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Comment by romaniv | Hacker News Reader

Comparing comments here to the comments on, say, first Alpha Go post, reveals the amazing amount of AI bias on this website.

When an expert system beats some human in a complex real-life problem the comments are about how it is narrow, boring, not sufficiently tested and ultimately doesn't matter.

When a neural network (with the help of MCTS and an entire data-center full of servers) beats some human in a board game the comments here hype it through the roof and jump to conclusions about the coming dawn AGI.

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>> Comparing comments here to the comments on, say, first Alpha Go post, reveals the amazing amount of AI bias on this website.

I think it's because only recently has AI been in the news again and it's been the news thanks to machine learning and neural networks in particular (and more specifically, deep learning). The last time there was a big todo about AI was in the '90s and most people writing here were probably not old enough to figure out what the hell was up back then.

Er, I'm not dating myself here. I know about those things out of happy accident (maybe a story for another time). Most people who graduated from CS in the last five or six years will not have heard anything about expert systems and GOFAI, except that it failed etc, if that.

Then along comes Google and promises it can make your phone talk to you. People are intrigued.

But of course, those who don't know their history are doomed to repeat it.

I'd guess there's a difference in kind that people get excited about.

The fighter jet AI technique is hard-coded to a very specific problem domain, and could only be reproduced in a different problem domain by doing it from scratch.

The technique used by AlphaGo is at least closer to the idea that we can eventually build generically trainable machines that can learn to do a variety of tasks, without having to code them from scratch every time.

>> The fighter jet AI technique is hard-coded to a very specific problem domain

So are machine learning models, ultimately. Although the algorithms that build the models have more general application, once you train a model on a certain set of data, that model can only ever be used in the domain circumscribed by the data. They're one-trick ponies, yes?

>> we can eventually build generically trainable machines that can learn to do a variety of tasks, without having to code them from scratch every time.

We would however have to train them from scratch every time, for each separate task, and each time we'd need terabytes of data and megawatts of power.

You realize that this system uses machine learning? And that AlphaGo was custom-built to play Go?

AlphaGo has several handcoded training features, uses Monte-Carlo tree search, and was primed by a huge human-made dataset. It's not like it can be throws at other problems without heave re-engineering.

For me personally, this is less exciting because AI jet pilots have a natural advantage.

Human pilots are limited in maneuvering by the amount of force their body can take. Computer pilots are limited by the amount of force the airframe can take. The later allows much more aggressive and radical maneuvers.

That means, compared to playing Go, the robot pilot can be comparatively much worse than the human and still win decisively by taking advantage of high g maneuvers.

The test were done in a simulation. Also, if you want to run high-G maneuvers, you can always control a drone from the ground.

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