In this episode, Byron and Rand discuss intelligence, AGI, consciousness and more.
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Byron Reese: This is “Voices in AI” brought to you by GigaOm, I’m Byron Reese. Today I’m excited our guest is Rand Hindi. He’s an entrepreneur and a data scientist. He’s also the founder and the CEO of Snips. They’re building an AI assistant that protects your privacy. He started coding when he was 10 years old, founded a social network at 14, founded a web agency at 15, and he showed interest in machine learning at 18, and began work on a Ph.D. in bioinformatics at age 21. He’s been elected by MIT Technology Reviewas one of their “35 Innovators Under 35,” and was a “30 Under 30” by Forbes in 2015, is a rising star by the Founders Forum, and he is a member of the French Digital Counsel. Welcome to the show, Rand.
Rand Hindi: Hi Byron. Thanks for having me.
That’s a lot of stuff in your bio. How did you get such an early start with all of this stuff?
Well, to be honest, I think, I don’t have any credit, right? My parents pushed me very young into technology. I used to hack around the house, dismantling everything from televisions, to radios, to try to figure out how these things were working. We had a computer at home when I was a kid and so, at some point, my mom came to me and gave me a coding book, and she’s like, “You should learn how to program the machines, instead of just figuring out how to break it, pretty much.” And from that day, just kept going. I mean you know it’s as if, I was telling you when you were 10, that here’s something that is amazing that you can use as a tool to do anything you ever had in mind.
And so, how old are you now? I would love to work backwards just a little bit.
I’m 32 today.
Okay, you mean you turned 32 today, or you happen to be 32 today?
I’m sorry, I am 32. My birthday is in January.
Okay. When did you first hear about artificial intelligence, and get interested in that?
So, after I started coding, you know I guess like everybody who starts coding as a teenager got interested in hacking security and these things. But when I went to university to study computer science, I was actually so bored because, obviously, I already knew quite a lot about programming that I wanted to take up a challenge, and so I started taking masters classes, and one of them was in artificial intelligence and machine learning. And the day I discovered that it was like, it was mind-blowing. It’s as if for the first time someone had shown me that I no longer had to program computers, I could just teach them what I want them to do. And this completely changed my perspective on computer science, and from that day I knew that my thing wasn’t going to be to code, it was to do AI.
So let’s start, let’s deconstruct artificial intelligence. What is intelligence?
Well, intelligence is the ability for a human to perform some task in a very autonomous way. Right, so the way that I…
But wait a second, to perform it in an autonomous way that would be akin to winding up a car and letting it just “Ka, ka, ka, ka, ka” across the floor. That’s autonomous. Is that intelligent?
Well, I mean of course you know, we’re not talking about things which are automated, but rather about the ability to make decisions by yourself, right? So, the ability to essentially adapt to the context you’re in, the ability to, you know, abstract what you’ve been learning and reuse it somewhere else—all of those different things are part of what makes us intelligent. And so, the way that I like to define artificial intelligence is really just as the ability to reproduce a human intelligent behavior in a machine.
So my cat food dish that when it runs out of cat food, and it can sense that there is no food in it, it opens a little door, and releases more food—that’s artificial intelligence?
Yep, I mean you can consider one form of AI, and I think it’s important to really distinguish what we currently have with narrow AI and strong AI
Sure, sure, we’ll get to that in due time. So where do you say we are when people say, “I hear a lot about artificial intelligence, what is the state of the art?” Are we kind of at the very beginning just doing the most rudimentary things? Or are we kind of like half-way along and we’re making stuff happen? How would you describe today’s state of the art?
What we’re really good at today is building and teaching machines to do one thing and to do it better than humans. But those machines are incapable of second-degree thinking, like we do as humans, for example. So, I think we’ve really have to think about this way: you’ve got a specific task for which you would traditionally have programmed a machine, right? And now you can essentially have a machine look at examples of that behavior, and reproduce it, and execute it better than a human would. This is really the state of the art. It’s not yet about intelligence in a human sense; it’s about a task-specific ability to execute something.
So I have posted an article recently on GigaOm where I have an Amazon Echo and a Google Assistant on my desk, and almost immediately I noticed that they would answer the same factual question differently. So, if I said, “How many minutes are in a year?” they gave me a different answer. If I said, “Who designed the American flag?” they gave me a different answer. And they did so because how many minutes in a year, one of them interpreted that as a solar year, and one of them interpreted that as a calendar year. And with regard to the flag, one of them gave the school answer of Betsy Ross, and one of them gave the answer to who designed the 50-state configuration of the stars. So, in both of those cases, would you say I asked a bad question that was inherently ambiguous? Or would you say the AI should have tried to disintermediate and figure it out, and that is an illustration of the limit you were just talking about?
Well I mean the question you’re really asking here is what would be ground truths that the AI should both have, and I don’t think there is. Because as you correctly said, the computer interpreted an ambiguous question in a different way., which is correct because there are two different answers depending on context. And I think this is also a key limitation of what we currently have with AI, is that you and I, we disambiguate what we’re saying because we have cultural references—we have contextual references to things that we share. And so, when I tell you something—I live in New York half the time—so if you ask me who created the flag, we’d both have the same answer because we live in the same country. But someone on a different side of the world might have a different answer, and it’s exactly the same thing with AI. Until we’re able to bake in contextual awareness, cultural awareness, or even things like, very simply, knowing what is the most common answer that people would give, we are going to have those kind of weird side effects that you just observed here.
