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“I actually do not believe that that's the case. I think Backprop will not get us there.”

“And I think they will be in the case of AI, but I don't think we're going to see this, you know, infinite growth that, you know, goes completely beyond you know, what humans can imagine.”

“I would say we are so much in the beginning that we can't even really picture where we're going to be 20 years from now.”

“So we humans, right, I can see a future where our full-time occupation is to tell the, you know, algorithms what we want to do, set the objective function, set the boundaries, and then verify the solutions all the time, continuously.”

“So in the not too distant future, the vast majority of the knowledge on earth Will be discovered and will be stored in computers.”

“For example, there's this venture fund recently that announced that one of their directors is now going to be an algorithm. There's seven directors on the board, and one of them's an algorithm. So their algorithm doesn't decide anything all by itself, but it d…”

“But what I and others believe is that we can Develop a true master algorithm, meaning an algorithm that is able to solve all the different kinds of learning problems that these different algorithms can.”

“Machines are remarkably better than human doctors at doing all types of medical diagnosis, not just from x-rays, but from, you know, symptoms, right? You have a patient, you have their symptoms, what is the diagnosis? And even very simple machine learning algo…”

“We already have two people in the cockpit instead of three, and then we'll have one and eventually we'll have zero.”

“But I, my guess is that, you know, five years from now, there will be a lot of self-driving cars around and, you know, maybe 10 years from now, 15, Most cars will be self-driving.”

“So, but I think that in the short term the approach of the Teslas and the Toyotas and whatnot will be the prevalent one. I think in the longer run, it will be the Google approach that prevails, right?”

“There's been enormous progress. The five major paradigms are exactly the same as they were then.”

“Terminator isn't happening anytime soon. First of all, because the technology isn't there, but second of all, because there is this, I mean, like a lot of these, I think, errors that people made come from anthropomorphizing AI.”

“And the thing that's amazing is that often just by taking a basic machine learning algorithm and applying on a database of, for example, x-rays and diagnosis, you actually wind up with something that is better. That, for example you know, pathology than a high…”

“I think as time goes forward, you know, the machine learning will get better using a broad spectrum of information. I think for a long time, there will still be Types of common sense knowledge that people have. So I don't think for most things you know, the hu…”

“And in fact, it would be safer if it was completely flown by a computer. You know, pilots tend to take, you know, the controls at landing and takeoff, which are actually the more dangerous moments. And they make more errors than the computers do.”

“Well, I think we could at this point actually, and it might actually win. I think in the past, the technology wasn't ready. And then once the technology is ready that people have to let it happen, right? So the Indy 500 would have to let a self-driving car com…”

“On a theoretical level, every one of these major machine learning algorithms has a theorem that says if you give it enough data, it can learn any function.”

“Having said that, if you look at the number of connections that the state-of-the-art machine learning systems for some of these problems have, they're more than many animals. So we're actually at the point where the, you know, they have hundreds of millions or…”

“And I think this emergence of computers as a source of knowledge is going to be every bit as momentous as the previous three were.”

“So for example, these days there are hedge funds that are completely run by machine learning algorithms for the most part, you know, hedge fund will use machine learning as one of its inputs. But there are some where the machine learning algorithms, they look …”

“With some types of machine learning, like, for example, neural networks and deep learning, it's very opaque, right? What is learned is this big jumble of lots of parameters and all near functions, and nobody really understands what's going on. Which, in fact, …”

“People have developed new types of radios and amplifiers and electronic circuits using this type of machine learning, and they've actually gotten patents for them. So they work better than the ones that were developed by human engineers. They're typically comp…”

“A couple years ago, Eve actually discovered the new malaria drug.”

“But, you know, eventually it is going to happen, and it is starting to happen, for example, in situations where doctors are not available, and so nurses can use this, or for patients that need, you know, constant monitoring, or in low resource situations where…”

“The best chess players in the world today are what are called centaurs in the community. They're a team of a human and a computer. So a human and a computer can actually, together, can actually beat the computer.”

“But I think for the foreseeable future in most jobs, it will be a combination in human and computer that works best.”

“The stock market is largely a bunch of algorithms trading against each other. And in fact, what these algorithms are doing Whether or not they know it is modeling each other.”

“And once, for example, these machine learning systems become more widely available as they are becoming, people will start using them and the doctors will be forced to catch up.”

“Ultimately I think most things will be done by machines, except the really key decisions that people will always want to retain, even though they make them with advice from the machines.”

“Having said that, this is still quite limited in what it can do, and it's not, we don't have enough at this point for this thing to set up this loop where it just keeps getting better and better. That hasn't happened yet”

“So what's going to happen is that the machines are going to be able to learn much more complex models of the phenomena than human beings ever could, and this is good, right? Because with those better models, we can make better decisions. With a better model of…”

“I think what's going to happen is that partly the learning algorithms are going to have better, to get better at explaining to people what they're doing.”

“Because I think this is something that has not been exploited in machine learning and might be the origin of that sixth paradigm.”

“And there are several major such algorithms today that have mathematical proofs that if you give them enough data, they can learn any function.”

“And this type of meta learning in certain basic forms is actually already widely used today. Like for example, Netflix uses this type of thing to recommend movies. It doesn't just use one learning algorithm. It uses a whole bunch of them. And then another algo…”

“So Deep Blue was very much classic AI. There was no machine learning involved. Deep Blue essentially, it was just doing a very a clever and very extensive search for the best moves to make.”

“And so what DeepMind did with AlphaGo was to actually combine some of the classic AI, you know, game search with deep learning, with this type of you know, neural network approach to do the evaluation.”

“So AlphaGo, the first thing that AlphaGo did was it learned from all the existing, the entire existing database of Go matches played by human masters. That was the first thing it did was learn from those, right? Thirty million moves or something like that is t…”

“This is actually a very, very old idea in machine learning. It's one of the oldest ideas. It goes all the way back to the fifties. And this researcher at IBM called Arthur Samuel, who actually wrote the first machine learning system to learn to play a game. An…”