The role of radiologists will evolve from doing perceptual things that could probably be done by a highly trained pigeon to doing far more cognitive things.
Geoffrey HintonRead
Most people in AI, particularly the younger ones, now believe that if you want a system that has a lot of knowledge in, like an amount of knowledge that would take millions of bits to quantify, the only way to get a good system with all that knowledge in it is to make it learn it. You are not going to be able to put it in by hand.
Interpretation
Knowledge in AI systems is best acquired through learning rather than manual input.
Geoffrey Hinton emphasizes that modern artificial intelligence, especially in its more advanced forms, relies on learning from vast amounts of data rather than being manually programmed with information. This perspective reflects a significant shift in how knowledge is integrated into systems, showcasing the necessity of machine learning to achieve the complexity of understanding required for sophisticated AI applications.
In practice
In a talk about the future of technology, this quote illustrates the necessity of learning algorithms in AI.
The role of radiologists will evolve from doing perceptual things that could probably be done by a highly trained pigeon to doing far more cognitive things.
Everybody right now, they look at the current technology, and they think, 'OK, that's what artificial neural nets are.' And they don't realize how arbitrary it is. We just made it up! And there's no reason why we shouldn't make up something else.
In the long run, curiosity-driven research just works better... Real breakthroughs come from people focusing on what they're excited about.
In science, you can say things that seem crazy, but in the long run, they can turn out to be right. We can get really good evidence, and in the end, the community will come around.
I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain. That is the goal I have been pursuing. We are making progress, though we still have lots to learn about how the brain actually works.
In a sensibly organised society, if you improve productivity, there is room for everybody to benefit.
You have to immerse yourself into a product and use it in order to really understand it and that's why I have a new cellphone every month or two.
For many oppositional movements, the Internet, while providing the opportunity to distribute information more quickly and cheaper, may have actually made their struggle more difficult in the long run.
The Open Source theorem says that if you give away source code, innovation will occur. Certainly, Unix was done this way... However, the corollary states that the innovation will occur elsewhere. No matter how many people you hire. So the only way to get close to the state of the art is to give the people who are going to be doing the innovative things the means to do it. That's why we had built-in source code with Unix. Open source is tapping the energy that's out there.
Only by developing a deeper understanding of AI systems as they act in the world can we ensure that this new infrastructure never turns toxic.
On the one hand information wants to be expensive, because it's so valuable. The right information in the right place just changes your life. On the other hand, information wants to be free, because the cost of getting it out is getting lower and lower all the time. So you have these two fighting against each other.
Television is becoming a collage - there are so many channels that you move through them making a collage yourself. In that sense, everyone sees something a bit different.
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