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.
With our Paleolithic instincts, we're simply unable to resist technology's gifts. But this doesn't just compromise our privacy. It also compromises our ability to take collective action.
The hardest thing is to go to sleep at night, when there are so many urgent things needing to be done. A huge gap exists between what we know is possible with today's machines and what we have so far been able to finish.
The strength of the computer lies in its being a logic machine. It does precisely what it is programed to do. This makes it fast and precise. It also makes it a total moron; for logic is essentially stupid.
Technology is causing a set of seemingly disconnected things - shortening of attention spans, polarization, outrage-ification of culture, mass narcissism, election engineering, addiction to technology.
We predicted the concept of a telephone that isn't tied to a wall or a desk. We anticipated that everyone would have a cell phone. We joked that when you're born you would be assigned a cell phone and if you didn't answer you had died.
If we enter into the kind of world that Google likes, the world that Google wants, it's a world where information is copied so much on the Internet that nobody knows where it came from anymore, so there can't be any rights of authorship.
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