A Quote by Geoffrey Hinton

The question is, can we make neural networks that are 1,000 times bigger? And how can we do that with existing computation? — © Geoffrey Hinton
The question is, can we make neural networks that are 1,000 times bigger? And how can we do that with existing computation?
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.
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 get very excited when we discover a way of making neural networks better - and when that's closely related to how the brain works.
All physical systems can be thought of as registering and processing information, and how one wishes to define computation will determine your view of what computation consists of.
Health care - the ability of neural networks to ingest lots of data and make predictions is very well suited to this area, and potentially will have a huge societal impact.
Emotions are enmeshed in the neural networks of reason.
We have no proper understanding of the relationship between conscious thought and conscious sensation. The various forms of thought and sensation are underpinned by very different neural mechanisms; so how can the neural correlate of their conscious natures be the same? I don't think we are yet in a position to make such speculations. To make progress, we have to have a good conception of the phenomenology of consciousness, among other things.
Deep neural networks are responsible for some of the greatest advances in modern computer science.
There are neural networks that can build whole apps from scratch - so why are we teaching high school kids to code?
The pooling operation used in convolutional neural networks is a big mistake, and the fact that it works so well is a disaster.
The question is: how you cross uneven ground, how you assemble networks around you.
The number of people on whose cooperative efforts your eventual existence depends has risen to approximately 1,000,000,000,000,000,000, which is several thousand times the total number of people who have ever lived.
We plutocrats need to get this trickle-down economics thing behind us: this idea that the better we do, the better everyone else will do. It's not true. How could it be? I earn 1,000 times the median wage, but I do not buy 1,000 times as much stuff, do I?
My particular focus at the moment is on the development of genetic algorithms and neural networks that work together to create computer architectural systems.
Neurogenesis continues throughout life and we have the capacity to establish new neural pathways and strengthen existing ones.
BitCoin is actually an exploit against network complexity. Not financial networks, or computer networks, or social networks. Networks themselves.
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