Does Knapp know anything about the way existing AI works? Itâ€™s not based around trying to copy humans, but often around improving this abstract mathematical quality called inference.
I think you missed my point. My point is not that AI has to emulate how the brain works, but rather that before you can design a generalized artificial intelligence, you have to have at least a rough idea of what you mean by that. Right now, the mechanics of general intelligence in humans are, actually, mostly unknown.
Whatâ€™s become an interesting area of study in the past two decades are two fascinating strands of neuroscience. The first is that animal brains and intelligence are much better and more complicated than we thought even in the 80s.
The second is that humans, on a macro level, think very differently from animals, even the smartest problem solving animals. We havenâ€™t begun to scratch the surface.
Based on the cognitive science reading I’ve done up to this point, this is false. Every year, scientists discover cognitive abilities in animals that were previously thought to be uniquely human, such as episodic memory or the ability to deliberately trigger traps. Chimps have a “near-human understanding of fire” and complex planning abilities. Articles such as this one in Discover, “Are Humans Really Any Different from Other Animals?”, and this one in New Scientist, “We’re not unique, just at one end of the spectrum” are typical from scientists who compare human and chimp cognition. It’s practically become a trope for the (often religious) person to say humans and animals are completely different, and the primatologist or cognitive scientist to say, “not nearly as much as you think…”
One primate biologist says this:
“If we really want to talk about the big differences between humans and chimps â€” they’re covered in hair and we’re not,” Taglialatela told LiveScience. “Their brains are about one-third the size of humans’. But the major differences come down to ones of degree, not of kind.”
There’s a really good paper somewhere out there on cognitive capacities in humans and chimps and how human cognitive abilities seem to be exaggerations of chimp abilities rather than different in kind, but I can’t find it.
Arguments that chimps and humans are fundamentally different tend to be found more often on Christian apologetics sites than in scientific papers or articles. The overall impression I get is that scientists think chimp cognition and human cognition are different in degree, not in kind. There are humans out there so dumb that chimps are probably more clever than them in many important dimensions. Certainly if Homo heidelbergensis and Neanderthals were walking around, we would have even more evidence that the difference between humans and chimps is one of degree, not kind.
Another point is that even if humans were radically different in thinking than animals, why would that automatically mean AI is more difficult? We already have AI that utterly defeats humans in narrow domains traditionally seen as representative of complex thought, no magical insights necessary.
Yet another possibility is one of AI that very effectively gathers resources and builds copies of itself, yet does not do art or music. An AI that lacks many dimensions of human thought could still be a major concern with the right competencies.
But before scientists knew anything about birds, we basically knew: (a) they can fly, (b) it has something to do with wings and (c) possibly the feathers, too. At that stage, you couldnâ€™t begin to design a plane. Itâ€™s the same way with human intelligence. Very simplistically, we know that (a) humans have generalized intelligence, (b) it has something to do with the brain and (c) possibly the endocrine system as well.
I should think that many tens of thousands of cognitive scientists would object to the suggestion that we only know a “few basic things” about intelligence. However, it’s quite subjective and under some interpretations I would agree with you.
The above paragraph is a vast oversimplification, obviously, but the point is to analogize. Right now, weâ€™re at the â€œwings and feathersâ€ stage of understanding the science of intelligence. So I find it unlikely that a solution can be engineered until we understand more of what intelligence is.
The impression that one has here probably correlates with how much cognitive science you read. If you read a lot, then it’s hard not to think of all that we do know about intelligence. Plenty is unknown, but we don’t know how much more needs to be known to build AI. It could be a little, it could be a lot — we have to keep experimenting and trying to build general AI.
Now, once we understand intelligence, and if (and I think this is a big if), it can be reproduced in silicon, then the resulting AGI probably doesnâ€™t necessarily have to look like the brain, anymore than a plane looks like a bird. But the fundamental principles still have to be addressed. And weâ€™re just not there yet.
Yet formalisms of intelligence, like Solmonoff induction, are not particularly algorithmically complicated, just computationally expensive. Gigerenzer and colleagues have shown that many aspects of human decision making rely on “fast and frugal heuristics” that are so simple they can be described in pithy phrases like Take the Best and Take the First. Robyn Dawes has shown how improper linear models regularly outperform “expert” predictors, including medical doctors. Rather than possessing a surplus of cognitive tools for addressing problems and challenges, humans seem to just possess a surplus of overconfidence and arrogance. It is easy to invent problems that humans cannot solve without computer help. Humans are notoriously bad at paying attention to base rates, for instance, even though base rates tend to be the most epistemologically important variable in any reasoning problem. After you read about many dozens of experiments in heuristics and biases research where people embarrass themselves in spectacular fashion, you start to roll your eyes a bit more when people gloat about the primacy of human reasoning.
I correspond with lots of neuroscientists. Virtually all of them tell me that the big questions remain unanswered and will for quite some time.
I correspond with neuroscientists who believe that the brain is complex but that exponentially better tools are helping quickly elucidate many of the important questions. Regardless, AI might be a matter of computer science, not cognitive science. Have you considered that possibility?
AIXI is a thought experiment, not an AI model. Itâ€™s not even designed to operate in a world with the constraints of our physical laws.
Sure it is. AIXI is “a Bayesian optimality notion for general reinforcement learning agents”, a yardstick that finite systems can compare against. It may be that the only reason our brains work at all is because they are approximations of AIXI.
My point is to recognize that the way machine intelligence operates, and will for the conceivable future, is in a manner that is complementary to human intelligence. And Iâ€™m fine with that. Iâ€™m excited by AI research. I just find it unlikely, given the restraints of physical laws as we understand them today, that an AGI can be expected in the near term, if ever.
“If ever”? You must be joking. That’s like saying, “I just find it unlikely, given the restraints of physical laws as we understand them today, that a theory of the vital force that animates animate objects can be expected in the near term, if ever”, or “I just find it unlikely, given the restraints of physical laws as we understand them today, that a theory of aerodynamics that can produce heavier-than-air flying machines can be expected in the near term, if ever”. Why would science figure out how everything else works, but not the mind? You’re setting the mind apart from everything else in nature in a semi-mystical way, in my view.
I am, however, excited at the prospect of using computers to free humans from grunt work drudgery that computers are better at, so humans can focus on the kinds of thinking that theyâ€™re good at.
To be pithy, I would argue that humans suck at all kinds of thinking, and any systems that help us approach Bayesian optimality are extremely valuable because humans are so often wrong and overconfident in many problem domains. Our overconfidence in our own reasoning even when it explicitly violates the axioms of probability theory routinely reaches comic levels. In human thinking, 1 + 1 really can equal 3. Probabilities don’t add up to 100%. Events with base rates of ~0.00001%, like fatal airplane crashes, are treated as if their probabilities were thousands of times the actual value. Even the stupidest AIs have a tremendous amount to teach us.
The problem with humans is that we are programmed to violate Bayesian optimality routinely with half-assed heuristics that we inherited because they are “good enough” to keep us alive long enough to reproduce and avoid getting murdered by conspecifics. With AI, you can build a brain that is naturally Bayesian — it wouldn’t have to furrow its brow and try real hard to obey simple probability theory axioms.