deep learning

“We think artificial intelligence, and in particular deep learning, could be as profound and maybe even bigger.”

“We're going to see Hundreds and hundreds of startups over time getting to better than human performance on things that we used to think only the most trained humans could do.”

“All the serious applications from here on out need to have deep learning and AI inside in exactly the same way that all serious computing systems needed to have Intel chips inside them.”

“We didn't have an expert say, here's how you find a cat with nose and paws and whiskers and this shaped eyes and these funny shaped ears. We basically just fed the network a bunch of data, and the data learned to categorize the inputs without any guidance from…”

“Deep learning is the most fundamental advance in artificial intelligence research since we started since that summer of 1956.”

“Deep learning is absolutely the bet that companies are making, right? So Google has put a lot of resources into deep learning you know, Facebook. We're designing future data centers and future infrastructures around this one technique, and it's primarily becau…”

“Instacart uses prediction techniques to help get shoppers through the grocery store faster. They went through this in two waves. Wave one was they used traditional machine learning techniques. Things like gradient boosted decision trees. And they got a three o…”

“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.”

“There are systems where robots are learning to cook food by watching YouTube videos of people cooking food. There's systems that can take photos and paint them in the style of Renoir or Van Gogh. There's algorithms that can create paintings that are indistingu…”

“It is the heart of the Go algorithms, although interesting to point out, it's an ensemble of techniques that's working for Go.”

“So the big trend is the triumph of data over algorithms. Which is you try to make more and more sophisticated edge detection algorithms, feature recognition algorithms. The big advance with deep learning was, screw all that. I'm not going to try to figure out …”

“And that's an important point about just innovation in general, which is there will be massive innovation, and in fact, I fully expect to see pure deep learning approaches to translation, to image recognition, which is, you know, internet already is that, but …”

“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 …”

“Deep learning is not the newest. It's actually developed in the sixties, seventies by people like Kunihiko Fukushima, then carried out by Jeff Hinton and Yang Lecun and their colleagues.”

“You want to just, you know, like show and talk about what tasks there is and have the robot observe and learn. That kind of training scenario we cannot do in deep learning yet.”

“Programming has been functional and procedural, which is I have if loops and else loops, and I tell it, and what I'm trying to do is predict enough state so that the computer can make the right decision. If this, do that. Else do this, right? With the introduc…”

“So the industrial project, even in capital markets, a lot of stuff we've done, you could have done 20, 25 years ago, deep learning, right? It was really distributed computing that made the big difference in that.”

“If you look at something like FB Learner Flow, which is Facebook's Automation workflow system for artificial intelligence. They've gotten it so good that 25% of their total software developer universe is writing deep learning. 25%, right?”

“It takes five years to train a radiologist, and in five years, deep learning will get better results than a trained radiologist, so we should stop training them right now.”

“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.”

“I think there are other kind of silly things that companies do sometimes that are unnecessary, like say things like physicians are going away, right, with AI and deep learning. Yeah, the greatest way to Make us not want to accept your product, right? That kind…”

“Deep learning of moles, cancer. Because that seems like a very intuitive kind of problem, but that's an easy one. We have many, many harder problems in biology medicine, but they take time to learn, so you have to be patient.”

“There is a deep learning component that will make that happen. And those are the areas that we want to invest in, so that when you're in that experience with your friends, You can actually communicate both verbally from the audio cues, but non-verbally as well…”

“One thing that's interesting to think about is that you can think of DNA almost like a one-dimensional image, and so you can use the exact same technology now to put in DNA sequences, and maybe now you're not identifying a face, you're identifying whether some…”

“When it comes to science, Just the applications of all this technology that's happening right now are still completely underexplored, whether it's, you know, using deep learning to get approximations of systems faster or figuring out how we can just accelerate…”

“Currently the pace of progress is very fast. Maybe also the ideas tends to work out a little bit more often than they did in the past. Because yeah, deep learning just wants to learn”

“And deep learning has been this miracle that keeps on giving, and we have kept finding, like, breakthrough after breakthrough. Again, when we got the reasoning model breakthrough, like, I also thought that was like, we're never gonna get another one like that.…”

“I think deep learning is still in the Shannon entropy world. It has not crossed over to the Kolmogorov complexity and the causal world.”

“All of deep learning is, ah, doing correlations. It's not doing causation. Causal models are the ones that are able to do simulations and intervention.”