machine learning

“Now you're getting into some interesting, ah, places where machine learning hasn't spent a lot of time, which is on time series data. And what we wound up realizing in our own, sort of, domain specific research is that there weren't a lot of tools for us from …”

“In some sense that's the greatest imperative and like the gauntlet that I lay down in front of anyone is that you're not doing machine learning because it's cool and it's fun and you can learn something about the data from the past. You're trying to really use…”

“In the future people are just going to be buying products where machine learning and machine intelligence are baked in.”

“So a company like Freenome will use machine learning and deep learning with genomics to be able to tell whether you have cancer early enough, such that the cure rate, success rate could be 80%, a hundred percent.”

“So the only way that we can look into an image or look into the massive amounts of data is with machine learning, and that machine learning, the algorithms and the applications employing machine learning will run at the end point. It's not going to be machine …”

“Some people may think this is a very simple machine learning problem. Like, okay, so let's grab some data, analyze it, and then build some sort of model to predict their cost and outcomes, and let's just apply in the reality. Without knowing the fact that the …”

“It's more about designing a new paradigm in machine learning from scratch.”

“If machine learning only did image recognition, that we would still be the biggest thing in the tech industry, because computers are going to be able to see.”

“Is that finding patterns becomes a generalizable solution. So is there a cat in this picture becomes the same kind of question as which customers are going to churn, or is that car going to let me merge, or is there something odd happening on our network, or i…”

“Machine learning doesn't replace one person. It gives you the capability to do what you could only have done if you had a thousand of those people before.”

“In fact, I think calling it AI itself is unhelpful. Talking about artificial intelligence is sort of unhelpful. It's more useful first of all, to say machine learning, which is the primary technology we're interested in, to talk about automation, and I think t…”

“And so the same thing with machine learning now, the first demos we get are cat pictures and trivia questions, but, you know, there will be an awful lot of other things that get built with that, because those are just demos, those aren't actually what the tech…”

“I think what we expect to see is the shift from many areas that used to be areas of science risk through engineering mechanisms and machine learning now become really engineering problems. With that in mind, we can finally understand and tackle the technical d…”

“And so if you think about the game plan that Freenome runs for taking blood and understanding where there's cancer and what's the cancerous agents in there, what BioAge is doing is doing essentially the same thing, looking at the blood of young, using machine …”

“I think I think we're already seeing a little bit of that with just the shift to CROs, where there's like not a purely medicinal chemist job, a sort of drug designer job. And medicinal chemists have so much great intuition and experience designing drugs that t…”

“I predict that with machine learning, that this is even going to become easier to trace, as machines are going to be able to get smart about who owns what wallets, and make connections that no human possibly could.”

“The fundamental framework for thinking about machine learning, it's a tool to scale people, but to scale people in two very different ways. One of them is, I have a million pictures in the basement, and I now have a million interns that I can send down to look…”

“Now we have two new fundamental layers. We have crypto, and we have machine learning, which among other things will swing away from centralization.”

“We have the opportunity to build both products and companies that have never been even imagined before, even without more significant fundamental advances in machine learning.”

“These kinds of services can't really exist as applications. A, they don't get any traction. Anything that requires machine learning and AI requires massive user bases to start to build out their data sets so that they get all the more powerful, et cetera. So w…”

“We don't necessarily get smarter over time. It's just that certain technologies get cheaper. They get, they become more available. So machine learning algorithms have always been around. The data that exists that you could collect has always been around. But i…”

“What you will see is that all of this machine learning will be a property of every application.”

“And the way I think about big data is when machines learn from big data is very much like human beings learn from life experiences.”

“So it turns out the reason for big data is machine learning.”

“You can think of business intelligence going forward as the ability to apply machine learning algorithms to big data, and not just look at past questions, but also future questions, or asking to predict the unknowns from the knowns.”

“The idea was you wanted to be able to bring in people to solve those parts of these, you know, machine learning problems that the machine learning wasn't quite up, up to speed for.”

