51–60 of 219 posts
As per Gartner - Deep Learning and ML is near the peak of the Hype Cycle, nearing trough of disillusionment.
I'm encouraged that so much fruitful work has come out of this one trick. If you can use the same basic framework for image labeling, playing Go, and translating natural languages, I'd say it's a powerful tool with broad applications. I think that there's a kernel of insight to "A real intelligence doesn’t break when you slightly change the problem." But human perception and intelligence are pretty brittle. The metho…
I feel like people have a harder time accepting these kinds of flaws if they differ wildly from mistakes a human would make.
I'm encouraged that so much fruitful work has come out of this one trick. If you can use the same basic framework for image labeling, playing Go, and translating natural languages, I'd say it's a powerful tool with broad applications. I think that there's a kernel of insight to "A real intelligence doesn’t break when you slightly change the problem." But human perception and intelligence are pretty brittle. The metho…
Of course, we're the product of an evolutionary history which results in such human "failure modes" being rare. If staring at a zebra made you hallucinate, you'd be unlikely to be the most successful member of your species, nor would your offspring thrive. So while we only tend to run into our obvious failing whens we do the unusual, computers fail at what we consider mundane.
Earlier quoted context omitted.
All AI is just objective minimization. AI cannot solve the halting problem or perform Solomonoff induction.
Neither can human AI solve the Halting Problem. It's proven unsolvable.
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…
1) learn how the brain works 2) build a simulator
most current AI research skips step 1
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.
Nature succeeded in creating human-level intelligence with one trick and no understanding, so clearly it can be done. It did take a while though. More tricks and more understanding would probably help speed things up.
And perhaps strong AI can only evolve if the agents can interact in a world that is as complicated as ours.