that were just as smart
Speaker 1: as humans. Terry's breakthrough inspired a lot of smart people
Speaker 1: to believe that sentient computers were just around the corner,
Speaker 1: and so shortly after Terry left m I t for
Speaker 1: the warmer pastures of California, back when Silicon Value was
Speaker 1: a quiet place full of fruit orchards, ah so nice,
Speaker 1: no traffic. And as a professor at Stanford and a
Speaker 1: researcher at the legendary research labs rox Park, he worked
Speaker 1: on trying to expand Shortloo. He was trying to get
Speaker 1: it to work in a more complicated environment than just
Speaker 1: a couple of geometric shapes on top of the table.
Speaker 1: And then Shirtloo took over the world and destroyed mankind
Speaker 1: or not, things weren't going the way Terry had hoped.
Speaker 1: The attempt we were making at in that project was
Speaker 1: to come to a broader analysis of meaning which could
Speaker 1: handle the ways in which meaning is much vaguer and
Speaker 1: less systematic than it was for the blocks world. And
Speaker 1: in my talks about this, I always use blocks as
Speaker 1: an example because it's a really simple one, which is
Speaker 1: in that world, block meant exactly one thing. It meant
Speaker 1: a rectangular shaped object of certain sizes on. But if
Speaker 1: I say to you, let's walk around the block, it
Speaker 1: has a second meaning which is different. So you say, okay,
Speaker 1: so you have to put in two meetings. But then
Speaker 1: I say something like that, Well, you know, I'm trying
Speaker 1: to write his paper, but there's some kind of a
Speaker 1: block I can't get over now, it's not even a
Speaker 1: physical object. It's a metaphorical physical objects. So language really
Speaker 1: works that way. Very little of it has precisely defined
Speaker 1: meetings outside of technical stuff. Uh, and we're always extending
Speaker 1: meetings and using implicit metaphor. It's not fancy metaphors, you know,
Speaker 1: life is a rose bowl of roses or whatever. But
Speaker 1: just like a block in my mental thinking, um, and
Speaker 1: trying to write the logic, the algorithms, the underlying computer
Speaker 1: stuff which could handle that is a totally different problem
Speaker 1: from just handling the simple logical stuff. And we tackled
Speaker 1: that problem. And in hindsight, I would say I was
Speaker 1: aware we weren't getting very far. And while Terry's doubts
Speaker 1: were growing, he also started hanging out with a few
Speaker 1: academics in the Bay Area. There were philosophers Hubert Dreyfus
Speaker 1: and John Searle, and there was this Chilean engineer, entrepreneur
Speaker 1: and politician called Fernando Flores. They were all making the
Speaker 1: same broad point around this time, which was this the
Speaker 1: way our brains work. So much of it happens without
Speaker 1: us explicitly thinking about it in a logical way, like
Speaker 1: if A then B and if B, then C. It's
Speaker 1: this complete black box to us. So maybe it wasn't
Speaker 1: ever going to be possible to get a machine to
Speaker 1: do all the things that are human brains can do.
Speaker 1: This philosophy resonated more and more with Terry, and in
Speaker 1: the meantime, the entire field of AI, who's having this
Speaker 1: moment of reckoning, as anybody does when they're doing a
Speaker 1: new technology and they need to get grants, they will
Speaker 1: say it's going to solve all the problems in the world, right,
Speaker 1: best things since life bread, and then it filters out.
Speaker 1: And so what happened is that a I hit this
Speaker 1: point where the claims had overreached. The results were not
Speaker 1: that great. There were good in certain small technical areas.
Speaker 1: It wasn't there was no results, but nothing on the
Speaker 1: scale of what people were promising, and that ushered in
Speaker 1: what became known as the AI winter. Research projects got defunded,
Speaker 1: startups died, All this excitement withered away. So by the
Speaker 1: nineteen eighties Terry was pretty much convinced that he'd reach
Speaker 1: the dead end, which I don't know you think would
Speaker 1: be this devastating realization for him. But right around this
Speaker 1: time something else came along up until approximately three. If
Speaker 1: you use the computer, you were a technical nerd in
Speaker 1: the basement somewhere, and the fact you had to learn
Speaker 1: all sorts of arcane stuff was yeah, that's what we do. Uh.
