Will Siri Ever Outsmart Us?

kind of stuff with computers. They've done business calculations or whatever, but nobody's trying to get them to see things, or to move a robot arm or to do language. And therefore we're on the exploring cutting edge, and we're going to solve all these problems right soon. And Terry started building a computer program that turned into his dissertation, which he called shirt looke Yeah, with only one vowel, s h r d l U. We're watching a video here of a demo of shurtloo that Terry had recorded at

the time. It's this silent, black and white, low rez, grainy video, and you see a virtual table top and a bunch of geometric shapes on top. It looks like a computer sketch of a really really boring form of Tetris. Yeah, chat bots are everywhere today, but Shirtlou was really one

of the original chat bots in history. Using this machine called a teletype that's like a typewriter on a tray hooked up to a gigantic computer the size of a small bathroom, Terry created this interactive assistant that could help you navigate the virtual world of blocks on a table. So you could type in something like pick up the red block and stack it on top of the blue cube, and like magic, this virtual robotic arm would appear and

do that stacking for you. And you could ask your lue questions too, like which cube is sitting on the table. And on the display screen you see this text show up, letter by letter, replying to your prompt. And the amazing thing about Surtly was the people into acting with it would use normal English. You didn't have to click on a bunch of buttons, you didn't have to throw in a string of numbers, and you didn't have to use

any obscure computer programming language. Yeah, watching this video of the program, now it feels like you're watching two people texting each other, even though it's a person talking to a computer. And so if you and I are impressed watching this thing in action today. You can imagine how stunned people were when they saw this half a century ago. And from there the field of AI was supposed to

take off like a rocket ship. Terry hunker down to bring Shortlood to life, and he wanted it to work in a universe bigger and more complicated than just a couple blocks on a table. But the deeper he got into it, he was running into more and more obstacles. And after a while he gave up on Shurtle, and he gave up on AI. He ended up leaving the

field all together. Hi. This is Aki Ito and I'm Alista bar And this week on Decrypted, we're taking you to Stanford to meet one of the most influential thinkers in the history of computing. This is an early pioneer in the field of AI who built one of the most impressive reasoning machines and ultimately concluded that computers wouldn't

be able to match human intelligence in his lifetime. And when he came to that conclusion, Terry Winnigrad dedicated the rest of his career to improving computers not as a replacement for human thought, but as a tool to help all of us. Terry's fingerprints are everywhere in the technologies powering modern life today, including Google Search, which started out as Larry Page's grad school research project, and this was

something that Terry supervised. Now, with all these digital assistance powering our everyday lives, we're going to have Terry, the very creator of their precursor, test them all out. Yeah, consider him the great grandfather of Syrie or Amazon's Alexa. You might be surprised by Terry's conclusion on these devices on how far he thinks the field has come since he unveiled Shirtly to the world almost fifty years ago. And he'll give us his thoughts on where these devices

are headed too. Don't worry, I have a long way to go before I become smarter than you. Okay, so let's rewind way back to a time before the days of personal computers. It's Elvis Presley is on the radio. Russia had launched Sputnik only three years ago. Russia I had blasted a man made moon into and it would be nine years until the US put a man on the moon. Terry, meanwhile, is in high school in Greely, Colorado. The physics teacher said, you know, you don't really need

to sit through these lectures in my physics class. I'll give you a workshop up in the attic. It was in the attic of the school, and you wanted to build something interesting. And this is when Terry first encounter as a computer. And my father owned a what was a steel business, but had grown up from being a junkyard and had a huge collection of old junk stuff. They picked up a governments plus sales and so I took spare parts from the junkyard and I built a

little computer. A box looked like a bread box. Uh. In fact, the case I put it in may have actually been a bread box. Uh. And it did a very simplistic computation, but it was it worked. He attended Colorado College, a liberal arts university, where he was a math major. But what he really fell in love with there was linguistics, the science of how language it's structured and understood. And he spent a year in London after

