machine learning

“The big thing now is machine learning, and that will be the thing for another 10 years.”

“Every big company has got dozens of projects and more every big company is probably using machine learning without knowing because it's inside something that they've already bought.”

“And so then what machine learning does, or neural networks, or deep learning, whatever, or slightly different terms that all kind of get at the same thing, is you say, instead of making it a logic problem, we make it a statistics problem.”

“And of course at this point, yes, you've got lots of AI inside it, but the product isn't machine learning. The product is sales process optimization or, you know, fraud detection or, you know, network security intrusion detection or something. And so the produ…”

“Generically what machine learning did was it let you solve problems that are hard for people to explain, but easy for people to do. The problem with these generative networks is of course, this is prompt engineering is it's easy for you to imagine, but very ha…”

“So basically what this is, is if you think very, very crude terms of machine learning is pattern recognition on a massive scale. This is take that pattern and make another one. So where image recognition is to say, does this, which pattern does this fit? Does …”

“I don't think the direction of travel of software is bigger screens. I don't think the direction, or more fundamentally, is not about showing you more information. It's not about having more rows in the spreadsheet. It's having less rows in the spreadsheet and…”

“And so this was, there was this sort of whole class of problem where, which people say easy for people to do, hard for people to describe. So something like calculating a mortgage is hard for people to do, but easy to describe. It's very easy to write down the…”

“So, you know, you do a database or a financial model, you know, you've made a series of logical steps and it will follow those logical steps. Whereas with machine learning systems, you're saying do something that's sort of like that, and it will be sort of lik…”

“Well, the analogy I always used to use talking about machine learning was that this gives you infinite intents, that all these sort of things where it doesn't need an expert, you just, you know, because I'm listening to the call, coming into the call center, i…”

“Creation of the stack with the enthusiasm for crypto, whereas creation at the top of the stack is the enthusiasm for machine learning.”

“It changes an awful lot of how tech works and what tech products you'll use. And there'll be all sorts of stuff that's happening inside stuff you use you don't even know is using this, which is also what happened with machine learning. So at the minimum, it's …”

“But in general, machine learning has become a commodity very quickly which is sort of the absurdity of people saying, well, we have to ban face recognition, or you can ban it, but that won't work. Cause it's just a commodity technology.”

“The way I always used to talk about machine learning is it gives you infinite interns. You can get an intern to do something. Anything you can get an intern to do, machine learning can do for you know.”

“We haven't got to the point that we've got a mental, even if you think it's a platform shift, we haven't got to a point that we've got like a kind of a useful mental model of what would, what is this? Whereas for the last wave of machine learning, we're now ki…”

“The analogy I used to use for the last wave of machine learning was to say, this gives you infinite interns. So you want to listen to every call coming into the call center and tell me when the customer's angry. You want to look at every credit card transactio…”

“It's hard to tell a computer how I recognize a cat, and that's the same category of problem as it's hard to tell a computer why I like that suitcase, or why I like that hotel, or why that's a bad flight to book.”

“I mean, the last wave of AI was machine learning, 1015 years ago now, 10 years ago, really. That was not transformative across everything. It was a new bunch of stuff that everybody could build, and there were a bunch of new companies with it, but it wasn't th…”