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."

Comments

Re:"constrained by cost"

[raftpeople]

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.

Re:"constrained by cost"

[mikael]

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).

Re: But how will I trick investors!?!

[TheRaven64]

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.