Voices in AI – Episode 41: A Conversation with Rand Hindi


Today’s leading minds talk AI with host Byron Reese

In this episode, Byron and Rand discuss intelligence, AGI, consciousness and more.

Today’s leading minds talk AI with host Byron Reese

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.

Correct.

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?

Of course.

And so they don’t actually have a probabilistic—are you saying it’s a de-facto one—

Exactly.

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…

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Peter Bordes

Exec Chairman & Founder at oneQube
Exec Chairman & Founder of oneQube the leading audience development automation platfrom. Entrepreneur, top 100 most influential angel investors in social media who loves digital innovation, social media marketing. Adventure travel and fishing junkie.
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