machine learning

“And also there's usually, there are usually diminishing returns from scale in, in terms of quality of models in general. And you can also kind of see it in other areas, like in computer vision, for example, we don't have, you know, trillion parameter models.”

“What we're seeing in, in machine learning is that it's actually making its way into production after, you know, a year, a few months, a few weeks sometimes a few days now. So this is, in my opinion, this is amazing. And that's what's driving Most of the speeds…”

“Big science was like the largest collaboration in, in machine learning to date with like a thousand researchers from 200 organizations, kind of like coming together in order to build and train a large language model completely in the open.”

“There's a lot of tech people who come in To life sciences, and it's like, we have that cell verbal. We are the smartest. We're machine learning. We're going to solve everything. And they don't respect the challenges of the other discipline. They sometimes don'…”

“My analogy is that it's not like x-ray crystallography. It's like computers. You're going to use it everywhere, and it's going to be transformative everywhere.”

“Because when you think about it, it's the one area where you really don't have the right type of training data, at least not obviously, because the question you're asking yourself is, if I make this therapeutic intervention in this patient, what is it going to…”

“The first project that I did, which was you know, like a machine learning classifier thing in 2003 2003, 2004. Like that was, you know, stacks of like super technical, you know, research papers and, you know this elements of statistical machine learning, you k…”

“And in reality, we've had a technology disruption. We've shifted to two very different architectures, diffusion-based models, which is a statistical physics model for ImageGen. And then on the language side, we moved to these large language models, which some …”

“And I think a lot of people basically view ML as this sort of continuity and everything has always been kind of rising in a, Sort of almost linear way. And in reality, it's this very bumpy set of discontinuities in terms of the set of technologies and markets …”

“We use ML to optimize authorization requests for issuers, basically Identifying the optimized retry messaging and routing combinations to recover a big chunk of false declines, about 10%, so billions of dollars globally.”

“In robotics, You know, most machine learning people will look at it as a data collection problem where your internet data, even video data of a bunch of actions just isn't enough. We need embodied action data, like, you know, controls data in some way.”

“The vast majority of, you know, mechanistic data that's been generated to date is really made to ask very specific, very well scoped questions and just, you know, way more tokens per experiment is, you know, just, it's just going to be the way to do it.”

“Typically in machine learning, a lot of the successful applications of like recommender systems, language models like image detection, a lot of the consumers of that Of the model outputs are actually humans who could actually check it, and the humans are good …”

“These observations were made by skilled geologists, and the rocks haven't moved, so there's no expiration date on the data, and so you can take data sets like this that provide ground truth and use it for training machine learning models based on modern airbor…”

“And I think the physical sciences, physical engineering, Will have a very similar property where we establish these scaling properties and Bring that mindset.”