he graduated, studying linguistics too. And after that, in the late nineteen sixties, Terry started his PhD at m I. T. S AI Lab, led by Marvin Minsky himself. Minsky is one of the godfathers of AI. Everyone felt the promise and it was you know, they say that people doing it were like undergraduates. I wasn't. I was an old person right in that group. So there was a sort of youthful sense of where the new generation we're going to make things happen. So Terry got to work on

surely the Intelligent Seeming software that we introduced earlier. It was grueling work piecing together different parts of of software,

but it gradually came together. I think one of the most striking things about the program, in addition to this direct visual you can see what it was doing you could talk about JOIN, is that I attempted to deal with some of the interesting properties of language in that you don't say everything that's explicitly so I used to I mean, take the most obviously ample there, put it next to a red one. A read what what is a one? And to know what you mean, you of

course have to go back into the context. You have to know that previously you said find a blue block, put it next to a red one. So now you're gonna go back a sentence and find that you meant block, or you could say pick up another one, What does another mean the whole and pronounce put it? If I say, now put it in the box. It has to figure out which of the things from the previous world you meant by it. So it had a very natural flow

to it. When Terry revealed short lude to the World in two in the form of an entire issue of the Journal of Cognitive Psychology, the world was amazed. We're starting here at zero. We don't know what we can do. When you make a big first step, you say, hey, I'm gonna keep going up right, I mean, and so I think that the fact they could do as much as it did certainly gave people like marvit Minsk a lot of confidence. I mean, he was I think a bigger booster of my program than I was. The confidence

Terry's referring to here. That's confidence in the progress scientists were making to develop computers that were just as smart as humans. Terry's breakthrough inspired a lot of smart people to believe that sentient computers were just around the corner, and so shortly after Terry left m I t for the warmer pastures of California, back when Silicon Value was a quiet place full of fruit orchards, ah so nice,

no traffic. And as a professor at Stanford and a researcher at the legendary research labs rox Park, he worked on trying to expand Shortloo. He was trying to get it to work in a more complicated environment than just a couple of geometric shapes on top of the table. And then Shirtloo took over the world and destroyed mankind or not, things weren't going the way Terry had hoped.

The attempt we were making at in that project was to come to a broader analysis of meaning which could handle the ways in which meaning is much vaguer and less systematic than it was for the blocks world. And in my talks about this, I always use blocks as an example because it's a really simple one, which is in that world, block meant exactly one thing. It meant a rectangular shaped object of certain sizes on. But if I say to you, let's walk around the block, it

has a second meaning which is different. So you say, okay, so you have to put in two meetings. But then I say something like that, Well, you know, I'm trying to write his paper, but there's some kind of a block I can't get over now, it's not even a physical object. It's a metaphorical physical objects. So language really works that way. Very little of it has precisely defined meetings outside of technical stuff. Uh, and we're always extending

meetings and using implicit metaphor. It's not fancy metaphors, you know, life is a rose bowl of roses or whatever. But just like a block in my mental thinking, um, and trying to write the logic, the algorithms, the underlying computer stuff which could handle that is a totally different problem from just handling the simple logical stuff. And we tackled that problem. And in hindsight, I would say I was

aware we weren't getting very far. And while Terry's doubts were growing, he also started hanging out with a few academics in the Bay Area. There were philosophers Hubert Dreyfus and John Searle, and there was this Chilean engineer, entrepreneur and politician called Fernando Flores. They were all making the same broad point around this time, which was this the

way our brains work. So much of it happens without us explicitly thinking about it in a logical way, like if A then B and if B, then C. It's this complete black box to us. So maybe it wasn't ever going to be possible to get a machine to do all the things that are human brains can do.

