machine learning

“The surprising thing with Middle Out is that, like, there is actually a technical background for it, and like, it is actually the result of a lot of sort of machine learning developments applied to compression. So the sort of, like, the theory behind the tech,…”

“Over the time Google search started to use machine learning because it was, it helps to improve results but simultaneously, they wanted to avoid it for some time as it's more difficult to interpret the results, and it's more difficult to actually understand wh…”

“Once a machine learning system is behaving in a biased way, one way of trying to deal with it is collecting more diverse data. Okay. But one of the big problems is when you do that, you end up collecting lots and lots of data about minorities, which raises all…”

“And the upshot of that basically is that the, like, amount of, like, you don't need a PhD in machine learning to get all the benefits from machine learning. Right. And I think that will shape the space, for sure.”

“The amount of data you need to pull off certain types of machine learning applications is going down over time. And what that tells me is that there might not be necessarily a first-mover advantage in this space, where you may actually have collected a bunch o…”

“Like what's it mean that we have an aging workforce, right? Or like, what's it mean that we have like falling workforce participation in the United States, right? Like those are actually trends that like, That are almost as large as, like, what someone comes u…”

“I think the second thing that's about to be in really strong demand is thinking about the visual dimension of this, right, which is, like, happens on a couple levels. That's both, like, the interface of how you work with machine learning systems, But also just…”

“And I think, like, one enormous skill will be, like, domain knowledge. Because, like, coming up with, like, a technical capability is just, like, one part of this huge picture, right? Which is just, like, okay, so then, like, how do we actually introduce autom…”

“One of the most common problems is just that you don't adequately think through your data, and so the machine does what the machine does, right, which is trying to optimize against your objective function that you give it. And it'll often maximize in ways that…”

“Where people are basically working on the ability to teach machines, but like a much smaller number of examples. Now that actually has a really big impact on the game. Cause that means that you can implement machine learning effectively. In situations where it…”

“Adversarial examples lead to these really fascinating results where, you know, you can take a picture of a panda, and that's a classic example, and you edit a couple of the pixels, and it, like, basically, like, the computer will be like, yep, that's definitel…”

“Like some things that used to be hard will be easy. And so, We'll offload all of that. And if people are happy just listening to the stuff that's now easy, then yeah, it's a problem solved and we'll be able to generate lots of it. But then what people tend to …”

“If we get to models that can handle longer structure and nested structure, we'll have a lot more ways in which we can decide what we want to work on versus what we have the machine help us with, right?”

“The first thing you're gonna do, if you think, if someone comes to you and says, here's this really smart model that you can make art with, what are you gonna do? You're gonna try to show the world that it's a stupid model, right? But maybe the way that, maybe…”

“Yes, I'll probably never live to see the day when someone doesn't say my kid could do that.”

“Because the thing is, if you have a new idea for, okay, well, I've kind of got this thing working, and now I'm going to try something slightly different. As you're doing the new thing, well, machine learning is, to some extent, very binary. At the start, it ju…”

“Getting good basics in linear algebra and in basic statistics, that's, especially when Doing experiments, it's easy to make like elementary statistics mistakes and linear algebra is just kind of most of what you need to know to like basic optimization as well …”

“Normally, the way that you do it is, well, your goal is to have everything be very observable. And so, yeah, you want to put metrics on everything, and like, you know, if something's not understandable, add more logging, like, you know, that that's how you des…”

“I'm in the process of trying to write a novel that has as kind of part of its text, the product of some of these machine learning systems.”

“The core interesting thing is the weird sort of wonderful output of this machine, right? But at the same time, you acknowledge that having a human kind of curate it and shape it and form it and just Be in the loop and be learning how to use it is just as impor…”

“The newsfeed assuredly is AI, right? Like, it uses machine learning. It uses the latest machine learning to do what it does. We don't really think about it as AI, right? Whereas, like, the car is, like I mean, I think a lot of robots kind of fall into this cat…”

“Archive is, is super important because the papers are, the new papers are coming out at such a high rate that people don't wait before the papers go to journals, before they start working on top of it and using the stuff that other people discover.”

“My view on this is AI is about a rapidly efficient way of acquiring knowledge. We can use sensors, cameras whatever sensors, to observe a phenomena, and use deep learning, machine learning, some techniques to rapidly distill into self-knowledge.”

“Pretty soon you can underwrite the likelihood that a person will be a successful software engineer before they've paid a dollar.”

“Then the use cases that people are building now tend to be a lot more subjective than you might have done with machine learning before. And so evaluation is a lot harder. You can't just calculate accuracy on a test set.”

“Historically in machine learning, you always, you know, it's like the rule was you have to know your data set really well. But now we're kind of outsourcing that to the AI itself, where the AI is the, it's the AI's job to understand the dataset and figure out …”

“I personally don't think that machine learning or AI in 50 years is still going to be built on this stack.”

“I think machine learning tends to have a long history of people starting with bio-plausible arguments, and then realizing that there's some variant of them that seems highly bio-implausible that actually works better.”

“I mean, I think the example you raised of a thousand X difference in bringing moving data versus actually computing on it in, in terms of energy is, is a pretty significant one. And it shapes a lot of aspects of what we do in machine learning. Because if you d…”