So isn’t it, though, the case that all language is inherently ambiguous? I mean once you get out of the realm of what is two plus two, everything like, “Are you happy? What’s the weather like? Is that pretty?” [are] all like, anything you construct with language has inherent ambiguity, just by the nature of words.
And so how do you get around that?
As humans, the way that we get around that is that we actually have a sort of probabilistic model in our heads of how we should interpret something. And sometimes it’s actually funny because you know, I might say something and you’re going to take it wrong, not because I meant it wrong, but because you understood it in different context reference frame. But fortunately, what happens is that people who usually interact together usually share some sort of similar contextual reference points. And based on this it means we’re able to share in a very natural way without having to explain the logic behind everything we say. So, language in itself is very ambiguous. If I tell you something such as, “The football match yesterday was amazing,” this sentence grammatically and syntactically is very simple, but the meaning only makes sense if you and I were watching the same thing yesterday, right? And so, this is exactly why computers vary. It’s still unable to understand human language the same way we do is because it’s unable to understand this notion of context unless you give it to it. And I think this is going to be one of the most active fields of research. Natural language processing is going to be you know, basically, baking in contextual awareness into natural language understanding.
So you just said a minute ago at the beginning of that, that humans have a probabilistic model that they’re running in their head—is that really true though? Because if I ask somebody, I just come up to a stranger how many minutes are in a year, they’re not going to say well there is 82.7% chance he’s referring to a calendar year, but it’s a 17.3% he’s referring to a solar year. I mean they instantly only have one association with that question, most people, right?
And so they don’t actually have a probabilistic—are you saying it’s a de-facto one—
Talk to that for just a second.
I mean, how it’s actually encoded in the brain? I don’t know. But the fact is that depending on the way I ask the question, depending on the information I’m giving you about how you should think about the question, you’re going to think about a different answer. So, if I tell you, you know how many stars are—let’s say, “How many minutes are in the year? If I ask you the question like this, this is the most common way of asking the question, which means that you know I’m expecting you to give me the most common answer to the question. But if I give you more information, if I told you, “How many minutes are in a solar year?” So now I’ve specified extra information, then that will change the answer you’re going to give me, because now the probability is no longer that I’m asking for the general question, but rather, I’m asking you for a very specific one. And so you have this sort of like, all these connections built into your brain, and depending on which of those elements are activated, you’re going to be giving me a different response. So, think about it as like, you have this kind of graph of knowledge in your head, and whenever I’m asking something, you’re going to give me a response by picking the most likely answer.
So this is building up to—well, let me ask you one more question about language, and we’ll start to move past this a little bit, but I think this is fascinating. So, the question is often raised, “Are there other intelligent creatures on Earth?” You know the other sorts of animals and what not. And one school of thought says that language is an actual requirement for intelligence. That without language, you can’t actually conceive of abstract ideas in your head, you can’t do any of that, and therefore anything that doesn’t have language doesn’t have intelligence. Do you agree with that?
I guess if you’re talking about general intelligence, yes. Because language is really just a universal interface for, you know, representing things. This is the beauty of language. You and I speak English, and we don’t have to learn a specific language for every topic we want to talk about. What we can do instead is we can use the sync from the mental interface, the language, to express all kinds of different ideas. And so, the flexibility of natural language means that you’re able to think about a lot more different things. And so this, inherently, I believe, means that it opens up the amount of things you can figure out—and hence, intelligence. I mean it makes a lot of sense. To be honest, I’ve never thought about it exactly like this, but when you think about it, if you have a very limited interface to express things, you’re never going to be able to think about that many things.
So Alan Turing famously made the Turing Test, which he said that if you are on a terminal, you’re in a conversation with something in another room and you can’t tell if its person or a machine—interestingly he said 30% of the time a machine can fool you—then we have to say the machine is thinking.Do you interpret that as language “indicates that it is thinking,” or language is “it is actually thinking”?
I was talking about this recently actually. Just because a machine can generate an answer that looks human, doesn’t mean that the machine actually understands the answer given. I think you know the depth of understanding of the semantics, and the context goes beyond the ability to generate something that makes sense to a human. So, it really depends on what you’re asking the machine. If you’re asking something trivial, such as, you know, how many days are in a year, or whatever, then of course, I’m sure the machine can generate a very simple, well-structured answer that would be exactly like a human would. But if you start digging in further, if you start having a conversation, if you start essentially, you know, brainstorming with the machines, if you start asking for analysis of something, then this is where it’s going to start failing, because the answers it’s going to give you won’t have context, it won’t have abstraction, it won’t have all of these other things which makes us really human. And so I think, you know, it’s very, very hard to determine where you should draw the line. Is it about the ability to write letters in a way that is syntactically, grammatically correct? Or is it the ability to actually have an intelligent conversation, like a human would? I think the former, we can definitely do in the near future. The latter will require AGI, and I don’t think we’re there yet.
