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I think a lot of people who are emphatic that AGI is a long way off are saying so out of an allergic reaction to the hype, rather than due to sound reasoning.
by emtel 8y ago
I think a lot of people who are emphatic that AGI is a long way off are saying so out of an allergic reaction to the hype, rather than due to sound reasoning.
(And let me be clear that none of this is an argument that AGI is near. I'm saying that confidence that it is far is unfounded.)
First, there are many cases in science where experts were totally blindsided by breakthroughs. The discovery of controlled fission is probably the most famous example. This shouldn't be surprising - the reason that a breakthrough is a breakthrough is because it adds fundamentally new knowledge. You could only have predicted the breakthrough if you somehow knew that this unknown knowledge was there, waiting to be found. But if you knew that, you'd probably be the one to make the breakthrough in the first place.
Second, most claims about the impossibility of near-term AGI are totally unscientific. By that, I mean that they aren't based on a successful theory of falsifiable predictions. What we'd want, in order to have any confidence, is a theory that can make testable predictions about what will and won't happen in the short term. Then, if those predictions turn out to be true, we can gain confidence in the theory. But this isn't what we get. What we get is people saying "We have no idea how to do x, y, and z, therefore it won't happen in the next 50 years". I don't see any evidence that people were able to predict even the incremental progress we've seen say, two years out. The fact is that when someone says "it'll take 50 years" that's just sort of a gut feeling, and people will almost certainly be making that same prediction the year before it actually happens.
Third, I think people have too narrow a view about what they imagine AGI might look like. People tend to envision something like HAL, that passes Turing tests, can explain chains of reasoning, and has comprehensible motivations. Let's consider the case of abstract reasoning, which is something thought to be very difficult. We tried and failed for decades to build vision systems based on methods of abstract reasoning, e.g. "detect edges, compose edges into shapes, build a set of spatial relationships between those shapes, etc". But humans don't use abstract methods in their visual cortex, they use something a lot more like a DNN. The mistake is in thinking that because the mechanism of successful machine vision resembles human vision, therefore the mechanism of successful machine reasoning must resemble human reasoning. But its quite possible that we'll simply train a DNN by brute force to evaluate causal relationships by "magic", i.e. in a way that doesn't show any evidence of the sort of step-by-step reasoning humans use. You can already see this happening - when a human learns to play breakout, they start by forming an abstract conception of the causal relationships in the game. This allows a human to learn really really fast. But with a DNN, we just brute force it. It never develops what we would consider "understanding" of the game, it just _wins_.
Sorry the third point was so long, let me summarize: We think some things are hard because we don't know how to do them the way that we think humans do them. But that doesn't serve as evidence that there isn't an easy way to do them that is just waiting to be discovered.
- roh0sun 8y agoWorth considering these two points. Inteligence is not one dimensional as is evolution => https://backchannel.com/the-myth-of-a-superhuman-ai-59282b686c62 https://backchannel.com/the-myth-of-a-superhuman-ai-59282b68... Silicon based intelligent machines might not energy efficient => https://aeon.co/essays/intelligent-machines-might-want-to-become-biological-again https://aeon.co/essays/intelligent-machines-might-want-to-be...
- seagreen 8y agoWhy do people keep reposting that first article? I encourage you to reread it with a critical eye. A choice quote: In contradistinction to this orthodoxy, I find the following five heresies to have more evidence to support them. Intelligence is not a single dimension, so “smarter than humans” is a meaningless concept. Humans do not have general purpose minds, and neither will AIs. This is awfully similar to "On the Impossibility of Supersized Machines" (https://arxiv.org/pdf/1703.10987.pdf https://arxiv.org/pdf/1703.10987.pdf). EDIT: Re: energy efficiency: the problem is that humans are too energy efficient. Your brain can keep functioning after 3 days of running across the Savanna without food, which is (a) awesome, and (b) not really helpful nowadays. The cost of this is that you can only usefully use a little energy each day, say 4 or 5 burgers at most. AGI prototypes will usefully slurp in power measured in number of reactors.
- emtel 8y agoOn the first article, if you believe that a human is smarter than a chimp, and a chimp is smarter than a snail, then it raises the question of whether entities smarter than humans are possible. I suppose it might be difficult to precisely define a concept of intelligence that matches our intuition that humans are smarter than chimps, who in turn are smarter than snails. But such difficulty just says something about how facile you are with defining concepts. It doesn't have any bearing on how things play out in the real world. On the second point, who cares? If the first AGI draws gigawatts, it will still be an AGI.
- aligshow 8y ago> First, there are many cases in science In my experience, artificial intelligence research is based on fear, paranoia, and science fiction rather than a search for truth. It seems the so-called "researchers" are really just kicking up dust to lure venture capital to support some play time. Where are the hypotheses? Where are the problems? What math will be brought to bear? What materials science? What philosophy? What business practices? What psychological questions? In general, what parts of academia will be engaged? And what research has already been done? If artificial intelligence is related to science, then what solutions to scientific questions can artificial intelligence reveal? And which questions? No one is saying artificial intelligence illuminates every scientific question equally. Philosophers have been playing fast and loose with definitions for problems, using the so-called "problem of consciousness" or "problem of free will" to put the idea in the mind of the listener that a problem exists, is well-defined, and accepted as a problem of academic philosophy. Is artificial intelligence related to the philosophical problem of consciousness? If so, perhaps someone can start with explaining what the philosophers are talking about. https://www.ted.com/talks/john_searle_our_shared_condition_consciousness https://www.ted.com/talks/john_searle_our_shared_condition_c... Is artificial intelligence related to translation? If so, what passes for "translation science" in academia? Is there even such a thing, or has translation research been turned into a ghetto of linguistics and computer science? Is artificial intelligence related to the soul? If so, why are we asking academic researchers for their opinions, shouldn't we be asking ministers and priests and witch doctors?