[nickpsecurity]

The author isn't hyperbolic. The problems that require what we call common sense continue to defeat all AI's. These board games don't require that. It comes down to pattern recognition, heuristic search, and good hardware. The examples of language and driving are great as the AI's only seem to do well on really narrow situations that require no greater understanding of context. And far as common sense, there's only a handful of projects like Cyc that were trying to teach it to machines.

Humans take 10-20 years of semi-supervised learning to acquire this combination of common sense, knowledge, and problem-solving. It also happens in stages where the infants or especially young children have brains in overdrive taking in everything followed by stages that are more selective about what they take in and solidify. Training AI's to be smart for real, common sense and all, might take over a decade of data for the first one unless this problem can be decomposed. Still will take years of similar experiences.

[computerex]

Modern AI, things like AlphaGo are examples of applied AI. "Common sense" falls within the realm of artificial general intelligence, which is a line of research that's largely abandoned now in favor of applied AI. Modern AI solutions are engineered to solve very very specific problems. You are never going to see attempts to teach "common sense".

https://arxiv.org/abs/1705.08807

With that said, the above is what the world's AI researchers think is possible hopefully within my lifetime using just applied AI without the notion of "common sense".

Common sense is AGI. That's not the goal anymore. The goal is to do things like self driving cars. Both Google and Tesla have placed vehicles on the road that have driven for literally millions of miles.

The idea is to build a bunch of classifiers and regression models and use them together in an ensemble to solve your problem. The same approach is being applied successfully to a lot of unrelated fields where deep learning is concerned.

Also, modern AI doesn't even pretend to be biological in nature, in fact we'll known researchers like Andrew Ng make a point in saying that they are only biologically inspires and that's where the commonalities end.

There are other models like HTM that are way more ambitious and want to come up with a single generalized scheme to solve a broad range of problems, AGI style. These guys think biology is important and are trying to emulate the neocortex. They ARE going for AGI, common sense, etc.

[nickpsecurity]

"The goal is to do things like self driving cars. "

Replacing a human driver takes an AGI at least for the exceptional or new situations. It's why we're including it as a counter instead of supporting point.

[computerex]

Nope, not at all. Like I said, we have had autonomous vehicles operating in normal traffic for decades now. Autonomous vehicles do not require AGI, far from it. Waymo, Uber, Tesla and more are all competing to bring autonomous vehicles to the mass consumer market and indeed most estimates claim that we'll have autonomous trucks by 2027: https://arxiv.org/abs/1705.08807

I hate how everyone thinks they know enough to talk about AI because it's so buzzy/trendy right now.

Modern AI is not pretending to be AGI. No one is claiming to be going for AGI, and whatever successes we have been seeing lately have to do with applied AI in solving specific problems, not AGI.

https://en.m.wikipedia.org/wiki/History_of_autonomous_cars

BTW did you even look at the survey? Because that's the opinion of actual AI researchers across the world.

This is easily Google able info, BTW, clearly your background is not AI.

[nickpsecurity]

Define normal traffic. Last time I read on it, the autonomous vehicles couldn't handle rain and some other weather conditions without a human operator. Then abnormal situations can still require a human operator. The AI has to correctly detect its own inadequacy and then hand ig over to human who must react in time starting out distracted by whatever they are doing. Safe, automated handling of these situations might take a broader AI thaf understands context.

Note: I'm all for getting data that shows the narrow AI's have been corrected to handle what I described.