The Ledger, every show

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

“And at the end of the day, you know, you can't, like the European Union, mandate that every model has to be explainable, right? Because then, if you read that law literally, it makes deep learning illegal, right?”

“In the particular case of medicine, it's not used more already because, of course, the doctors are also the gatekeepers Of the system, and they're not very interested in replacing themselves or their job that they like best by machines.”

“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.”

“With the existing machine learning technology, of what we can do with that, how much have we done? Very little so far. So there's enormous amount for, enormous scope to do things there. Not just in finance, but in a lot of other fields, right?”

“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.”

“The hardest problem in AI is coming up with a representation. Once you've done that, the rest follows, and when you don't do that, you don't really get very far”

“AI can be a great tool for democracy. It is also unfortunately an amazing tool for authoritarianism.”

“The real danger of AI is not that computers will get too smart and take over the world. The real danger is that computers are too stupid, and they've already taken over the world, right? Computers are making decisions about us the whole time, and they're dumb,…”

“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.”

“And my gut feeling is that actually it's more the latter. I do believe that we have made a lot of progress, but I think we are still missing some important ideas.”

“So people often think that the easiest jobs to automate are like the blue collar ones. But actually our experience in AI is that it's actually more the opposite. It's often white collar jobs that are easy to automate. For example, things like engineering and, …”

“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?”

“A lot of the things that people think of as successes of deep learning are actually successes of combining deep learning with other things.”

“I think capsule networks are a very interesting, you know I like the intuition behind capsule networks. I think they point in the right direction. I think Jeff hasn't quite been able to make them work yet.”

“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…”

“Part of my goal in writing the book was to try to get people from outside the field interested in these problems, because in some sense they are more likely, ironically, to have these new ideas than the people who are already professional machine learning rese…”

“And what typically happens when somebody, you know, deploys a neural network to predict, you know, a certain stock, like for example, you might have 3000 networks each predicting one stock in, in, in the Russell 3000 is that it works for a few weeks and then i…”

“Precisely because machine learning is something that can be used just about everywhere, right? In every single industry, in every single part of what a company does, So far, it's only been used for a small fraction of the things that it could be used for. So y…”

“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.”

“I think that argument is actually very dubious, because in reality, no exponential goes on forever. Because there's always a limit because the world is finite. So actually what happens with all of these technology curves is that in the beginning they look like…”

“Actually, the learning algorithms don't have to be black boxes. There's actually no reason why I shouldn't be able to say to the Amazon recommender system, why did you recommend that book to me? Or, you know, I just bought a watch. Please don't recommend more …”

“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…”

“This notion of a mix of mixed control between the human and the car is actually very problematic. If someone is not driving the car and then suddenly the computer says like, oh, shoot, I'm confused. Take over. Then the person will not be very well able to take…”

“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 …”

“In traditional computer science, you need to write down a different algorithm for everything that you want to do. So if you want the computer to do diagnosis, you need to explain to it what are the rules of that diagnosis. If you wanted to play chess, you need…”

“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, …”

“Any knowledge that you induce from data is necessarily uncertain, because you never know if you generalized correctly or didn't.”

“Conversely, a lot of the knowledge that we have from evolution and from experience and from culture, we often tend to think of it as much more certain than it really is. We have this great tendency that's been well studied by psychologists to be overconfident …”

“Machine learning today is very good at learning about one thing at a time. The thing that humans have is that they can bring to bear knowledge from all sorts of directions.”

“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.”