So you used the word “understanding,” and that of course immediately calls up the Chinese Room Problem, put forth by John Searle. For the benefit of the listener, it goes like this: There’s a man who’s in a room, and it’s full of these many thousands of these very special books. The man doesn’t speak any Chinese, that’s the important thing to know. People slide questions in Chinese underneath the door, he picks them out, and he has this kind of algorithm. He looks at the first symbol; he finds a matching symbol on the spine of one of the books. He looks up the second book, that takes him to a third book, a fourth book, a fifth book, all the way up. So he gets to a book that he knows to copy some certain symbols from and he doesn’t know what they mean, he slides it back under the door, and the punch line is, it’s a perfect answer, in Chinese. You know it’s profound, and witty, and well-written and all of that. So, the question that Searle posed and answered in the negative is, does the man understand Chinese? And of course, the analogy is that that’s all a computer can do, and therefore a computer just runs this deterministic program, and it can never, therefore, understand anything. It doesn’t understand anything. Do you think computers can understand things? Well let’s just take the Chinese Room, does the man understand Chinese?
No, he doesn’t. I think actually this is a very, very good example. I think it’s a very good way to put it actually. Because what the person has done in that case, to give a response in Chinese, he literally learns an algorithm on the fly to give him an answer. This is exactly how machine learning currently works. Machine learning isn’t about understanding what’s going on; it’s about replicating what other people have done, which is a fundamental difference. It’s subtle, but it’s fundamental because to be able to understand you need to be able to also replicate de-facto, right? Because if you can understand, you replicate. But being able to replicate, doesn’t mean that you’re able to understand. And the way that we build those machine learning models today are not meant to have a deep understanding of what’s going on. It’s meant to have a very appropriate, human, understandable response. I think this is exactly what happens in this thought experiment. It’s exactly the same thing pretty much.
Without going into general intelligence, I think what we really have to think about today, the way I’d like to see this is, machine learning is not about building human-like intelligence yet. It’s about replacing the need to program a computer to perform a task. Up until now, when you wanted to make a computer do something, what you had to do first is understand what the phenomenon is yourself. So, you had to become an expert in whatever you were trying to automate, and then you would write a computer code with those rules. And so the problem is that doing this would take you a while, because a human would have to understand what’s going on, which can take a while. And also your problem, of course, is not everything is understandable by humans, at least not easily. Machine learning completely replaces the need to become an expert. So instead of understanding what’s going on and then programming the machine, you’re just collecting examples of what’s going on, and feeding it to the machine, who will then figure out a way to reproduce that. So, you know the simple example is, show me a pattern of numbers with written five times five, and ask me what is a pattern, I’ll learn that it’s five, if that makes sense. So this is really about this—this is really about getting rid of the need to understand what you’re trying to make the machine do and just give it examples that it can just figure out by itself.
So we began with my wind-up car, then the cat food dish, and we’re working up to understanding…eventually we have to get to consciousness because consciousness is this thing, people say we don’t know what it is. But we know exactly what it is, we just don’t know how it comes about. So, what it is, is that we experience the world. We can taste the pineapple or see the redness of the sunset in a way that’s different than just sensing the world…we experience. Two questions: do you have any personal theory on where consciousness comes from, and second, is consciousness key to understanding, and therefore key to an AGI?
I think so. I think there is no question that consciousness is linked to general intelligence because general intelligence means that you need to able to create an abstraction of the world, which means that you need to be able to go beyond observing it, but also be able to understand it and to experience it. So, I think that is a very simple way to put it. What I’m actually wondering is whether consciousness was a consequence of biology and whether we need to replicate that in a machine, to make it intelligent like a human being is intelligent. So essentially, the way I’m thinking about this is, is there a way to build a human intelligence that would seem human? And do we want that to seem human? Because if it’s just about reproducing the way intelligence works in a machine, then we shouldn’t care if it feels human or not, we should just care about the ability for the machine to do something smart. So, I think the question of consciousness in a machine is really down to the question of whether or not we want to make it human. There are many technologies that we’ve built for which we have examples in nature, which perform the same task, but don’t work the same. Birds and planes, for example, I’m pretty sure a bird needs to have some sort of like, consciousness of itself of not getting into the wall, whereas we didn’t need to replicate all those tiny bits for the actual plane to fly. It’s just a very different way of doing things.
So do you have a theory as to how it is that we’re conscious?
Well, I think it probably comes from the fact that we had to evolve as a species with other individuals, right? How would you actually understand where to position yourself in society, and therefore, how to best build a very coherent, stable, strong community, if you don’t have consciousness of other people, of nature, of yourself? So, I think there is like, inherently, the fact that having a kind of ecosystem of human beings, and humans in nature, and humans and animals meant that you had to develop consciousness. I think it was probably part of a very positive evolutionary strategy. Whether or not that comes from your neurons or whether that comes more from a combination of different things, including your senses, I’m not sure. But I feel that the need for consciousness definitely came from the need for integrating yourself into broader structure.
And so not to put words in your mouth, but it sounds like you think, you said “we’re not close to it,” but it is possible to build an AGI, and it sounds like you think it’s possible to build, hypothetically, a conscious computer and you’re asking the question of would we want to?