Speaker 1: And then there was the computer for the rest of us.
Speaker 1: We can draw a picture or it can draw conclusions.
Speaker 1: It's a personal computer from Apple up and it's as
Speaker 1: easy to use as this Macintosh, the computer for the
Speaker 1: rest of us. If you're a tech you probably have
Speaker 1: seen that ad at some point, right and Apple came
Speaker 1: out with mac and all of a sudden you had
Speaker 1: all these people who wanted to use computers who were
Speaker 1: not tech nerds, and we're not willing to learn all
Speaker 1: the arcane stuff, and so the whole field of how
Speaker 1: do you make them something that ordinary people can deal
Speaker 1: with blossom. So Terry decided not to obsess over building
Speaker 1: computers that were going to truly think, and he wasn't
Speaker 1: going to worry about understanding how the brain really works either.
Speaker 1: John Lily, a former student of Terry's who's a partner
Speaker 1: at the venture capital firm Graylock Capital, described Terry's vision
Speaker 1: like this, machines can't do everything, but focusing on what
Speaker 1: the machine capabild years is always a mistakes. He really
Speaker 1: want to pucus on the whole system, which includes humans,
Speaker 1: and they include humans and machines in the interviews between
Speaker 1: two machines is the key, and that philosophy helps shape
Speaker 1: the course of what was at the time a budding
Speaker 1: discipline called Human computer interaction h c I. Is all
Speaker 1: about designing tools to help us humans use computers more easily. Yeah,
Speaker 1: taking these clunky machines out of research labs and putting
Speaker 1: them into the hands of you and me in Silicon
Speaker 1: Valley today, you hear this acronym h CI all the time.
Speaker 1: That's a very very strong view. The printed on me
Speaker 1: for sure, like everybody looking out program massa others. Okay,
Speaker 1: so that's Marissa as in Marissa Meyer, Google employee number
Speaker 1: twenty and the CEO of Yahoo and read as in
Speaker 1: Reed Hoffman, a founding director of PayPal and the creator
Speaker 1: of LinkedIn. But the most famous of Terry students are
Speaker 1: Google co founders Larry Page and Sergey Bryn. They met
Speaker 1: as graduate students in They started working on a way
Speaker 1: to organize all the different web pages out there, and
Speaker 1: Terry supervised their project that turned into the foundations of
Speaker 1: Google Search. I mean, something that helps us access the
Speaker 1: trillions of web pages of content. I can't really think
Speaker 1: of a single digital tool that's been more useful in
Speaker 1: modern life. Thanks to Terry's guidance, Google was born as
Speaker 1: a company on September four and it's now the world's
Speaker 1: second most valuable company. Pretty cool stuff. Hey, I can
Speaker 1: calm down, calm down. We should point out that Terry
Speaker 1: wasn't always this business genius, passing down sage advice to
Speaker 1: his students. And I would say to Larry occasionally, well, yeah,
Speaker 1: that's great, but how you're going to make money with this?
Speaker 1: I think what you should do is my advice is,
Speaker 1: you know, find a company like Microsoft or somebody who
Speaker 1: needs a search engine and sell it for a nice
Speaker 1: chuck of change. I always say, they're fortunate they took
Speaker 1: my technical advice, but not my business advice. So let's
Speaker 1: fast forward about two decades to today, Larry Page. It's
Speaker 1: still at the helm of Google, and one of the
Speaker 1: company's biggest bets is its digital assistant. It's called Google Assistant,
Speaker 1: and it's connected to its new smartphone called Pixel, and
Speaker 1: it's also connected to its home device, which is like
Speaker 1: this portable speaker that speaks back to you, and that
Speaker 1: follows in the footsteps of all the other digital assistance
Speaker 1: out there. First of all, they're Sirie, which is the
Speaker 1: assistant on the iPhone. Hello therecky. There's the Amazon Echo,
Speaker 1: which is called Alexa. Hello, I'm here, And then there's
Speaker 1: Microsoft's assistant, which is called Cortana. Hey there, my friend.
Speaker 1: We wanted to know if these new helpers are useful
Speaker 1: and smart, so you better to quiz them than Terry.