“In practical software architectures, we're starting to see the rise of microservices. What's nice about microservices are very, very cleanly defined systems. So you don't need generalized intelligence. You just need very specialized optimizations. And as our s…”

“So I think there might be something special this time and one of the indicators of that is the fact that there's so much commercial and industrial in, interest in in, in AI and in machine learning.”

“I see machine learning as a kind of subfield of artificial intelligence, and it's a subfield that's had tremendously a tremendous amount of success in recent years, and is going to go very, very far, but ultimately, the machine learning components have to be e…”

“The way that you build a system that predicts using machine learning is, is very utilitarian, right? You say there's some cost function you want to minimize, there's some objective function we want to target, and then you train it, and you don't really worry a…”

“What's driving the whole machine learning revolution, if we can call it that is I mean, there are three things, and one is Moore's Law, so the availability of a huge amount of computation, and in particular the development of GPUs, or the application of GPUs t…”

“I think with, especially with machine learning, you could learn features from data without having to share the data itself. And that's useful for IP or for HIPAA and so on. So I think there's a lot of ways that one could contribute to network effects without m…”

“Especially these new modern machine learning methods like deep learning just crave data. And so often you have to reach a critical mass before they can even be used.”

“Especially at the very earliest stages, you don't have features In the traditional sense, like, you don't have a lot of, you know, really good data to work with in terms of prediction, so the later it gets, probably like series C or thereabouts, you have enoug…”

“An entire battery of techniques that people have known about for a long time, plus some new techniques have, in, in, in machine learning and deep learning, have really started to work. 20 12 was kind of the tipping point for that, and now it's really building …”

“What's different now as opposed to what was happening in 2000 is finally the technology, the machine learning techniques, as well as the hardware supporting that has matured to a point where we don't have to try to manually figure this complicated system out b…”

“There's a learning aspect here of machine learning, which is intriguing, that as you get more data, you get better, and that's something that's really not like any other test, where, you know, a lipid blood test doesn't get better as you have more patients the…”

“Most ML implementations are not deep learning. They're not neural networks. There's a ton of implementations that are incredibly valuable that don't involve networks.”

“Unless we get to in memory processing and in-memory data structures, machine learning doesn't really work.”

“Machine learning is like this new foundational technology that, in a sense, will enable a bunch of new stuff and new companies, but it makes Google better Google, and it makes Facebook stronger, Facebook and Amazon maybe stronger Amazon. It doesn't unlock, it …”

“So there are companies that come in that have been doing, you know, hardcore ML stuff for a long time But they haven't called it AI. They're probably older techniques, probably not the kind of latest DNN stuff or whatever. And then they start calling it AI bec…”

“And that does get fixed. That will absolutely get fixed because of machine learning.”

“There are going to be lots of cases of, in specific domains of, it did this thing a human would never do, and there will be lots of machine learning examples of, well, the machine thinks we should do X, and the people think we should do Y, but the machines wil…”

“Healthcare, we're going to look back 10 years from now and go, oh my god, we let humans who had only seen four cases of this decide what to do next instead of running it through, you know, here are the symptoms and evaluating against terabytes of historical da…”

“If you're in this engineering curve, the false positives are actually as important to learning as the true positives.”

“We're still not in the large, large data regime where, you know, blind architectures that don't exploit structure of the problem can just work out of the box. So you really have to understand your problem domain and figure out how to exploit the structure that…”

“It's not really about the machine learning inside the box. It's about how do you get it so that the physician doesn't even have to think about how to use your system. It just happens naturally.”

“So maybe the only area where people are very aggressively buying right now is machine learning, and so maybe there's a lack of why now.”

“It's a way to effectively index machine learning, because I think it's very hard to invest in, quote, unquote, a machine learning company.”

“You just see breakthroughs in biology and chemistry, in astrophysics. Coming out of various forms of machine learning. So all of a sudden it becomes this tool that is applicable to a whole range of things and is changing those fields.”