Yes. The question is whether or not it would make sense for whatever we have in mind for it. I think probably we should do it. We should try to do it just for the science, I’m just not sure this is going to be the most useful thing to do, or whether we’re going to figure out an even more general general-intelligence which doesn’t have only human traits but has something even more than this, that would be a lot more powerful.
Hmmm, what would that look like?
Well, that is a good question. I have clearly no idea because otherwise—it is very hard to think about a bigger intelligence and the intelligence that we are limited to, in a sense. But it’s very possible that we might end up concluding that well you know, human intelligence is great for being a human, but maybe a machine doesn’t have to have the same constraints. Maybe a machine can have like a different type of intelligence, which would make it a lot better suited for the type of things we’re expecting the machine to do. And I don’t think we’re expecting the machines to be human. I think we’re expecting the machines to augment us, to help us, to solve problems humans cannot solve. So why limit it to a human intelligence?
So, the people I talk to say, “When will we get an AGI?” The predictions vary by two orders of magnitude—you can read everything from 5 to 500 years. Where do you come down on that? You’ve made several comments that you don’t think we’re close to it. When do you think we’ll see an AGI? Will you live to see an AGI, for instance?
This is very, very hard to tell, you know I mean there is this funny artifact that everybody makes a prediction 20 years in the future, and it’s actually because most people when they make those predictions, have about 20 years left in their careers. So, you know, nobody is able to think beyond their own lifetime, in a sense. I don’t think it’s 20 years away, at least not in the sense of real human intelligence. Are we going to be able replicate parts of AGI, such as, you know, the ability to transfer learning from one task to another? Yes, and I think this is short-term. Are we going to be able to build machines that can go one level of abstraction higher to do something? Yes, probably. But it doesn’t mean they’re going to be as versatile, as generalist, as horizontally thinking as we are as humans. I think for that, we really, really have to figure out once and for all whether a human intelligence requires a human experience of the world, which means the same senses, the same rules, the same constraints, the same energy, the same speed of thinking, or not. So, we might just bypass, as I said—human intelligence might go from like narrow AI, to a different type of intelligence, that is neither human or narrow. It’s just different.
So you mentioned transferred learning. I could show you a small statue of a falcon, and then I could show you a hundred photographs, and some of them have the falcon under water, on its side, in different light, upside down, and all these other things. Humans have no problem saying, “there it is, there it is, there it is,” you know just kind of find Waldo [but] with the falcon. So, in other words, humans can train with a sample size of one, primarily because we have a lot of experience seeing other things in lowlight and all of that. So, if that’s transferred learning it sounds like you think that we’re going to be able to do that pretty quickly, and that’s kind of big deal if we can really teach machines to generalize the way we do. Or is that kind of generalization that I just went through, that actually is part of our general intelligence at work?
I think transferred learning is necessary to build AGI, but it’s not enough, because at the end of the day, just because a machine can learn to play a game and then you know have a starting point to play another game, doesn’t mean that it will make the choice to learn this other game. It will still be you telling it, “Okay, here is a task I need you to do, use your existing learning to perform it.” It’s still pretty much task-driven, and this is a fundamental difference. It is extremely impressive and to be honest I think it’s absolutely necessary because right now when you look at what you do with machine learning, you need to collect a bunch of different examples, and you’re feeding that to the machine, and the machine is learning from those examples to reproduce that behavior, right? When you do transferred learning, you’re still teaching a lot of things to the machine, but you’re teaching it to reuse other things so that it doesn’t need as much data. So, I think inherently the biggest benefit of transferred learning will be that we won’t need to collect as much data to make the computers do something new. It solves, essentially, the biggest friction point we have today, which is how do you access enough data to make the machine learn the behavior? In some cases, the data does not exist. And so I think transferred learning is a very elegant and very good solution to that problem.
So last question I want to ask you about AGI and then we can turn the clock back and talk to issues closer at hand is as follows: It sounds like you’re saying an AGI is more than 20 years off, if I just inferred that from what you just said. And I am curious because the human genome is 2 billion base pairs, it’s something like 700 MB of information, most of which we share with plants, bananas, and what-not. And if you look at our intelligence versus a chimp, or something, we only have a fraction of 1% of the DNA that is different. What that seems to suggest to me at least is that if the genome is 700 MB, and the 1% difference gives us an AGI, then the code to create an AGI could be a small as 7 MB.
Pedro Domingos wrote a book called The Master Algorithm, where he says that there probably is an algorithm, that can solve a whole world of problems, and get us really close to AGI. Then other people on another end of the spectrum, like Marvin Minsky or somebody, don’t even know that we have an AGI, that we’re like just 200 different hacks—kind of 200 narrow intelligences that just kind of pull off this trick of seeming like a general intelligence. I’m wondering if you think that an AGI could be relatively simple—that it’s not a matter of more data or more processing, but just a better algorithm?
So just to be clear, I don’t consider a machine who can perform 200 different tasks to be an AGI. It’s just like an ensemble of, you know, narrow AIs.
