deep learning

“So probably many of you know that most You know, mobile speech recognition application search and stuff like that uses deep learning for the last two years. This is both for, you know, Apple, Android, Microsoft. All of those applications use deep learning for …”

“There's a kind of a sense that in the community that the next set of techniques to kind of fall to deep learning, if you want, will be natural language processing.”

“And deep learning basically opens the possibility that you, to automate the process of feature engineering. So, it essentially learns to, It integrates the process of producing good representations with the process of learning a classifier in one fell swoop.”

“I mean, the whole success of deep learning, actually has taken some of us by surprise. We were all sort of Convinced from a long time ago that deep learning was going to take off at some point, but the speed at which has been picked up by, ah, industry and res…”

“Industry picked up on deep learning faster than academia.”

“In terms of where we're going to see it In industry, it's likely to be still in a kind of vertical by vertical system for a little while before we start seeing a little more convergence.”

“You don't need an expert in your domain for understanding and representing that data in order to give it to a final classifier. It will actually learn all of that automatically.”

“Deep learning is a set of algorithms that is really not that different to machine learning in general. It can do anything that general machine learning can do, and in many cases better, but it really shines when you have unstructured data.”

“People get very excited every time there's a new deep learning result. But it's always the case of doing better on the high frequency data than the low frequency data.”

“Deep learning doesn't seem to be getting us there. The old systems, in some ways, we're better at that. We do need a marriage between the two.”

“In deep learning, you're not doing feature engineering anymore. Nobody cares about what you know about the domain.”

“Deep learning requires a lot of data, relatively speaking, and therefore, by definition, cannot detect recent changes in the data, emerging patterns, ah, and therefore, it's, ah, really, really, ah, inadequate to predict human behavior that is, ah, actually do…”

“All the technologies that we have today were developed around 50 years ago, plus minus a decade. Even deep learning that is so trendy today, it's basically very large neural networks. Concepts from the fifties matured around the seventies.”

“But I think that's, you know, that was the sort of tailing off of the, for me, the Twitter Magic Pony deal was kind of the, ah, the tail end of this absurd, extraordinary premium on, on deep learning, and then now, more and more, it's becoming just part of the…”

“In that world, even the most advanced models, deep learning, and machine learning frameworks can't beat them because they have access to this kind of core underlying data.”

“Notice I did not quite use buzzwords like NLP, or deep learning, or data science, or machine learning, which I could very well have used, but I don't think it's that useful.”

“And that's something that historically has been done mostly with rule-based systems, but today we do it with deep learning and a lot of decision trees.”

“If you have startups in your portfolio, I know we have at least one VC in the audience, so and they're trying to do something in deep learning. They're trying to build a new product with some of this technology. I think you'll find that they do struggle with i…”

“But if you want to change your columns, your categories, then you have to redo all your multiple choice tests and then retrain the system. And if you want to change the categories, you have to start over.”

“AI is really good at instinct. I mean deep learning, it's not so great at thought.”

“More than half Of those companies in that fund involved sort of deep learning in some way.”

“Deep learning doesn't even really fully understand the relations between parts and wholes. It certainly doesn't understand what a silhouette is, and it gets it wrong.”

“Deep learning, it mostly cares about texture.”

“These successes are examples of one thing. They're all examples of what a cognitive psychologist would call perceptual classification, and that's part of what we do as intelligent human beings, but it's not all that we do.”

“If a typical person can do a mental task with less than one second of thought, and we can gather an enormous amount of data, of directly relevant data, we have a fighting chance. So long as the test data aren't too terribly different from the training data, an…”

“Perception is what deep learning does, and actually it only does a small part of perception. There's lots of parts of perception where we use our knowledge about the world that it doesn't capture. And then there are all these other things like common sense and…”

“I think that deep learning doesn't just fail on reading. It completely, entirely misses the point.”

“Deep learning can't do what we call compositionality. It can't put ideas together.”

“Deep learning is a better ladder for sure, but a better ladder doesn't necessarily get you to the moon.”

“I agree that deep learning is not about to give us generalized intelligence, but it has really fundamentally changed the way that we interact with unstructured data.”

“A lot of the things that people think of as successes of deep learning are actually successes of combining deep learning with other things.”

“And at the end of the day, you know, you can't, like the European Union, mandate that every model has to be explainable, right? Because then, if you read that law literally, it makes deep learning illegal, right?”

“I think pharma is investing way more in deep learning than people realize.”

“Deep learning is, is amazing. And with that said, it's necessary, but not sufficient component.”

“There's maybe a couple of million deep learning engineers now in the world. In contrast, there's probably maybe only a few hundred like really great program synthesis folks across, across the world.”

“In deep learning, people laugh that ideas are cheap. Making them work is, is the hard part.”

“Especially in deep learning, a negative results doesn't mean something doesn't work. It means you haven't made it work yet often.”

“At the time he had been working in bioinformatics, so he hadn't been working on deep learning or the things that we call AI these days.”