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Comment by rjromero | Hacker News Reader

Finally a realistic view of where “artificial intelligence” currently stands. I wish I knew where guys like Elon Musk are seeing this other artificial intelligence I’m just not seeing. The current AI we have is just fancy linear regression.

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Sorry but that's a ridiculous statement. It's like saying all the cloud technology we have today is not much more than having VMs. Yes, AI is hyped, but it made some very unexpected progress over the past 5 years (e.g., solving facial recognition). More than many experts expected.

Can you please define this term "cloud technology"? I always thought it was a marketing term for "the internet", which then would be just a bunch of servers, vms, and containers; all of which we have had for over 20 years.

"cloud technology" is "servers, VMs, containers" that is also SER (Somebody Else's Responsibility). Hence the term "cloud", as that was always used in network diagrams to refer to network connection infrastructure that wasn't yours.

What is novel about "the internet"? We've been sending data over telegraph wires for nearly 200 years. The Internet is just someone else's cable.

That was my point, you just went back further in history than I did :p

Then your point is wrong. What's so novel about CPUs? We've had sand since the Earth was first formed!

I swear the tech industry has more luddites than the Amish.

The Amish are not luddites.

They are not capital-L official Luddites, but they could be classified as colloquial lowercase-L luddites.

Packet switching and distributed routing protocols.

It's turtles all the way down to the Big Bang.

Since when are cables such a unique idea? The telegraph is nothing more than a large number of fancy carrier pigeons as a service, which we've had since the 12th century at least.

"It's like saying all the cloud technology we have today is not much more than having VMs."

No shit? Unless you mean that it's ignoring the "distributed systems" part of the cloud, which is mostly a shitshow. The provisioning/configuration-management stacks are all complete wrecks, stacking hacks atop ad-hoc container schemes atop a poorly-design OS. A real distributed OS would be so much simpler and more robust.

Calling it AI is very misleading when there has been minimal progress toward actual reasoning (the closest I've seen being some LSTM work on answering simple queries about scenarios based on prose descriptions of them). It's ML, as in "learning a function", not as in "general-purpose learning like an intelligent agent does".

https://en.wikipedia.org/wiki/AI_effect

Got any links to info about distributed OSes?

Nope. Linear regression will not reconstruct images, nor will it create and generate new images that have not existed previously from databases of images. Nor will it translate colloquial Chinese into colloquial English, nor will it deconstruct a question into a set of queries and generate an answer.

The issue isn't the availability of fantastic algorithmics, it's the availability of people who can see them and apply them to real applications.

In fact linear regression could be used to do all of those things, most of them poorly but some of them pretty well (see dictionary learning). If you believe otherwise then you fundamentally misunderstand how neural networks work.

Ohhh - so the multilayer perceptron xor thing is all wrong?

I can use bricks to build a boat, if you think I can't then you don't understand floating.

>I can use bricks to build a boat, if you think I can't then you don't understand floating.

Is this supposed to be sarcastic? Because it's absolutely true...

I think the point is that you can build a boat out of bricks, and you can build neural networks out of linear regression. But that doesn't make modern AI "just fancy linear regression" the same way modern boats aren't "just fancy bricks".

The current AI seems to be pretty good at pattern recognition. There’s plenty of active research in higher-level “cognition” like decision theories and semantics, so I don’t think it’s crazy to believe good results will emerge there in the next few decades.

Sure, there is no other way for computer science to go. AI always been the last and most important frontier. There may be more AI winters, there may be more AI summers. Ultimately people will continue working on this problem until it is solved or computer science goes away.

>There’s plenty of active research in higher-level “cognition” like decision theories and semantics, so I don’t think it’s crazy to believe good results will emerge there in the next few decades.

Interesting. Can someone elaborate about this?

I would start by looking at what these people are researching:

http://cocosci.mit.edu/people

https://web.stanford.edu/group/mbc/research.html

The current computers we have are just fancy compound gate machines.

All AI is just objective minimization. AI cannot solve the halting problem or perform Solomonoff induction.

> AI cannot [...] perform Solomonoff induction.

Well it's clearly got to approximate it somehow, else humans wouldn't currently be making decently accurate predictions about the future.

You're making an assumption that is not necessarily true.

Neither can human AI solve the Halting Problem. It's proven unsolvable.

It's proven unsolvable for computers (i.e., Turing machines). Whether that makes it impossible for humans to solve (as a matter of course, or as a matter of Turing's proof) depends on whether you believe that we are isomorphic to computers. Which, to be fair, is exactly the belief that underwrites artificial intelligence. But then Turing's proof involves a program that inspects the halting oracle you're using, which isn't well-posed for humans evaluating halting.

Are you thinking Elon's concern is about what we have today?

Duh. Today's AI methods are great at pattern recognition (weak AI).

The real risk is obviously a strong, generic AI. We don't know how to to get there, but we kinda do know that we will, so now is the time to think about risks.

I really don't get why this is controversial or hard to understand. (My most cynical thought is that it's just about people doing weak AI feeling bothered and trying to kill the conversation out of short term strategical reasons because they know that many people are bound to mix up the concepts of weak and strong AIs, particularly since they are often selling their weak pattern matching AI systems as something quite a lot more.)

To make an analogy, it's like starting the discussion on plane based terrorism and the need for TSA the next week after the Wright brothers had their first flight. If that happened, it would have stalled progress in aviation. Andrew Ng said he isn't worried about overpopulation on Mars, either.

The real risk now is not AI taking over, it's humans using AI to abuse other humans. I trust the emerging AGI to do what's right more than humans. We can't even distribute enough food for all, and many of us get killed or abused at the hands of our leaders. We can be easily bullshited in elections, and as a result we managed to put Trump in charge. That's not very intelligent for a self declared intelligent species. We are our worst enemies, AGI will probably balance human society.

Some terrorism might have indeed been prevented if some of the security measures had been put in place earlier. E.g. locked cabin doors. I could make a similar argument like yours, "that's like worrying about nuclear safety programs just because we started building nuclear weapons".

Not that I'm saying it's worth it, mind you. It's a tradeoff between money/time/progress vs. safety. It's totally valid to decide that it isn't worth the tradeoff to stifle the industry right now.

The main difference though between the potential dangers of AI, vs. terrorism, IMO, is that the potential danger is much much larger with AI (as it was with nuclear weapons).

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