Severe limitations of AI
https://medium.com/mit-technology-review/is-ai-riding-a-one-trick-pony-b9ed5a261da0

Some passages from the article:

Neural nets are just thoughtless fuzzy pattern recognizers, and as useful as fuzzy pattern recognizers can be — hence the rush to integrate them into just about every kind of software — they represent, at best, a limited brand of intelligence, one that is easily fooled. A deep neural net that recognizes images can be totally stymied when you change a single pixel, or add visual noise that’s imperceptible to a human. Indeed, almost as often as we’re finding new ways to apply deep learning, we’re finding more of its limits. Self-driving cars can fail to navigate conditions they’ve never seen before. Machines have trouble parsing sentences that demand common-sense understanding of how the world works.

It can be hard to appreciate this from the outside, when all you see is one great advance touted after another. But the latest sweep of progress in AI has been less science than engineering, even tinkering. And though we’ve started to get a better handle on what kinds of changes will improve deep-learning systems, we’re still largely in the dark about how those systems work, or whether they could ever add up to something as powerful as the human mind.

We make sense of new phenomena in terms of things we already understand. We break a domain down into pieces and learn the pieces. Eyal is a mathematician and computer programmer, and he thinks about tasks — like making a soufflé — as really complex computer programs. But it’s not as if you learn to make a soufflé by learning every one of the program’s zillion micro-instructions, like “Rotate your elbow 30 degrees, then look down at the countertop, then extend your pointer finger, then …” If you had to do that for every new task, learning would be too hard, and you’d be stuck with what you already know. Instead, we cast the program in terms of high-level steps, like “Whip the egg whites,” which are themselves composed of subprograms, like “Crack the eggs” and “Separate out the yolks.”

Computers don’t do this, and that is a big part of the reason they’re dumb. To get a deep-learning system to recognize a hot dog, you might have to feed it 40 million pictures of hot dogs. To get Susannah to recognize a hot dog, you show her a hot dog. And before long she’ll have an understanding of language that goes deeper than recognizing that certain words often appear together. Unlike a computer, she’ll have a model in her mind about how the whole world works. “It’s sort of incredible to me that people are scared of computers taking jobs,” Eyal says. “It’s not that computers can’t replace lawyers because lawyers do really complicated things. It’s because lawyers read and talk to people. It’s not like we’re close. We’re so far.”

A real intelligence doesn’t break when you slightly change the requirements of the problem it’s trying to solve. And the key part of Eyal’s thesis was his demonstration, in principle, of how you might get a computer to work that way: to fluidly apply what it already knows to new tasks, to quickly bootstrap its way from knowing almost nothing about a new domain to being an expert.