Comment by nickdavidhaynes | Hacker News Reader

Comment by nickdavidhaynes | Hacker News Reader

nickdavidhaynes

While I definitely agree that advances in ML in the last 20 extremely important (and potentially revolutionary), I think this article misses the mark in a few places.

>Now, instead of humans designing algorithms to be executed by a computer, the computer is designing the algorithms. (Albeit guided by human-devised algorithms)

This line is way off in both tone and substance. On tone, it really underplays the human effort involved in effective machine learning (as it is practiced in 2017) and anthropomorphizes "machines" to an unreasonable extent. In substance, I fail to see how a machine that "designs its own algorithms" according to an algorithm designed and implemented by a human is fundamentally different than an algorithm coded directly by a human. To use the author's example, machine learning allows humans to build complex software systems in less time just as a bicycle allows humans to cover more distance with less energy. It's a big improvement, but it's not, say, teleportation.

>it is only now that the machines are creating themselves, at least to a degree. (And, by extension, there is at least a plausible path to general intelligence)

I could not disagree more strongly with this addendum. Simply put, I fail to see any path from state-of-the-art ML/DL research today to AGI, and I would even go so far as to say that humans have made approximately zero progress on this task since it was first formulated in the 50s. I think we know about as much about "intelligence" (and consequently, what would constitute AGI) as star-gazers in ancient times know about the universe. That's not to say that it will take millennia to invent AGI, but the path to get there is probably quite orthogonal to modern ML research.

Simply put, I fail to see any path from state-of-the-art ML/DL research today to AGI

Before I really understood and worked with NN, I felt the same way. I thought the atomspace computation approach and other similar granular computation paradigms were much more likely to make progress.

However after seeing the striking similarities between how I watched my three kids learn from infant -> toddler ages and how we build our convolutional neural nets in my company, it was like a light went on.

If you look at how relatively sparse and weak even the best deep nets are compared to human brains, especially considering a really narrow set of inputs - we are at the very early beginnings of mimicking the complexity of the human brain. It seems to me that the ANN approach is right, we now need to make it radically more efficient and give it better input sensors.

We need a nervous system for AGI (structured data acquisition) before the big brain tasks will be solved.

I think that when people talk about "AGI" what they often mean is artificial personality.

Sure, your NN learns facts and processes like your toddler learns facts and processes. Those are a tiny part of who your toddler is, though.

The essential component is their will. You don't have to set them up and feed them data. They don't sit quietly until you ask them to answer a question. Kids have distinct personalities from very early on, and demand input, and produce opinionated output (to put it mildly)--from day one.

Emotions are a huge part of that. But to my knowledge, we have less understanding of emotions, and spend less time trying to create them with computers, than conscious processes like "which picture has a car in it."

But there is evidence that if you take away a person's emotions, they have great trouble making decisions. They can consciously evaluate their options. They just struggle to pick one.

So how will AI research focused on replicating conscious thought result in AGI, if we don't know how to generate emotions? Is anyone even trying to do that?

My standard joke is that a lot of people are working to create a car that can drive itself, but who is investing to build a car that will tell its owner, "fuck off, I don't feel like driving today"?

But can a machine that always does exactly what it is told to do really be thought of as "intelligent" the way we think of human intelligence? Do smart people always do exactly what they are told?

At a guess there are two major schools of thought here. The first thinks that emotions, will, personality etc are much more complex than the way we think of neural nets today. The other thinks that what we are seeing is already much more like the brain than we were expecting and if we continue down this path we may discover those things are emergent aspects of much simpler behaviours at a small scale.

My slightly optimistic money is on the latter one.

What you call will is no different in my mind than any other thing we encode into a NN - it's a different level and depth.

Creating motivation in AI is an open area, and in fact is arguably the big hairy beast when it comes to the "Friendly AI" question or really the whole "General" part of it.

You do the same thing everyone else does in this debate which is move the poles - we don't know how to build "emotions", we don't know how to build motivation - until we do or it is perhaps an emergent property of a sufficiently deep net.

Too many other strawmen in there to argue eg. the idea that we will need always tell them what to do.

The point I am making is that because the reinforcement nature of biological systems is mimicked in the basic ANN structure, it's the strongest candidate (at scale) for the building blocks of an AGI.

I interpreted the point of the article as being that we tend to focus too much on AGI and not enough of how disruptive "narrow" AI may be. The only thing I disagree with there is that I think lots of people focus a lot on the potential issues of broadening use cases for ML. But I agree that AGI is mostly just a distraction to that discussion.

Agreed all around. At the very least, I don't think it was obvious what message the author was trying to convey.

> I fail to see how a machine that "designs its own algorithms" according to an algorithm designed and implemented by a human is fundamentally different than an algorithm coded directly by a human

Human creativity can be empowered by optimization algorithms. It's a huge improvement over design by hand.

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