“And this is a big gap right now because the senior people in the field, it's highly unlikely that most of Most of them are going to take a year or two out and go back and learn a bunch of things about computer science and statistics and machine learning ideas.…”

“ML is the ultimate garbage in, garbage out technology, because if the data isn't good and properly validated and the learning process isn't You're going to get assumptions and outputs that are ridiculous.”

“I think the marriage of direct evolution with synthetic biology really is also the future, coupled to machine learning.”

“There's just a lot more we can do with machine learning, especially when it comes to language and music that is still, I think, very untapped here in the West.”

“And then not only is that the case, they use the machine learning aspect to help the teachers with scoring the kids and scoring the performance, because with music, just like with language, there is a actual pitch, there is an actual tempo, an actual rhythm th…”

“The robots aren't coming for your jobs. Skynet's not coming for your children. Machine learning is actually going to make us better humans.”

“We're not going to think about these models as some sacrosanct up or down vote. That they are basically going to be features into machine learning where you're going to have this as the inputs and label data in humans from previous experiments to be able to co…”

“And so right now we've got an experiment going on where we're looking at machine learning based prediction of which trucks we should inspect on the border.”

“When you do biological experiments, one of the strongest signals when you apply machine learning to it is what was the technician who actually did the experiments? You could read that very clearly off the cells because they behave a little bit differently.”

“What brought me back to this field back in 2016 post Coursera was the realization that we can now finally, for the first time, measure biology at scale, both at the cellular level, sometimes at subcellular level, and at the organism level via ways of quantitat…”

“It wasn't having much of an impact in the life sciences, and I believe one of the main reasons for that is because there's so very few people who actually have the language of both disciplines and are able to bring them together.”

“I think this time that we're living is the time when those last two disciplines are actually going to merge, and they're giving us an era of what I think of as digital biology, which is the ability to measure biology at unprecedented stability and scale, inter…”

“And so it's interesting because AI has become so useful because we've got so much more measurement technology, right? Cheap measurement technology. So we can do, you know, we can do generalized sequencing, cheap, we can do RNA seq, we can do the single cell ty…”

“We've got more measurement technology, but it's in such high dimensionality on people that, like, you, I can't make sense of an array of, like, a whole bunch of expression changes which just fluctuate around baseline versus a disease patient. Right? Like, who …”

“Usually in a drug discovery project, you know, if you have a hundred actives, you should be close to a drug, but a hundred data points is, like, tiny for machine learning, right?”

“Yeah, I think you touched on, like, probably the core problem of, like, you know, ML applied to science. While we have a lot of, like, unlabeled data, like, there's just not that much label data out there. And a lot of it is because, like, it's very experiment…”

“If you have structured data, you're doing forecasting models, a lot of the fraud management, traditional artificial intelligence, machine learning, Is going to be more efficient, more effective, and certainly more cost effective as a way to do it.”

“But really, I think with some of these more product experience questions, there's something qualitative about it that is very hard to quantify. That is one of the big challenges internally, actually, is how do you hill climb effectively on what is really an ML…”

“I think from my perspective, good taste in ML today, because it's such a fast moving field with so many people working across, you know, open source and APIs and big labs and so forth. Really, you're trying to identify What part of the ecosystem or what part o…”

“And it was a somewhat localized problem because like you're doing data science on your laptop versus now I'm just shipping off secret IP to some SaaS company or something like that.”

“Like machine learning is the stuff that's now become easy and then AI is all the fun new stuff. And then as soon as it stops becoming the cutting edge, Then it just becomes, oh, that's just machine learning.”

“On the material science side, we're seeing scaling laws within physics, within chemistry both with respect to simulations, with respect to experiment, and it's like the same kind of principles at play and ML.”

“And to do that, you have to underwrite. Like, you can't shortcut the, I'll just look at your FICO score, or I'll just sort of, you know, I'll look at your Facebook friends. Like, none of that works. You actually have to do a real, very sophisticated degree of …”