Speaker 1: Along with our editor Emily A. Busso we started sending
Speaker 1: up these devices on Terry's desk in his office. Hey, Alexa,
Speaker 1: are you on Hello I'm here? Okay, great, Alexa's working yea.
Speaker 1: So we have we have Amazon Echo Alexa. These are
Speaker 1: all on Professor Winograds desk in his office. Alexa has
Speaker 1: just turned herself on you listening to me? Okay. So
Speaker 1: here's the first question. This is Terry asking Siri, where's
Speaker 1: a nightclub that my methodist uncle would enjoy? Okay, check
Speaker 1: it out? What is it? Show? Show some random nightclubs? Now,
Speaker 1: I have no idea if they have any you know,
Speaker 1: Holy holy Holy Town nightclub the grants, So it gave
Speaker 1: us nightclubs, but we're not sure something our Methodist uncle
Speaker 1: could have. Holy cow, do you think it probably understood today?
Speaker 1: We may have been religion and holy that This is
Speaker 1: the problem with this kind of AI, which is there's
Speaker 1: no logical chain you can follow, but that may have
Speaker 1: somewhere in the workings have actually caused that to have
Speaker 1: a higher ranking than something else. Okay, so maybe a
Speaker 1: B minus per Syria here. The next one went to
Speaker 1: Microsoft Quartana, where is a nightclub my Methodist uncle would enjoy?
Speaker 1: So okay, we got a bing basically the being searched
Speaker 1: with that entire phrase, and the top one is called
Speaker 1: I fell in love with my uncle who abused me
Speaker 1: from the age of that is very very experienced project.
Speaker 1: Oh no, yeah, what Why do you think they said
Speaker 1: that it did have being searched just took that whole phrase,
Speaker 1: so it had uncle. And why that comes up? Why
Speaker 1: those particular keywords? Again, the same problem they I you
Speaker 1: have no logic that can tell you why that one
Speaker 1: came up somewhere in the sorting through all the millions
Speaker 1: of things that got higher rating. Wow, there must have
Speaker 1: been something in there with with Oh, that's terrible. Probably
Speaker 1: mentioned the nightclub somewhere in it. Yeah, probably that's probably
Speaker 1: what it is. That's that's exactly what I ah. Okay,
Speaker 1: let's put Kurtana away. Here's another question Terry came up
Speaker 1: with and he tested it out on Google. If Maunaloa erupts,
Speaker 1: well I have to worry about the lava. Here here's
Speaker 1: what I found on the web. Now that one's not bad.
Speaker 1: So it found a web page from Hawaiian News called
Speaker 1: what could happen when malanal A erupts? So it didn't
Speaker 1: answer again, it didn't answer my question about here there's
Speaker 1: no way of doing that, But at least it got
Speaker 1: bunalow and erupt answer. I got an article about what
Speaker 1: could happen? You said, there's no way of doing that?
Speaker 1: Oh no, given given the techniques they use. But will
Speaker 1: there ever be a way of doing that ever? Is
Speaker 1: a hard question. It will take a mixture of techniques
Speaker 1: of which the old Ai stuff has to be resurrected
Speaker 1: in a new form which combines with the new Ai
Speaker 1: stuff in a way that at this point I think
Speaker 1: nobody has a good grip on. So so let me
Speaker 1: start off with you. I'm I'm a mechanist, I believe
Speaker 1: everything that goes on in my brain in yours is
Speaker 1: all because of electrons and chemical squirting around whatever, and
Speaker 1: therefore there's no reason that some physical device other than
Speaker 1: a brain can't do the same thing if it were
Speaker 1: probably constructed. Hey, Alexa, if Mauna loa erupts, will I
Speaker 1: have to worry about the lava here? Sorry, I can't
Speaker 1: find the answer to the question I heard. Okay, at
Speaker 1: least knowing you don't know the answer is better than
Speaker 1: making up an answer. And since we visited Terry on
Speaker 1: the Friday before the election, we had to get him
Speaker 1: to ask this too. He asked, in the order of Kurtana, Google,
Speaker 1: and Sirie, who do you want to win the U
Speaker 1: s presidential election? I honestly can't tell if that's a
Speaker 1: trick question. I suspect want triggers. That's the trick question,
Speaker 1: is my guess? Not too bad? This is Google? Who
Speaker 1: do you want to win the US presidential elect that's
Speaker 1: in the hands of informed citizens? Okay, so that one
Speaker 1: they somebody, And my guess is that's a human intervention
Speaker 1: where they there are enough people asking about the election
Speaker 1: that they put in a special thing. It's try to
Speaker 1: serious who do you want to win the election US
Speaker 1: presidential election. Election day is Tuesday, November eight, So it
Speaker 1: just triggered on the word election. I didn't pay attention
Speaker 1: to the rest of it. And after a couple more questions,
Speaker 1: we turned to Terry for his grand assessment and in
Speaker 1: general and did were your with your caution proved? Yeah?