This philosophy resonated more and more with Terry, and in the meantime, the entire field of AI, who's having this moment of reckoning, as anybody does when they're doing a new technology and they need to get grants, they will say it's going to solve all the problems in the world, right, best things since life bread, and then it filters out. And so what happened is that a I hit this point where the claims had overreached. The results were not

that great. There were good in certain small technical areas. It wasn't there was no results, but nothing on the scale of what people were promising, and that ushered in what became known as the AI winter. Research projects got defunded, startups died, All this excitement withered away. So by the nineteen eighties Terry was pretty much convinced that he'd reach the dead end, which I don't know you think would be this devastating realization for him. But right around this

time something else came along up until approximately three. If you use the computer, you were a technical nerd in the basement somewhere, and the fact you had to learn all sorts of arcane stuff was yeah, that's what we do. Uh. And then there was the computer for the rest of us. We can draw a picture or it can draw conclusions. It's a personal computer from Apple up and it's as easy to use as this Macintosh, the computer for the

rest of us. If you're a tech you probably have seen that ad at some point, right and Apple came out with mac and all of a sudden you had all these people who wanted to use computers who were not tech nerds, and we're not willing to learn all the arcane stuff, and so the whole field of how do you make them something that ordinary people can deal

with blossom. So Terry decided not to obsess over building computers that were going to truly think, and he wasn't going to worry about understanding how the brain really works either. John Lily, a former student of Terry's who's a partner at the venture capital firm Graylock Capital, described Terry's vision like this, machines can't do everything, but focusing on what

the machine capabild years is always a mistakes. He really want to pucus on the whole system, which includes humans, and they include humans and machines in the interviews between two machines is the key, and that philosophy helps shape the course of what was at the time a budding discipline called Human computer interaction h c I. Is all

about designing tools to help us humans use computers more easily. Yeah, taking these clunky machines out of research labs and putting them into the hands of you and me in Silicon Valley today, you hear this acronym h CI all the time. That's a very very strong view. The printed on me

for sure, like everybody looking out program massa others. Okay, so that's Marissa as in Marissa Meyer, Google employee number twenty and the CEO of Yahoo and read as in Reed Hoffman, a founding director of PayPal and the creator of LinkedIn. But the most famous of Terry students are

Google co founders Larry Page and Sergey Bryn. They met as graduate students in They started working on a way to organize all the different web pages out there, and Terry supervised their project that turned into the foundations of Google Search. I mean, something that helps us access the trillions of web pages of content. I can't really think of a single digital tool that's been more useful in

modern life. Thanks to Terry's guidance, Google was born as a company on September four and it's now the world's second most valuable company. Pretty cool stuff. Hey, I can calm down, calm down. We should point out that Terry wasn't always this business genius, passing down sage advice to his students. And I would say to Larry occasionally, well, yeah, that's great, but how you're going to make money with this?

I think what you should do is my advice is, you know, find a company like Microsoft or somebody who needs a search engine and sell it for a nice chuck of change. I always say, they're fortunate they took my technical advice, but not my business advice. So let's fast forward about two decades to today, Larry Page. It's still at the helm of Google, and one of the

company's biggest bets is its digital assistant. It's called Google Assistant, and it's connected to its new smartphone called Pixel, and it's also connected to its home device, which is like this portable speaker that speaks back to you, and that follows in the footsteps of all the other digital assistance out there. First of all, they're Sirie, which is the assistant on the iPhone. Hello therecky. There's the Amazon Echo, which is called Alexa. Hello, I'm here, And then there's

Microsoft's assistant, which is called Cortana. Hey there, my friend. We wanted to know if these new helpers are useful and smart, so you better to quiz them than Terry. Along with our editor Emily A. Busso we started sending up these devices on Terry's desk in his office. Hey, Alexa, are you on Hello I'm here? Okay, great, Alexa's working yea. So we have we have Amazon Echo Alexa. These are all on Professor Winograds desk in his office. Alexa has

just turned herself on you listening to me? Okay. So here's the first question. This is Terry asking Siri, where's a nightclub that my methodist uncle would enjoy? Okay, check it out? What is it? Show? Show some random nightclubs? Now, I have no idea if they have any you know, Holy holy Holy Town nightclub the grants, So it gave us nightclubs, but we're not sure something our Methodist uncle could have. Holy cow, do you think it probably understood today?

