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.
I like going to Burning Man, for example. An environment where people can try new things. I think as technologists we should have some safe places where we can try out new things and figure out the effect on society. What's the effect on people, without having to deploy it to the whole world.
Usability is not everything. If usability engineers designed a nightclub, it would be clean, quiet, brightly lit, with lots of places to sit down, plenty of bartenders, menus written in 18-point sans-serif, and easy-to-find bathrooms. But nobody would be there. They would all be down the street at Coyote Ugly pouring beer on each other.
Social capital may turn out to be a prerequisite for, rather than a consequence of, effective computer-mediated communication.
The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.
Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.
I hope to literally change the world with Black Girls Code by changing the paradigm which produces the current monolithic ecosystem in technology.
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