Speaker 1: I mean there's no none of these showed the kind
Speaker 1: of understanding of person would for the same question, and
Speaker 1: then even okay, even more than that. So think back
Speaker 1: to your shirty program. How far have these come? They've
Speaker 1: gone a different direction. So sure, look could have answered
Speaker 1: questions like that perfectly if they were about these few
Speaker 1: blocks on the tabletop and nothing else period, because it
Speaker 1: was trying to do the logic. They have given up
Speaker 1: basically trying to do that, which is why they depend
Speaker 1: on things like search so much. Um and um. They've
Speaker 1: come a long way from a usefulness point of view.
Speaker 1: You sure toly was not very useful unless you were
Speaker 1: moving blocks on his tabletop. This can find you a restaurant,
Speaker 1: or it found me any club. Right now, it didn't
Speaker 1: really focus in on the ones I might have wanted,
Speaker 1: but at least let's start found me the web page
Speaker 1: about mana loa effects. So from a pure usefulness point
Speaker 1: of view, um, I think they're doing some useful things
Speaker 1: as long as you don't depend on them too much. Okay,
Speaker 1: So it's been more than forty years since Terry created
Speaker 1: Shortloo and this is how far we've come, which I
Speaker 1: don't know, it doesn't sound like a whole lot. Yeah,
Speaker 1: we've we felt pretty deflated actually our own mini AI
Speaker 1: winter right there in Terry's office. I'm interested in your
Speaker 1: view of the future, especially involving AI and the ability
Speaker 1: of of computers to to get more, more and more
Speaker 1: of arce and do maybe do things more themselves. Do
Speaker 1: these things make you feel confident in the future or
Speaker 1: just kind of blur or worried, I would say so.
Speaker 1: My view is that the kind of the advances and
Speaker 1: developments that are going on in AI are going to
Speaker 1: have lots of very practical applications. You take a lot
Speaker 1: of medical cases and you can figure out a likely
Speaker 1: diagnosis for something. I think that's gonna happen. Now. The
Speaker 1: part where you're trying to deal with people and how
Speaker 1: they're thinking and what they're asking is probably on the
Speaker 1: on the hard and not as practical end compared to
Speaker 1: all of these things. Driving cars, right, they can drive cars.
Speaker 1: I think I believe that currently they could probably drive
Speaker 1: as well as most people. And there's no sense of
Speaker 1: perfection in driving, right you're competing with human beings or um,
Speaker 1: you know, positioning, stay stations whatever. I mean, there's a
Speaker 1: zillion things which can be done better if you have
Speaker 1: a learning algorithm to help come up with the right
Speaker 1: parameters and all that kind of stuff. Um So I'm optimistic,
Speaker 1: and you know, if I were investing right, but I'm not.
Speaker 1: But you know, you could say there's gonna be a
Speaker 1: lot there. Look for companies that are finding really useful niches,
Speaker 1: not ones to say we're going to solve the grand
Speaker 1: problem all at once. Um, well, the grand problem is
Speaker 1: can you have something which is indistinguishable from how people think?