Right, and that school of thought says that therefore we are not an AGI. We only have this really limited set of things we can do that we like to pass off as “ah, we can do anything,” but we really can’t. We’re 200 narrow AIs, and the minute you ask us to do things outside of that, they’re off our radar entirely.
For me, the simplest definition of how to differentiate between a narrow AI and an AGI is, an AGI is capable of kind of zooming out of what it knows—so to have basically like a second-degree view of the facts that it learned, and then reuse that to do something completely different. And I think this capacity we have as humans. We did not have to learn every possible permutation; we did not have to learn every single zooming out of every fact in the world, to be able to do new things. So, I think I definitely agree that as a human, we are AGI. I just don’t think that having a computer who can learn to do two hundred different things would do that. You would still need to figure out this ability to zoom out, this ability to create abstraction of what you’ve been learning and to reapply it somewhere else. I think this is really the definition of horizontal thinking, right? You can only think horizontally if you’re looking up, rather than staying in a silo. So, to your question, yea. I mean, why not? Maybe the algorithm for AGI is simple. I mean think about it. Deep learning, machine learning in general, these are deceptively easy in terms of mathematics. We don’t really understand how it works yet, but the mathematics behind it is very, very, easy. So, we did not have to come up with this like crazy solution. We just came up with an algorithm that turned out to be simple, and that worked really well when given a ton of information. So, I’m pretty sure that AGI doesn’t have to be that much more complicated, right? It might be one of those E = mc2sort of plugins I think that we’re going to figure out.
That was certainly the hope, way back, because physics itself obeys such simple laws that were hidden from us, and then once elucidated seemed, any 11th gradehigh-school student could learn, maybe so. So, pulling back more toward the here and now—in ’97, Deep Blue beat Kasparov, then after that we had Ken Jennings lose in Jeopardy, then you had AlphaGo beat Lee Sedol, then you had some top-ranked poker players beaten, and then you just had another AlphaGo victory. So, AI does really well at games presumably because they have a very defined, narrow rule set, and a constrained environment. What do you think is going to be, kind of, the next thing like that? It hits the papers and everybody’s like, “Wow, that’s a big milestone! That’s really cool. Didn’t see that coming so soon!” What do you think will be the next sort of things we’ll see?
So, games are always a good example because everybody knows the game, so everybody is like, “Oh wow, this is crazy.” So, putting aside I guess the sort of PR and buzz factor, I think we’re going to solve things like medical diagnosis. We’re going to solve things like understanding voice very, very soon. Like, I think we’re going to get to a point very soon, for example, where somebody is going to be calling you on the phone and it’s going to be very hard for you to distinguish whether it’s a human or a computer talking. Like I think this is definitely short-term as in less than 10years in the future, which poses a lot of very interesting questions, you know, around authentication, privacy, and so forth. But I think the whole realm of natural language is something that people always look at as a failure of AI—“Oh it’s a cute robot, it barely actually knows how to speak, it has a really funny sounding voice.” This is typically the kind of thing that nobody thinks, right now, a computer can do eloquently, but I’m pretty sure we’re going to get there fairly soon.
But to our point earlier, the computer understanding the words, “Who designed the American flag?” is different than the computer understanding the nuance of the question. It sounds like you’re saying we’re going to do the first, and not the second very quickly.
Yes, correct. I think like somewhere the computer will need to have a knowledge base of how to answer, and I’m sure that we’re going to figure out which answer is the most common. So, you’re going to have this sort of like graph of knowledge that is going to be baked into those assistants that people are going to be interacting with. I think from a human perspective, what is going to be very different, is that your experience of interacting with a machine will become a lot more seamless, just like a human. Nobody today believes that when someone calls them on the phone, it’s a computer. I think this is like a fundamental thing that nobody is seeing coming really but is going to shift very soon. I can feel there is something happening around voice which is making it very, very, very…which is going to make it very ubiquitous in the near future, and therefore indistinguishable from a human perspective.
I’m already getting those calls frankly. I get these calls, and I go “Hello,” and it’s like, “Hey, this is Susan, can you hear me okay?” and I’m supposed to say, “Yes, Susan.” Then Susan says, “Oh good, by the way, I just wanted to follow up on that letter I sent you,” and we have those now. But that’s not really a watershed event. That’s not, you wake up one day and the world’s changed the way it has when they say, there was this game that we thought computers wouldn’t be able to do for so long, and they just did it, and it definitively happened. It sounds like the way you’re phrasing it—that we’re going to master voice in that way—it sounds like you say we’re going to have a machine that passes the Turing Test.
I think we’re going to have a machine that will pass the Turing Test, for simple tasks. Not for having a conversation like we’re having right now. But a machine that passes the Turing Test in, let’s say, a limited domain? I’m pretty sure we’re going to get there fairly soon.
Well anybody who has listened to other episodes of this, knows my favorite question for those systems that, so far, I’ve never found one that could answer, and so my first question is always “What’s bigger a nickel or the sun?” and they can’t even right now do that. The sun could be s-u-nor s-o-n, a nickel is a metal as well as a unit of currency, and so forth. So, it feels like we’re a long way away, to me.
