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Apocalypse or golden age: What machine intelligence will do to us

Apocalypse or golden age What machine intelligence will do to us VentureBeat Science by Patrick Ehlen Loop AI Labs


There is no doubt that we are in the middle of a quantum leap in machine intelligence (to the extent that you can be “in the middle” of anything quantized), though history leads many to be skeptical.

The field of AI research has seen fits and starts of excitement since its earliest days, when, in 1949, McCulloch & Pitts developed the first “artificial neuron” algorithm that could learn the logical concept of “or” from a few simple examples, without any human programming.

Contemporaneously, researchers at MIT and elsewhere developed symbolic processing approaches to intelligence that showed specialized algorithms could do many of the things we thought only humans could do, like playing games and engaging in simple conversations. Both approaches initially showed a great deal of promise, and suddenly the future seemed clear. “General artificial intelligence” was just around the corner, and machines were coming that would think and learn on their own, make interesting conversation during long, interplanetary travels, and hopefully not go haywire and lock us out in the cold vacuum of space.

But the cold vacuums that ensued turned out to be of our own making, and giddiness turned to aversion when these two parallel fields of AI research — neural networks and symbolic processing — both failed to live up to their promises. “AI Winters” ensued in the 1970s and again in the late 1980s-1990s, where funding dried up despite isolated successes in areas like expert systems (for the symbolic approach) and neural nets that proved handy for automatically reading zip codes and scratching people’s names and phone numbers into early PDAs.

The renaissance we see today owes primarily to continued developments in both of these fields from researchers who refused to say die despite being ignored for years by almost everyone.

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I remember the AI winter of the 1980s and 1990s. Most computer science majors were forced into systems because there wasn't much grant money for AI. Probably delayed the AI renaissance. 

Then again, we needed compute power to catch up to really make the next breakthroughs. 

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