The Ledger, every show

“There's this network effect of data where if you have a good product and people start using it, then you have a lot of use this, for example, you know, how Google has built up such an unassailable position in search, right? Is like you use their search engine.…”

“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.”

“I think more likely what will happen is that people will still be playing each other, even though the best is our computers in the same way that, you know, there's race cars that go way faster than people. But we still have people doing, you know, in the Olymp…”

“The cars don't have to be perfect before we start using them instead of people. They just have to get better than people.”

“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.”

“Having said that, you know, we, the machine learning researchers and the data scientists, we actually have a good understanding of how the learning algorithm itself works. You know, what is it that it does to learn, and how could you make it learn better? And …”

“So what happens whenever you deploy a machine learning system is that the people who are being modeled Change their behavior in response to the system. Sometimes in benign ways, but sometimes in adversarial ways.”

“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…”