But this is exactly what we’ve been talking about earlier; this is because currently those assistants are lacking context. So, there’s two parts of it, right? There’s the part which is about understanding and speaking, so understanding a human talking and speaking in a way that a human wouldn’t realize it’s a computer speaking, this is more like the voice side. And then there is the understanding side. Now you add some words, and you want to be able to give a response that is appropriate. And right now that response is based on a syntactic and grammatical analysis of the sentence and is lacking context. But if you plug it into a database of knowledge, that it can tap into—just like a human does by the way—then the answers it can provide you will be more and more intelligent. It will still not be able to think, but it will be able to give you the correct answers because it will have the same contextual references you do.
It’s interesting because, at the beginning of the call, I noted about the Turing Test that Turing only puta 30% benchmark. He said if the machine gets picked 30% of the time, we have to say its thinking. And I think he said 30% because the question isn’t, “Can it think as well as a human,” but “Can it think?” The really interesting milestone in my mind is when it hits 51%, 52%, of the time, and that would imply that it’s better at being human than we are, or at least it’s better at seeming human than we are.
Yes, so again it really depends on how you’re designing the test. I think a computer would fail 100% of the time if you’re trying to brainstorm with it, but it might win 100% of the time if you’re asking it to give you an answer to a question.
So there’s a lot of fear wrapped up in artificial intelligence and it’s in two buckets. One is the Hollywood fear of “killer robots,” and all of that, but the much more here and now, the one that dominates the debate and discussion is the effect that artificial intelligence, and therefore automation, will have on jobs. And this you know there are three broad schools of thought, one is that there is a certain group of people that are going to be unable to compete with these machines and will be permanently unemployed, lacking skills to add economic value. The second theory says that’s actually that’s what’s going to happen to all of us, that there is nothing in theory a machine can’t do, that a human can do. And then a final school of thought that says we have 250 years of empirical data of people using transformative technologies, like electricity, just to augment their own productivity and increase their productivity, and therefore their standard of living. You’ve said a couple of times, you’ve alluded to machines working with humans—AIs working with humans—but I want to give you a blank slate to answer that question. Which of those three schools of thought are you most closely aligned to and why?
I’m 100% convinced that we have to be thinking human plus machines, and there are many reasons for this. So just for the record, it turns out I actually know quite a bit about that topic because I was asked by the French government, a few months ago, to work on their AI strategy for employment. The country, the government wanted to know, “What should we do? Is this going to be disruptive?” So, the answer, the short answer is, every country will be impacted in a different way because countries don’t have the same relationship to automation based on how people work, and what they are doing essentially. For France in particular, which is what I can talk about here, what we ended up realizing is that machines…the first thing which is important to keep in mind is we’re talking the next ten years. So, the government does not care about AGI. Like, we’ll never get to AGI if we can’t fix the short-term issues that, you know, narrow intelligence is already bringing on the table. The point is, if you destroy society because of narrow AI, you’re never going to get to AGI anyway, so why think about it? So, we really focused on thinking on the next 10years and what we should do with narrow AI. The first thing we realized that is narrow intelligence, narrow AI, is much better than humans at performing whatever it has learned to do, but humans are much more resilient to edge cases and to things which are not very obvious because we are able to do horizontal thinking. So, the best combination you can have in any system will always be human plus machine. Human plus machine is strictly better in every single scenario, to human-alone or machine-alone. So if you wanted to really pick an order, I would say human plus machine is the best solution that you can get, then human and machine are just not going to be good at the same things. They’re going to be different things. There’s no one is better than the other, it’s just different. And so we designed a framework to figure out which jobs are going to be completely replaced by machines, which ones are going to be complimentary between human and AI, and which ones will be pure human. And so those criteria that we have in the framework are very simple.
The first one is, do we actually have the technology or the data to build such an AI? Sometimes you might want to automate something, the data does not exist, the censors to collect data does not exist, there are many examples of that. The second thing is, does that task that you want to automate require a very complicated manual intervention? It turns out that robotics is not following the same experimental trends as AI, and so if your job is mostly consisting of using your hands to do very complicated things, it’s very hard to build an intelligence that can replicate that. The third thing is, very simply, whether or not we require general intelligence to solve a specific task? Are you more of a system designer thinking about the global picture of something, or are you very, very focused narrow task worker? So, the more horizontal your job is, obviously, the safer it is. Because until we get AGI, computers will never be able to end this horizontal thinking.
The last two are quite interesting too. The first one is, do we actually want—is it socially acceptable to automate a task? Just because you can automate something, doesn’t mean that this is what we will want to do. You know, for instance, you could get a computer to diagnose that you have cancer, and just email you the news, but do we want that? Or don’t we prefer that at least a human gives us that news? The second good example about it, which is quite funny, is the soccer referee. Soccer in Europe is very big, not as much in the U.S., but in Europe it’s very big, and we already have technology today that could just look at the video screen and do real-time refereeing. It would apply the rules of the game, it would say “Here’s a foul, here’s whatever,” but the problem is that people don’t want that, because it turns out that a human referee makes a judgment on the fly based on other factors that he understands because he’s human such as, “Is it a good time to let people play? Because if I stop it here, it will just make the game boring.” So, it turns out that if we automated the referee of a soccer match, the game would be extremely boring, and nobody would watch it. So nobody wants that to be automated. And then finally, the final criteria is the importance of emotional intelligence in your job. If you’re a manager, your job is to connect emotionally with your team and make sure everything is going well. And so I think a very simple way to think about it is, if your job is mostly soft skills, a machine will not be able to do it in your place. If your job is mostly hard skill, there is a chance that we can automate that.
