11–20 of 92 posts
The hardware is now here but the algorithms are not. A crow knows not to land on sharp nails without ever having any experience stepping on one. Current architectures lack this basic intuition. Something is missing. Probably an internal world model or simulation
This is a common sentiment, and pundits have been making similar remarks for decades. This author writes "Sixty years later, however, high-level reasoning and thought remain elusive." That's the wrong problem with AI. The trouble with AI is that it still sucks at manipulation in unstructured situations and at "common sense". Common sense can usefully be defined as getting through the next 30 seconds of life without a…
I can only speculate but it certainly is for a reason that certain parts of our body are vegetativelly controlled whereas others are under the active control of our consciousness.
If you step on a nail, the first reaction comes from vegetative stimulus, later your consciousness processes that information. A squirrels neuronal network is also separated in that way. That may be a reason.
And second, AFAIK AI still doesn't 'think' in concepts, it has no notion of the 'world'.
And third: The capability of reproduction and acting accordingly may be another thing.
>"However, the current focus on doing AI research via the gathering of data, the deployment of “deep learning” infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills — with little in the way of emerging explanatory principles — tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems" I'd…
GOFAI = "Good old fashioned AI" for those not familiar with the acronym
If GOFAI includes semantic reasoning in real world concepts modeled eg. with theasauri and concept maps - I think AI research was on the right track but went astray as there was not enough resounding business success to warrant further funding.
The hardware is now here but the algorithms are not. A crow knows not to land on sharp nails without ever having any experience stepping on one. Current architectures lack this basic intuition. Something is missing. Probably an internal world model or simulation
or perhaps just increased computing power
>"However, the current focus on doing AI research via the gathering of data, the deployment of “deep learning” infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills — with little in the way of emerging explanatory principles — tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems" I'd…
Sure, because it's already a training input. We'd run because we recognize the axe, the signs of aggression, the horror movie trope of an evil clown, and so forth. We have to teach "stranger danger" to children.
"There's no agency, liveliness, autonomy or learning in a dynamic real-time way to any of the systems we have, they're for the most part just static, 'flat', machines."
Well, that's at least in part because we design them that way. It's more convenient to separate out the "learning" and "doing" parts so we have control over how the network is trained.
We already have artificial intelligence. It’s called children
I think this essay includes a specific prediction, that human level ai is far away, that might be disproved this decade. If human level ai is close, focusing on some other kind of ai is more likely to be a waste of time.
This is a common sentiment, and pundits have been making similar remarks for decades. This author writes "Sixty years later, however, high-level reasoning and thought remain elusive." That's the wrong problem with AI. The trouble with AI is that it still sucks at manipulation in unstructured situations and at "common sense". Common sense can usefully be defined as getting through the next 30 seconds of life without a…
There's an old (and sometimes forgotten) idea in AI that perhaps things we think are simple, like vision and control (robotics), are actually incredibly complicated and took millions of years to evolve.
Whereas things we think are complicated, like playing Go or picking stocks or computer programming, are actually quite simple to learn.
This would be counter-intuitive but---as you observed, and taking my argument recursively---common sense might be much more difficult to get right than obscene pathological thinking.
Anyway, I've always thought a good startup would be to automate away Silicon Valley using AI. It's so punk rock that a lot of disillusioned smart techies would join under this banner. A collaborator of mine has already used AI to do high-level bug finding in blockchain code.
The hardware is now here but the algorithms are not. A crow knows not to land on sharp nails without ever having any experience stepping on one. Current architectures lack this basic intuition. Something is missing. Probably an internal world model or simulation