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Wired Founding Editor Now Challenges 'The Myth of A Superhuman AI' (backchannel.com)
Wired's founding executive editor Kevin Kelly wrote a 5,000-word takedown on "the myth of a superhuman AI," challenging dire warnings from Bill Gates, Stephen Hawking, and Elon Musk about the potential extinction of humanity at the hands of a superintelligent constructs. Slashdot reader mirandakatz calls it an "impeccably argued debunking of this pervasive myth." Kelly writes:
Buried in this scenario of a takeover of superhuman artificial intelligence are five assumptions which, when examined closely, are not based on any evidence...
1.) Artificial intelligence is already getting smarter than us, at an exponential rate.
2.) We'll make AIs into a general purpose intelligence, like our own.
3.) We can make human intelligence in silicon.
4.) Intelligence can be expanded without limit.
5.) Once we have exploding superintelligence it can solve most of our problems...
If the expectation of a superhuman AI takeover is built on five key assumptions that have no basis in evidence, then this idea is more akin to a religious belief -- a myth
Kelly proposes "five heresies" which he says have more evidence to support them -- including the prediction that emulating human intelligence "will be constrained by cost" -- and he likens artificial intelligence to the physical powers of machines. "[W]hile all machines as a class can beat the physical achievements of an individual human...there is no one machine that can beat an average human in everything he or she does."
Kelly proposes "five heresies" which he says have more evidence to support them -- including the prediction that emulating human intelligence "will be constrained by cost" -- and he likens artificial intelligence to the physical powers of machines. "[W]hile all machines as a class can beat the physical achievements of an individual human...there is no one machine that can beat an average human in everything he or she does."
Given that our knowledge of the computational complexity of a single neuron is growing steadily, I think it's safe to say your FPGA cell estimate for a neuron was significantly too low.
For example, scientists now know that one single neuron (of certain types) is an entire neural network all by itself. Dendrites with multiple localized spikes communicating with each other and with other cells. Ultimately performing non-linear computation prior to forwarding any signal to cell body.
Because then you wouldn't have been saying things like:
If you've figured out AI, you go general as soon as you can, because you get everything in one box.
...when Kelly dismisses that the concept of general-purpose AI because we look at intelligence through an anthropocentric lens. "General purpose" actually isn't.
Human (or just vision in general) is the best example. It accounts for 30% of the brain capacity. At one end, you have the human eye with a retina consisting of 100 million rods and cones. Then just in that space of a 5mm disc, there are seven layers of processing used to do contrast detection between colors and intensity along with edge detection. The optic nerve takes the compressed information from a thousand areas then passes it through to the brain into two paths; one to identify where objects are, the other to determine what the objects are and their orientation. Understanding what just a single region or layer of brain cells does leads to dozens of papers being published and advances in digital photography (image stabilization, motion correction).
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This seems to be an example of some kind of unbounded technological/scientific optimism that disregards the fact that during that history you're using as proof, we have also refined an understanding of physical limits that have not fundamentally changed. Think about laws of thermodynamics or the speed of light as a hard limit, among other things. We are not getting around those any time soon.
Of course if you're counting on a complete revolution of Physics, you're going to need "extraordinary evidence" to overturn a lot of what we already know. This is a tall order; even the theory of relativity and quantum mechanics do not do things like totally overturn Newton's ideas in our everyday life. You can't just expect these kinds of things to happen.
Then there is just some weirdness in the post...
What? The laws of physics have always had to be testable, otherwise you're just doing math. This is the reason the LHC was built, to be an experimental instrument. I do not understand the point about photons and gravitons; the former is a well-known quantum, the latter is theoretical. So far we haven't been able to quantize gravity.
Yeah, and time is a cube, eh?
No, limitations probably still are limitations, even when you develop a better understanding of what is going on. Stuff will fall down even tomorrow, even if you could demonstrate that you can quantize gravity. Getting around strongly established phenomena by better explanations would mean there is some until now completely non-observed part of the world we could exploit. This rarely happens so that what didn't work today, magically starts working tomorrow.
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Except that the claims of strong AI 'real soon now' have been coming since the '60s. Current AI research is producing things that are good at the sorts of things that an animal's autonomic system does. AI research 40 years ago was doing the same thing, only (much) slower. The difference between that and a sentient system is a qualitative difference, whereas the improvements that you list are all quantitative.
Neural networks are good at generating correlations, but that's about all that they're good for. A large part of learning to think as a human child is learning to emulate a model of computation that's better suited to sentient awareness on a complex neural network. Most animals have neural networks in their heads that are far more complex than anything that we can build now, yet I'm not seeing mice replacing humans in most jobs.
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"Its"
Fucking retard can't spell. I stopped reading right there.
I agree how some people can lack perspective and not see the big picture. We got planes, we got cars and ships, we got wireless communication, we can send shit into space. And we had all the same shit 50 years ago, as well as in 1970. Douche basement nerds can take surface details and call that a huge improvement. The rest of us have what I like to call perspective. You're not even smart enough for propper grammer, so I wouldn't expect perspective from you. Moron.
Given that our knowledge of the computational complexity of a single neuron is growing steadily, I think it's safe to say your FPGA cell estimate for a neuron was significantly too low. For example, scientists now know that one single neuron (of certain types) is an entire neural network all by itself. Dendrites with multiple localized spikes communicating with each other and with other cells. Ultimately performing non-linear computation prior to forwarding any signal to cell body.
Right you are. The absolute give-away (in addition to the ridiculous low-ball answer he provided) was "... that was pretty straightforward..." which shows the Dunning-Kruger Effect in full bloom. He had no idea now little he knows about the subject.
The example I like to use to illustrate how much smoke is being blown about this my tech types is the model organism Caenorhabditis elegans. This 1 mm long nematode has had every one of its 302 neurons in its nervous mapped out, including all connections to every other neuron, as well as the process of development from the initial fertilized egg - we have mapped out exactly how the nervous system develops (indeed every one of the 959 cells in its body have been similarly traced out).
Given this complete map of C. elegans nervous system we must have a spiffy computer of the little worm's "brain" able to replicate its behavior? Right?
Not even close. So far we cannot accurate model the behavior of even a single neuron in C. elegans. Even one single neuron represents computational complexity that we are still trying to understand.
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