AlphaGo Is Not AI

IMO there are only 2 problems in machine learning (ML):

1 Regression - Given a set of vector pairs Xi and Yi, find a function that maps each Xi to Yi minimizing some objective function. 2 Path Finding - Given some topology (typically on some regression output) find the "best" path, given some objective function.

All other problems (classification, clustering, etc) can be reposed as a combination of these two.

Viewed in this way, an "AI" system set on any reasonable test of intelligence would score super-human (defined against the average adult human) with the current set of algos, with one caveat.

If we think of operating systems in say the 90s we would mostly agree that few innovations (evolutions as opposed to revolutions) have occurred when compared to the OSs we have today. It's the development, integration, testing, tools, hardware support, etc that have taken all this time.

So, to the article's point, the AlphaGo system does these 2 things pretty well just as Windows95 did things pretty well. Yet even today I have to restart my computer every time there is the most trivial of updates.

TL;DR Super human Audio/Visual/NLP ML == AI is both here now and a long way off.

One could probably cut this to one but stating it would be very convoluted. Also "pathing" problems are typically non-convex and real-time whereas regression problems are typically not.

The term AI researchers have used to describe what you are talking about is Artifical General Intelligence. And they've made a distinction because there is no reason an advancement over human capabilities shouldn't be called AI. Sure, learning how to play Go better than any human alive doesn't mean that AlphaGo can learn and interpret languages, but that doesn't mean it's not intelligent. Even human intelligence is not homogenous in its capabilities.

It's obviously speculative, but I'm of the belief that AGI will never exist the way fanciful minds imagine it. Rather AI will evolve slowly, and systems will incorporate small advances one by one until one day we don't really care to distinguish between AI and AGI. It will literally just be a collection of capabilities ("just a computation") that are well understood and have lost all semblance of magic.

Very disappointing article indeed.

Alpha Go is an AI project with historical relevance, that had a well specified and measurable goal, which is beating humans in a game that requires to develop an intuition for moves rather than just reading moves in advance.

Having said that I don't understand what's the point of trying to delegitimize the significance of Alpha Go. Implementing what we call "intuition" is not a minor thing.

In contrast, I encountered the Pepper robot in a mall in SF. It was almost impossible to interact with it, so if there is going to be a critique, why not starting from one of his own projects.

"the capability of a machine to imitate intelligent human behavior"

That's what I would define as weak AI. So yes, even algorithms which do not adapt their behaviors by learning, but can imitate intelligent human behavior to some extent would be weak AI, such as AI in video games.

Now strong AI would be: "the capability of a machine to originate intelligent human behavior"

This means the intelligent behavior would originate from the machine itself. Some Machine Learning algorithms exhibit strong AI, in that they find ways to even outperform a human, by themselves, through the learning process. Sometimes, we can't even understand why it chose to do what it did, but it appeared to be even more intelligent than what most human would have chosen to do themselves.

Now Artificial General Intelligence, or sometimes called strong AI also, but I think there needs to be a distinction as I explain before. This would be: "the capability of a machine to originate any intelligent human behavior"

The reason a lot of people are talking about true AI, AGI, strong AI, etc. today, is because Machine Learning is seen as the be all, end all of AI. Yet practitioners and scientists know that it is not, at least in its current form. Currently, it can solve simple decision tasks, probably the kind of decisions a person makes in under a second or two, like driving. But it does not solve them generally, so a person must train different models for each tasks.

At the same time, having a way to automate decision making of a single simple task is huge in terms of potential. There's so many things we can automate through this, and if we combine them, you can create even more complex autonomous machines. That's why people are excited. Though I understand that it is not as awesome as having found general AI, or AI which can make complex decisions. The dream of AI continues, but the practicality of it are closer then ever.

Totally agree, and very happy to see this statement at the top of HN. I'm against hyping up AI or misinterpreting it just as much as the next guy (especially coming from a position where dealing with a lot of higher-management people has made me very wary of articles and vision-pieces from the other end of the spectrum that are all like "invest now in AI or lose it all"), but this is just clickbait for the sake of enjoying playing devil's advocate.

AlphaGo solved one of the hardest challenges which we believed to be very far out in the future before it came along. I'd say the field is on the right -- and very exciting -- track; people just need to learn to approach it in a clear-headed manner.

Good on IEEE though for welcoming opposing viewpoints.