So, when you take those five criteria, right, and you look at distribution of jobs in France, what you realize is that only about 10% of those jobs will be completely automated, another 30%, 40% won’t change, because it will still be mostly done by human, and about 50% of those jobs will be transformed. The 10% of jobs the machines will take, you’ve got 40% of jobs that humans will take, and you’ve got 50% of jobs, which will change because it will become a combination of humans and machines doing the job. And so the conclusion is that, if you’re trying to anticipate the impact of AI on the French job market and economy, we shouldn’t be thinking about how to solve mass unemployment with half the population not working; rather, we should figure out how to help those 50% of people transition to this AI+human way of working. And so it’s all about continuous education. It’s all about breaking this idea that you like learn one thing for the rest of your life. It’s about getting into a much more fluid, flexible sort of work life where humans focus on what they are good at and working alongside the machines, who are doing things that machines are good at. So, the recommendation we gave to the government is, figure out the best way to make humans and machines collaborate, and educate people to work with machines.
There’s a couple of pieces of legislation that we’ve read about in Europe that I would love to get your thoughts on, or proposed legislation, to be clear. One of them is treating robots or certain agents of automation as legal persons so that they can be taxed at a similar rate as you would tax a worker. I guess the idea being that, why should humans be the only ones paying taxes? Why shouldn’t the automation, the robots, or the artificial intelligences, pay taxes as well? Practically, what do you think? Two, what do you think should be the case? What will happen and what should happen?
So, for taxing robots, I think that it’s a stupid idea for a very simple reason, is that how do you define what a machine is, right? It’s easy when you’re talking about an assembly line with a physical machine because you can touch it. But how many machines are in an image recognition app? How do you define that? And so what the conclusion is, if you’re trying to tax machines, like you would tax humans for labor, then you’re going to end up not being able to actually define what is a machine. Therefore, you’re not going to actually tax the machine, but you’re going to have to figure out more of a meta way of taxing the impact of machines—which basically means that you’re going to increase the corporate taxes, like the profit tax, that companies are making as a kind of catch-all for what you’re doing. So, if you’re doing this, you’re impeding your investment and innovation, and you’re actually removing the incentive to do that. So I think that it makes no sense whatsoever to try to tax robots because the net consequence is that you’re just going to increase the taxes that companies have to pay overall.
And then the second one is the idea that, more and more algorithms, more and more AIs help us make choices. Sometimes they make choices for us—what will I see, what will I read, what will I do? There seems to be a movement to legislatively require total transparency so that you can say “Why did it recommend this?” and a person would need to explain why the AI made this recommendation. One, is that a good idea, and two, is it even possible at some level?
Well this [was] actually voted [upon] last year and it comes into effect next year as part of a bigger privacy regulation called GDPR, that applies to any company that wants to do business with a European citizen. So, whether you’re American, Chinese, French, it doesn’t matter, you’re going to have to do that. And in effect, one of the things that this regulation poses, is that any automated treatment that results in a significant impact on your life—a medical diagnosis, an insurance pricing whatever, like an employment or like a promotion you get—you have to be able to explain how the algorithm made that choice. By the way, this law [has] existed in France already since 1978, so it’s new in Europe, but it has been existing in France for 40 years already. The reason why they put this is very simple, is because they want to avoid people being excluded because a machine learned a bias in the population, and that person essentially not being able to go to court and say, “There’s a bias, I was unfairly treated.”
So essentially the reason why they want transparency, is because they want to have accountability against potential biases that might be introduced, which I think makes a lot of sense, to be honest. And that poses a lot of questions, of course, of what do you consider an algorithm that has an impact on your life? Is your Facebook newsfeed impacting your life? You could argue it does, because the choice of news that you see will change your influence, and Facebook knows that. They’ve experimented with that. Does a search result in Google have an impact on your life? Yes it does, because it limits the scope of what you’re seeing. My feeling is that, when you keep pushing this, what you’re going to end up realizing is that a lot of the systems that exist today will not be able to rely on this black-box machine learning model, but rather would have to use other types of methods. And so one field of study, which is very exciting, is actually making deep learning understandable, for precisely that reason.
Which it sounds like you’re in favor of, but you also think that that will be an increasing trend, over time.
Yeah, I mean I believe that actually what’s happening in Europe is going to permeate to a lot of the other places in the world. The right to privacy, the right to be forgotten, the right to have transparent algorithms when they’re important, the right to transferability of your personal data, that’s another very important one. This same regulation means that all my data I have with a provider, I can tell that provider, to send it to another provider, in a way that the other provider can use it. Just like when you change carriers, you can switch phone number without worrying about how this works, this will now apply to every single piece of personal data companies have around you when you’re a European citizen.