We may have been religion and holy that This is the problem with this kind of AI, which is there's no logical chain you can follow, but that may have somewhere in the workings have actually caused that to have a higher ranking than something else. Okay, so maybe a B minus per Syria here. The next one went to Microsoft Quartana, where is a nightclub my Methodist uncle would enjoy?

So okay, we got a bing basically the being searched with that entire phrase, and the top one is called I fell in love with my uncle who abused me from the age of that is very very experienced project. Oh no, yeah, what Why do you think they said that it did have being searched just took that whole phrase, so it had uncle. And why that comes up? Why

those particular keywords? Again, the same problem they I you have no logic that can tell you why that one came up somewhere in the sorting through all the millions of things that got higher rating. Wow, there must have been something in there with with Oh, that's terrible. Probably mentioned the nightclub somewhere in it. Yeah, probably that's probably what it is. That's that's exactly what I ah. Okay, let's put Kurtana away. Here's another question Terry came up

with and he tested it out on Google. If Maunaloa erupts, well I have to worry about the lava. Here here's what I found on the web. Now that one's not bad. So it found a web page from Hawaiian News called what could happen when malanal A erupts? So it didn't answer again, it didn't answer my question about here there's no way of doing that, But at least it got bunalow and erupt answer. I got an article about what could happen? You said, there's no way of doing that?

Oh no, given given the techniques they use. But will there ever be a way of doing that ever? Is a hard question. It will take a mixture of techniques of which the old Ai stuff has to be resurrected in a new form which combines with the new Ai stuff in a way that at this point I think nobody has a good grip on. So so let me

start off with you. I'm I'm a mechanist, I believe everything that goes on in my brain in yours is all because of electrons and chemical squirting around whatever, and therefore there's no reason that some physical device other than a brain can't do the same thing if it were probably constructed. Hey, Alexa, if Mauna loa erupts, will I have to worry about the lava here? Sorry, I can't find the answer to the question I heard. Okay, at least knowing you don't know the answer is better than

making up an answer. And since we visited Terry on the Friday before the election, we had to get him to ask this too. He asked, in the order of Kurtana, Google, and Sirie, who do you want to win the U s presidential election? I honestly can't tell if that's a trick question. I suspect want triggers. That's the trick question, is my guess? Not too bad? This is Google? Who do you want to win the US presidential elect that's

in the hands of informed citizens? Okay, so that one they somebody, And my guess is that's a human intervention where they there are enough people asking about the election that they put in a special thing. It's try to serious who do you want to win the election US presidential election. Election day is Tuesday, November eight, So it just triggered on the word election. I didn't pay attention to the rest of it. And after a couple more questions, we turned to Terry for his grand assessment and in

general and did were your with your caution proved? Yeah? I mean there's no none of these showed the kind of understanding of person would for the same question, and then even okay, even more than that. So think back to your shirty program. How far have these come? They've gone a different direction. So sure, look could have answered questions like that perfectly if they were about these few blocks on the tabletop and nothing else period, because it

was trying to do the logic. They have given up basically trying to do that, which is why they depend on things like search so much. Um and um. They've come a long way from a usefulness point of view. You sure toly was not very useful unless you were moving blocks on his tabletop. This can find you a restaurant, or it found me any club. Right now, it didn't really focus in on the ones I might have wanted, but at least let's start found me the web page

about mana loa effects. So from a pure usefulness point of view, um, I think they're doing some useful things as long as you don't depend on them too much. Okay, So it's been more than forty years since Terry created Shortloo and this is how far we've come, which I don't know, it doesn't sound like a whole lot. Yeah, we've we felt pretty deflated actually our own mini AI

winter right there in Terry's office. I'm interested in your view of the future, especially involving AI and the ability of of computers to to get more, more and more of arce and do maybe do things more themselves. Do these things make you feel confident in the future or just kind of blur or worried, I would say so. My view is that the kind of the advances and developments that are going on in AI are going to

