A Quote by Jeff Dean
Deep neural networks are responsible for some of the greatest advances in modern computer science.
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The important thing to know about playing to win and playing not to lose is that there are actually different neural networks that are being used. It's not very easy to do both at the same time and, if you are trying to have a playing to win mentality, you're going for it, there's some things that trip you up or trigger the wrong neural network. If you start worrying about your mistakes all of a sudden, if you get too focused on the facts and the details, these are going to shift your neural networks and sort of screw up your strategy.
Modern science developed in the context of western religious thought, was nurtured in universities first established for religious reasons, and owes some of its greatest discoveries and advances to scientists who themselves were deeply religious.
My particular focus at the moment is on the development of genetic algorithms and neural networks that work together to create computer architectural systems.
The life and soul of science is its practical application, and just as the great advances in mathematics have been made through the desire of discovering the solution of problems which were of a highly practical kind in mathematical science, so in physical science many of the greatest advances that have been made from the beginning of the world to the present time have been made in the earnest desire to turn the knowledge of the properties of matter to some purpose useful to mankind.
BitCoin is actually an exploit against network complexity. Not financial networks, or computer networks, or social networks. Networks themselves.
Some of the greatest, most revolutionary advances in science have been given their initial expression in attractively modest terms, with no fanfare.
If somebody is working on a new medicine, computer science helps us model those things. We have a whole group here in Seattle called the Institute for Disease Modelling that is a mix of computer science and math-type people, and the progress we're making in polio or plans for malaria or really driven by their deep insights.
One might say the computer is being used to program the child. In my vision, the child programs the computer, and in doing so, both acquires a sense of mastery over a piece of the most modern and powerful technology and establishes an intense contact with some of the deepest ideas from science, from mathematics, and from the art of intellectual model building.
I think that it's extraordinarily important that we in computer science keep fun in computing. When it started out, it was an awful lot of fun. Of course, the paying customers got shafted every now and then, and after a while we began to take their complaints seriously. We began to feel as if we really were responsible for the successful, error-free perfect use of these machines. I don't think we are. I think we're responsible for stretching them, setting them off in new directions, and keeping fun in the house. I hope the field of computer science never loses its sense of fun.
I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.
I think the brain is essentially a computer and consciousness is like a computer program. It will cease to run when the computer is turned off. Theoretically, it could be re-created on a neural network, but that would be very difficult, as it would require all one's memories.
Emotions are enmeshed in the neural networks of reason.
Whatever you are studying right now, if you are not getting up to speed on deep learning, neural networks, etc., you lose. We are going through the process where software will automate software, automation will automate automation.
When you apply computer science and machine learning to areas that haven't had any innovation in 50 years, you can make rapid advances that seem really incredible.
I was never as focused in math, science, computer science, etcetera, as the people who were best at it. I wanted to create amazing screensavers that did beautiful visualizations of music. It's like, "Oh, I have to learn computer science to do that."
I just thought making machines intelligent was the coolest thing you could do. I had a summer internship in AI in high school, writing neural networks at National University of Singapore - early versions of deep learning algorithms. I thought it was amazing you could write software that would learn by itself and make predictions.
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