So, this is huge, right? Because think about it, what this means is if you have a very key algorithm for making a decision, you now have to publish and make that algorithm transparent. What that means is that someone else could replicate this algorithm in the exact same way you’re doing it. This, plus the transferability of personal data means that you could have two exactly equivalent services which have the same data about you, that you could use. So that completely breaks any technological monopoly[on] important things for your life. And so I think this is very, very interesting because the impact that this will have on AI is huge. People are racing to get the best AI algorithm and the best data. But at the end of the day—if I can copy your algorithm because it’s an important thing for my life, and it has to be transparent, and if I can transfer my data from you to another provider—you don’t have as much of a competitive advantage anymore.
But doesn’t that mean, therefore, you don’t have any incentive to invest in it? If you’re basically legislating all sorts…[if] all code is open-sourced, then why would anybody spend any money investing in something that they get no benefit whatsoever from?
Innovation. User experience. Like monopoly is the worst thing that could happen for innovation and for people, right?
Is that truly necessarily? I mean patents are a form of monopoly, right? We let drug companies have a monopoly on some drug for some period of time because they need some economic incentive to invest in it. All of law is built around monopoly, in one form or the other, based on the idea of patents. If you’re saying there’s an entire area that’s worth trillions of dollars, but we’re not going to let anybody profit off of it—because anything you do you have to share with everybody else—aren’t you just destroying innovation?
That transparency doesn’t prevent you from protecting your IP, right?
What’s the difference between the IP and the algorithm?
So, you can still patent the system you created, and by the way, when you patent a system, you make it transparent as well because anybody can read the patent. So, if anything I don’t that changes the protection over time. I think what that fundamentally changes is that you’re no longer going to be limited to a black-box approach that you’re not going to be able to have visibility on. I think the Europeans want the market to become a lot more open, they want people to have choices, and they want people to be able to say no to a company that they don’t share the values of the company, and they don’t like the way they’re being treated.
So obviously privacy is something near and dear to your heart. Snips is an AI assistant designed to protect privacy. Can you tell us what you’re trying to do there, and how far along you are?
So when we started the company in 2013, we did it as a research lab in AI, and one of the first things we focused on was this intersection between AI and privacy. How do you guarantee privacy in the way that you’re building those AIs? And so that eventually led us to what we’re currently doing now, which is we’re selling a voice platform for connected devices. So, if you’re building a car and you want people to talk to it, you can use our technology to do that, but we’re doing it in a way that all the data of the user, its voice, its personal data never leaves the device that the user has interacted with. So, you know whereas Alexa and Siri and Google Assistant are running in the cloud, we’re actually running completely on the device itself. There is not a single piece of your personal data that goes to a server. And this is important because voice is biometric, voice is something that identifies you uniquely that you cannot change, it’s not like a cookie in a browser, it’s more like a fingerprint. When you send biometric data to the cloud, you’re exposing yourself to having your voice copied, potentially, down the line, and you’re increasing your risk that someone might break into one of those servers and essentially pretend to be a million people on the phone, with their banks, their kids, whatever. So, I think for us, like, privacy is extremely important as a part of the game, and by the way, doing things on device means that we can guarantee privacy by design, which also means that we are currently the only technology on the planet that is 100% compliant with those new European regulations. Everybody else is in a gray area right now.
And so where are you in your lifecycle of your product?
We’ve been actually building this for quite some time; we had quite a bunch of clients use it. We officially launched it a few weeks ago, and the launch was really amazing. We even have a web version that people can use to build prototypes for Raspberry Pi. So, our technology, by the way, can run completely on a Raspberry Pi. So we do everything from speech recognition to natural language understanding on that actual Raspberry Pi, and we’ve had over a thousand people start building assistants on it. I mean it was really, really crazy. So, it’s a very, very mature technology, we benchmarked it against Alexa, against Google Assistant, against every other technology provider out there for voice, and we’ve actually gotten better performances than they did. So we have a technology that can run on a Raspberry Pi, or any other small device, that guarantees privacy by design, that is compliant with the new European regulation, and that performs better than everything that’s out there. This is important, because, you know there is this false dichotomy that you have to trade off AI and privacy, but this is wrong, this is actually not true at all. You can really have the two together.
Final question, do you watch or read, or consume any science fiction, and if so, do you know any views of the future that you think are kind of in alignment with yours or anything you look at and say “Yes, that’s what could happen!”
I think there are bits and pieces in many science fiction books, and actually this is the reason why I’m thinking about writing one myself now.
All right, well Rand this has been fantastic. If people want to keep up with you, and follow all of the things you’re doing and will do, can you throw out some URLs, some Twitter handles, whatever it is people can use to keep an eye on you?
Well, the best way to follow me I guess would be on Twitter, so my handle is RandHindi, and on Medium, my handle is RandHindi. So, I blog quite a bit about AI and privacy, and I’m going to be announcing quite a few things and giving quite a few ideas in the next few months.
All right, well this has been a far-reaching and fantastic hour. I want to thank you so much for taking the time, Rand.
Thank you very much. It was a pleasure.
Byron explores issues around artificial intelligence and conscious computers in his upcoming book The Fourth Age, to be published in April by Atria, an imprint of Simon & Schuster. Pre-order a copy here.