The Exchanges

Q And what did computers use to do that?

A So for that, it's, it's basically what caused the emergence of what we could call classical computer science. Okay. You write a program and that program basically internally searches for a solution and has some way of checking whether the solution it, it proposes is good or not. Um, Uh, people had a name for this in the city. They call this, uh, heuristic programming, because you can't, you can never exhaustively search all solutions for a good one, because the number of solutions is ridiculously large. You know, at chess, for example, right, you, you can play a certain number of moves, but then for every move that you play, your opponent can play a certain number of moves, and then for every of those moves, you can play a certain number of moves. So you get this exponential explosion of the number of possible Trajectories, basically, or sequences of moves. And, uh, you cannot possibly explore all of them until the end of the game to figure out which move to, uh, to play first. So, so you have to use what, you know, what's called heuristics to, to basically not search the entire, uh, graph or tree of possibilities.

AI assessment note: “They call this, uh, heuristic programming, because you can't, you can never exhaustively search”

Q Can you arrive at the answer which I removed and I'm telling you that this was the answer?

A Right. But you can only use the thing that you can see. So you don't see the answer on the input. You have to predict it. But I'm telling you when, during training, I tell you what it is. And so the system can adjust its parameter to its parameters in a Supervised fashion. So the only difference, the difference is not in the algorithms themselves. It's basically supervised learning, but it's in the structure of the system and the way the data is, uh, is used and produced. You don't need to basically have, uh, you know, someone going through millions of images and telling you, uh, this is a cat or a dog at a table. Um, you just show an image of a dog, a cat, or a table, and you Corrupt it, partially change it, change the colors maybe, or something, um, and then ask the system to recover the original one from the corrupted one. So that's, that's, uh, one particular form of self-supervised learning, and this is what's been incredibly successful for natural language understanding. So things like, so chatbots are, uh, or LLMs, large language models, are a special case of that, where you train a system to predict a word, But you only allow it to look at the words, the words that precede it, you know, that are to the left of it. Um, and that requires kind of building the neural net in a particular way so that the connections that, that predict one word only look at the, the words that…

AI assessment note: “ask the system to recover the original one from the corrupted one”

Q Is this a part of self-supervised learning?

A So you could think of this as an instance of self-supervised learning because you only need Sequences of symbols, and it doesn't matter where they come from, and you don't, they don't necessarily come from human production if they are not text. If it, you know, it could be, for example, a sequence of frames for a video, right? I mean, you would have to turn it into discrete objects, which of course is difficult, but, um, but it's, you know, whatever data comes to you. So, uh, in the late nineties, um, Uh, some people had the idea that, in particular Yoshua Bengio, the idea that you could use a neural net to, to do this prediction instead of Filling up tables with conditional probabilities that you measure from text. Just train a neural net to predict the next word, ok? Give it a context of words and just train it to produce a probability distribution over the next word. And he experimented with this with, you know, neural nets that were big for the time, but small by today's standards. And one difficulty was you cannot exactly predict what word is going to come next, so you have to produce a probability over all the words. And There's maybe a 100,000 words in a typical, um, uh, language, and so that means you're, you need to output a 100,000 scores, one for each word that indicates with which probability that word follows the, the previous sequence of words. Um, so it demonstra…

AI assessment note: “So you could think of this as an instance of self-supervised learning because”

Q When somebody says godfather of AI, the term, How does it make you feel? What do you think about it?

A Uh, I mean, I don't particularly like this term. You know, I, I live in New Jersey. Godfather in New Jersey means you are, you belong to the mafia, right? I mean, science is never a sort of individual, uh, pursuit. You, you, You make progress by, by the collision of ideas from multiple people, and you, you, you make, you do make hypothesis, and then you try to show that your hypothesis is correct by demonstrating that the idea you have, the mental model of what should work, um, is correct by demonstrating that, uh, that it works, um, or doing some theory and things like that. Um, and Um, it, it's not an isolated, uh, activity. So, there's always a lot of people who have contributed to, uh, to progress. But then, Because of the nature of how the world works, we only remember just a few people. Um, uh, I think a lot of the credit should go to a lot more people. It's just that we don't have a good, you know, memory for attributing credit to, to a lot of people.

AI assessment note: “I don't particularly like this term. You know, I, I live in New Jersey.”

Q So what happens to intelligence in society with all of this changing? What becomes, forget computers and AI for a second, for humans, what is intelligence in that world?

A So people's intelligence will be moving to different set of tasks than the one we are trying to do today. Um, because a lot of what we're trying to do today will be done by AI systems. And so we will focus on other tasks. So things like Not doing things, but deciding what to do or figuring out what to do. Okay. Those are two different things. Like think about the difference between a low level employee in a company that is told what to do and just does it. And then, you know, a high level manager in the company that has to figure out like strategy and think about like what to do and then tell people below what to do. Okay. We're going to, we're all going to be a boss. We're all going to be like those, uh, high level managers. We're going to tell our AI systems what to do. But we're not going to have to do it ourselves necessarily. Okay, so.

AI assessment note: “people's intelligence will be moving to different set of tasks”

Q If AI were to predict the future, would it be utopian or dystopian?

A It would be utopian. Uh, because it would be just, uh, uh, An alternative way for predicting the future than our brains, and for planning action sequences to satisfy certain conditions, to achieve goals. That is, ah, alternative to using our brains, perhaps accumulating more knowledge to be able to do this, and perhaps having abilities that humans don't have because of the limitations of our brain, right? Computers can calculate and stuff like that, right? So the, the future is that if we succeed in this plan, which may succeed within the next five or 10 years, you know, five to 10 years, we'll have systems that As time goes by, we can build up to become as intelligent as humans, perhaps. So reach human level intelligence within a decade. That may be optimistic, right? Um, five to 10 years would be if everything goes great, all the plans that we're, we've been making will succeed. We're not going to encounter unexpected obstacles, but that is almost certainly not going to happen.

AI assessment note: “It would be utopian.”