Speaker 1: And it's sort of gone off that track in a
Speaker 1: way because most of the work that's being done in
Speaker 1: these kind of programs don't really try to think like
Speaker 1: people think. Everybody knows you do not think by having
Speaker 1: a trillion examples in your head and doing you know,
Speaker 1: gigga flops of processing right to go through example. It's
Speaker 1: just done how it works. There's something else going on,
Speaker 1: and Terry here he's referring to the advances scientists have
Speaker 1: made and what's called machine learning. Instead of programming these
Speaker 1: explicit rules one by one, scientists have been able to
Speaker 1: do a lot of things by making computers and just
Speaker 1: millions of examples of the same thing, and then they
Speaker 1: use this really high powered form of statistics to learn
Speaker 1: from those examples. And that's made it possible for us
Speaker 1: to get say, self driving cars and software to recognize
Speaker 1: cats on the internet. Yeah, that's a real breakthrough there.
Speaker 1: But for something like a machine that can solve all
Speaker 1: of our problems, it's going to take the kind of
Speaker 1: leap forward that Einstein made. It's not a matter of
Speaker 1: take what we have now and just keep chugging away. Now,
Speaker 1: when are those eenstein is gonna come along? Maybe one
Speaker 1: of these in my class. Who knows. So, I guess
Speaker 1: the results of this very unscientific test that we conducted
Speaker 1: match Terry's vision all along. For the foreseeable future, computers
Speaker 1: are going to need us humans to help them with
Speaker 1: the nuances and the complexities of the real world. Although
Speaker 1: Terry did leave us with one last warning. The systems,
Speaker 1: the smart the systems that run things and so on
Speaker 1: should And I'm putting that into show it as opposed
Speaker 1: to will, because it has to happen, be made to happen,
Speaker 1: involve the combined intelligence and wisdom of people and computers.
Speaker 1: The danger, I think is that people will put in
Speaker 1: computer systems without that check and then trust them. In
Speaker 1: the military angle is a big one. Theows this whole
Speaker 1: question about robot drones. What if you put drones up
Speaker 1: in the air with weapons which we have and then say, okay,
Speaker 1: go kill bad guys. Right, Well, that shouldn't be without
Speaker 1: human in a loop. Right, there should be some sense
Speaker 1: of postility. But it's the easy thing to do from
Speaker 1: a military point of view, right. And so I think
Speaker 1: that the danger when I see one, are the dangers
Speaker 1: of a I'm not worried about machines taking over and
Speaker 1: thinking better than when I'm worried about people putting dependencies
Speaker 1: on machines which do enough intelligence things that they can
Speaker 1: let them go off on their own. And among terry
Speaker 1: students who make up this next generation of innovators, this
Speaker 1: key ingredient of morality really stuck with them too. We
Speaker 1: stopped by the office of another of Terry's former students
Speaker 1: an investor called Manu Kumar who is the founder of
Speaker 1: a seed fund called K nine. He definitely helped shape
Speaker 1: my worldview in terms of think, oh, how to be
Speaker 1: responsible as a as a scientist and a and a technologist,
Speaker 1: right um, And that plays a factor today, like there
Speaker 1: will be companies that I have passed on investing in
Speaker 1: just because I feel they're doing things that are morally
Speaker 1: or or ethically questionable, right um. And like I've I've
Speaker 1: walked away from investing in companies which, like, technically a
Speaker 1: lot of what they're doing is possible and makes a
Speaker 1: lot of sense. But but if you're doing surveillance based
Speaker 1: on reading the mac address in your phone, right and
Speaker 1: then using that information for for doing retail intelligence as
Speaker 1: an example, right, Yes, I know it's technically possible, it
Speaker 1: can be done, but should it be done? And that's
Speaker 1: it for this week's episode of Decrypted. Thanks for listening
Speaker 1: and tell us what have your experience has been with
Speaker 1: all the digital as systems out there. You can tweet
Speaker 1: at me at Akita seven and I'm at Alistair m
Speaker 1: Bar and if you're not a Twitter user, you can
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Speaker 1: can record a voice memo and send it to her
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Speaker 1: take a moment to leave us a rating and a review. Yeah.
Speaker 1: I read each and every one of them, and they
Speaker 1: really help us get in front of more listeners. This
Speaker 1: episode was produced by Emily Busso, Pierre gat Cary, Liz Smith,
Speaker 1: and Magnus Hendrickson. Alec McCabe, his head of Bloomberg Podcasts.
Speaker 1: That's it for the week's episode of Decrypted. Thanks for listening.
Speaker 1: We'll see you next week.