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Comment by Animats | Hacker News Reader

We still have a huge gap in the middle between symbolic AI and machine learning. Basically, what the cerebellum does - manage short-term activity. "Common sense" at the low level.

Not much going on in this area. Which is why robot manipulation in unstructured situations still sucks, after 50 years.

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It makes sense. Machine learning can only learn what it has already seen. Combining with connectivism from a neural network, it may come up with brand new concepts through linking existing ideas and elements. But since these new concepts are also new to itself as well, would the machine know how to interpret it correctly? Connectivism alone may help keep generating new links and connections, but to interpret those new elements correctly we would need something more than just a neural network.

Remember there is a very fine line between creativity and illusion. Technically there is not much of a difference because both are a distortion of reality. Even we humans have a hard time distinguishing this sometimes as many new inventions used to be deemed crazy at first. It looks like when it comes to concepts that we hadn't seen, we were also susceptible to the same flaw just like those machines.

I don't really know much about symbolic AI but can it really solve this problem? If it can then the human game may really be over!

Granted I have the naivety of someone who doesn't work in the field, but I think the gap was recently narrowed, no? The resemblance of the modern 4-layer LSTMs to traditional LL(K) parsers had me on the floor for days.

That's interesting, I did some traditional parsing work a dozen years ago, but had not noticed any obvious similarities like the one you mention, can you elaborate on that resemblance?

The biggest breakthrough in AI will come when someone builds a robot that can fold a shirt.

Yeah someone made a big claim about it being increadibly hard for robots to deal with cloth. It's true it is tricky, but the shirt folding problems has been solved for a while.

https://foldimate.com/

https://www.theverge.com/2018/1/10/16865506/laundroid-laundr...

So in the video, the user still had to manually insert each piece of clothing into the machine individually. I would have just folded it myself at that point and it would have been quicker too. I thought the whole point was for human not to have bother with this meaningless non-value added task.

"The Foldimate demo I saw wasn’t actually a fully working prototype, meaning clothes-folding technology wasn’t all there yet."

So not quite solved just yet. And I'll give you long odds against them working out the kinks in the next five years.

A general purpose cloth folder is difficult, but standardized pieces are covered.

https://youtu.be/OpiFp9i6owY?t=209

It's tough, but the industrial laundry business more or less has this working. Not much AI, lots of compressed air.

1. Robes and sheets != shirts

2. Human operators are doing the heavy lifting (quite literally) by loading the pieces one by one in a standardized initial state.

I'm talking about a machine that will fold men's shirts that are initially sitting in a pile just as they came out of the dryer.

It's quite astounding when you think about how much of our life has been automated or made virtual. But basic house hold tasks like cooking, dishes, tidying up, folding clothes are still fully manual. Also shopping for food, vacuum or tending your garden (for the majority of us). Tasks which all together take many hours each week.

Think about all the time going to waste. Yes, there are tasks which some actually want to keep manual (maybe you love cooking) and there are (subpar) online service, which try to replace that labor, but there is no automatic option otherwise. And outsourcing labor is not what I mean. I want a washing machine at home, which is a lot simpler to handle than outsourcing it to a washing business.

I sometimes wonder, if the reason is really a technical one or if there are other factors, social or incentive problems, which hold progress back in those areas...

When it comes to folding shirts it really is a technological limit. Folding shirts just turns out to be incredibly hard.

Try folding a shirt with mittens on some time to get some idea of what a robot with a standard manipulator would have to deal with.

Here's a Willow Garage PR2 doing it.[1] Very, very slowly.

[1] https://youtu.be/YdStbvguvwU

I don't want to denigrate this effort in any way, because this robot is very cool and represents real progress. But that's not a shirt.

I mean, I'm not very good at folding shirts anyway.

Maybe the solution is lower standards?

Reminds me of some story about a person doing something and another person saying "that looks painful, what's the trick", with the answer being "not minding that it hurts".

I consider true AI is possible only when it is able to read through an entire novel or book and then provide an accurate summary of its story or plots that not only makes sense, but correctly conveys the emotional aspect of those books. Remember a lot of meanings or sentiments from the stories in those books are implicit, and so they do require a certain level of emotional processing to interpret it correctly.

Wasn't there a laundry folding machine released not too long ago? It flopped though.

I think the recent moves in game playing AI (e.g. AlphaGo, Pluribus) are beginning to bridge that gap. Specifically, the use of concrete tree search algorithms (e.g. MCTS, Minimax) combined with neural networks to intelligently guide the search.

I suspect this sort of algorithm will be applied to robot manipulation in the next couple of years, and would be extremely surprised if DeepMind et al weren't already working on it.

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