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?