have lots of very practical applications. You take a lot of medical cases and you can figure out a likely diagnosis for something. I think that's gonna happen. Now. The part where you're trying to deal with people and how they're thinking and what they're asking is probably on the on the hard and not as practical end compared to all of these things. Driving cars, right, they can drive cars. I think I believe that currently they could probably drive

as well as most people. And there's no sense of perfection in driving, right you're competing with human beings or um, you know, positioning, stay stations whatever. I mean, there's a zillion things which can be done better if you have a learning algorithm to help come up with the right parameters and all that kind of stuff. Um So I'm optimistic, and you know, if I were investing right, but I'm not. But you know, you could say there's gonna be a

lot there. Look for companies that are finding really useful niches, not ones to say we're going to solve the grand problem all at once. Um, well, the grand problem is can you have something which is indistinguishable from how people think? And it's sort of gone off that track in a way because most of the work that's being done in these kind of programs don't really try to think like people think. Everybody knows you do not think by having a trillion examples in your head and doing you know,

gigga flops of processing right to go through example. It's just done how it works. There's something else going on, and Terry here he's referring to the advances scientists have made and what's called machine learning. Instead of programming these explicit rules one by one, scientists have been able to do a lot of things by making computers and just millions of examples of the same thing, and then they use this really high powered form of statistics to learn

from those examples. And that's made it possible for us to get say, self driving cars and software to recognize cats on the internet. Yeah, that's a real breakthrough there. But for something like a machine that can solve all of our problems, it's going to take the kind of leap forward that Einstein made. It's not a matter of take what we have now and just keep chugging away. Now, when are those eenstein is gonna come along? Maybe one

of these in my class. Who knows. So, I guess the results of this very unscientific test that we conducted match Terry's vision all along. For the foreseeable future, computers are going to need us humans to help them with the nuances and the complexities of the real world. Although

Terry did leave us with one last warning. The systems, the smart the systems that run things and so on should And I'm putting that into show it as opposed to will, because it has to happen, be made to happen, involve the combined intelligence and wisdom of people and computers. The danger, I think is that people will put in computer systems without that check and then trust them. In the military angle is a big one. Theows this whole

question about robot drones. What if you put drones up in the air with weapons which we have and then say, okay, go kill bad guys. Right, Well, that shouldn't be without human in a loop. Right, there should be some sense of postility. But it's the easy thing to do from

a military point of view, right. And so I think that the danger when I see one, are the dangers of a I'm not worried about machines taking over and thinking better than when I'm worried about people putting dependencies on machines which do enough intelligence things that they can let them go off on their own. And among terry students who make up this next generation of innovators, this

key ingredient of morality really stuck with them too. We stopped by the office of another of Terry's former students an investor called Manu Kumar who is the founder of a seed fund called K nine. He definitely helped shape my worldview in terms of think, oh, how to be responsible as a as a scientist and a and a technologist, right um, And that plays a factor today, like there will be companies that I have passed on investing in just because I feel they're doing things that are morally

or or ethically questionable, right um. And like I've I've walked away from investing in companies which, like, technically a lot of what they're doing is possible and makes a lot of sense. But but if you're doing surveillance based on reading the mac address in your phone, right and then using that information for for doing retail intelligence as an example, right, Yes, I know it's technically possible, it can be done, but should it be done? And that's

it for this week's episode of Decrypted. Thanks for listening and tell us what have your experience has been with all the digital as systems out there. You can tweet at me at Akita seven and I'm at Alistair m Bar and if you're not a Twitter user, you can also write to our producer Pia, or even better, you can record a voice memo and send it to her

at pe ged Cary at bloomberg dot net. If you haven't already, please subscribe to our show on iTunes or wherever you get your podcasts, And while you're there, please take a moment to leave us a rating and a review. Yeah. I read each and every one of them, and they really help us get in front of more listeners. This episode was produced by Emily Busso, Pierre gat Cary, Liz Smith, and Magnus Hendrickson. Alec McCabe, his head of Bloomberg Podcasts.

That's it for the week's episode of Decrypted. Thanks for listening. We'll see you next week.