141–150 of 219 posts
The most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot ne…
Finally a realistic view of where “artificial intelligence” currently stands. I wish I knew where guys like Elon Musk are seeing this other artificial intelligence I’m just not seeing. The current AI we have is just fancy linear regression.
Are you thinking Elon's concern is about what we have today? Duh. Today's AI methods are great at pattern recognition (weak AI). The real risk is obviously a strong, generic AI. We don't know how to to get there, but we kinda do know that we will, so now is the time to think about risks. I really don't get why this is controversial or hard to understand. (My most cynical thought is that it's just about people doing w…
The real risk now is not AI taking over, it's humans using AI to abuse other humans. I trust the emerging AGI to do what's right more than humans. We can't even distribute enough food for all, and many of us get killed or abused at the hands of our leaders. We can be easily bullshited in elections, and as a result we managed to put Trump in charge. That's not very intelligent for a self declared intelligent species. We are our worst enemies, AGI will probably balance human society.
Real intelligence is whatever computers can't do yet. Some think this is because we have such an impoverished grasp of intelligence, that it's only when we see a computer actually do it that we realize it doesn't really represent intelligence (logical deduction and inference, rudimentary natural language understanding, expert systems, chess, speech recognition, image recognition). Machines and tools that perform bett…
The unrestricted Turing test has been around since the 1950s as a test that hasn't changed. No one is moving the goalposts, I think it's rather the opposite. Every ten years computers learn a new trick or two and people rush to claim that this time, it's intelligent.
The most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot ne…
Earlier quoted context omitted.
> 1) learn how the brain works 2) build a simulator I disagree that step #1 is important. Consider the "Air-foil", which led to flight. In one sense, its an approximation of the wings of birds and other animals. But ultimately, the discovery that the "Air-foil" shape turns sideways blowing wind into an upward force now called "lift" is completely different from how most people understand bird wings. Bird Wings flap,…
Disclaimer: I have no expertise in AI. That said, I agree that learning how the brain works seems unimportant and unnecessary. Evolution doesn't know how a brain works, but it's given us Einstein, Michelangelo, and conversations on HN. It seems really important to learn how to build evolution into attempts at AI, given that evolution is the only known mechanism that leads to what we recognize as intelligence.
Genetic Algorithms is what you want to study.
In particular, a good, easy to read book on the subject (almost in "novel" form) is "Blondie24: Playing at the Edge of AI"
There is a bit of "can't see the forest for the trees" failure in the article. AI is spearheading a paradigm shift in how we write programs. Or rather, we don't write programs. We write much much shorter programs that search the program space for programs that satisfy some desiderata. The programs we get as the output of the search process are extremely flexible, work very well, are very homogeneous in compute (e.g.…
Is that really a better way of writing code? (for example compared to being able to reason about the code to create something provably correct)
There is a bit of "can't see the forest for the trees" failure in the article. AI is spearheading a paradigm shift in how we write programs. Or rather, we don't write programs. We write much much shorter programs that search the program space for programs that satisfy some desiderata. The programs we get as the output of the search process are extremely flexible, work very well, are very homogeneous in compute (e.g.…
Sounds like AI behaves similar to TDD where you blindly refactor until green, except AI have like a zillion more test cases(training data) to pass? Is that really a better way of writing code? (for example compared to being able to reason about the code to create something provably correct)
The most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot ne…
Great quote from Hinton. The biggest deficiency in AI is that we still don't have artificial systems which simulate human thought with any fidelity. Sooner or later that's bound to become a focus of attention.
Earlier quoted context omitted.
it's really simple 1) learn how the brain works 2) build a simulator most current AI research skips step 1
> 1) learn how the brain works 2) build a simulator I disagree that step #1 is important. Consider the "Air-foil", which led to flight. In one sense, its an approximation of the wings of birds and other animals. But ultimately, the discovery that the "Air-foil" shape turns sideways blowing wind into an upward force now called "lift" is completely different from how most people understand bird wings. Bird Wings flap,…
Not true; "lift" was well known for thousands of years, horizontal "lift" is how ships sail upwind. The breakthrough for the Wright bros was making something light enough to make use of this phenomenon vertically.