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Comment by hharrison | Hacker News Reader

We are far, far from hard AI. If anything, this article shows that we're only just now starting to ask the right questions. And that's even debatable. Plus they're very hard questions.

The problem is we have no theory of intelligence, no theory of psychology. Research in the cognitive fields is fractured, all about tiny insignificant phenomena with little relation to anything else. Our best theory is "the brain is like a computer" which is, frankly, a terrible theory.

Here's something I find more promising: On Intelligence From First Principles: Guidelines for Inquiry Into the Hypothesis of Physical Intelligence [1]

In short, what we really need to understand is self-organization and non-equilibrium thermodynamics. Not image labeling.

[1] http://www.tandfonline.com/doi/pdf/10.1080/10407413.2012.645...

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The problem is we have no theory of intelligence

I don't think we can have a theory of intelligence. At least in the public consciousness, intelligence is one of those "God-of-the-gaps" style concepts that continually evolves in order to maintain the illusion of human superiority.

Well, to the extent that it's a scientific problem, it sure would help to have a theory. I sure don't expect that theory to enter the public consciousness anytime soon, if at all.

But I do agree with your sentiment as far as the way intelligence is usually discussed, even among the science-literate.

Intelligence certainly exists. There is a reason humans are building space ships and chimpanzees are playing around with sticks.

Right. But how do you define it intensionally? Saying that "humans have a lot of it", "chimpanzees have less of it", "ditto for dolphins", "reptiles have very little of it", etc. is defining intelligence extensionally. Why is this a problem? Because an extensional definition doesn't tell you how to add new elements to the set.

I'll try: a system is intelligent if it is able to respond to low-energy deposits (information) with high-energy reactions (e.g., movement) in order to seek non-local sources of negentropy to dissipate.

But then again I'm more interested in the intelligence that differentiates a slime mold from a hurricane than the intelligence that differentiates a human from a chimpanzee.

For example: hurricanes are self-organized, constituted by a structured flow of energy and matter rather than specific pieces of matter. But a hurricane is a slave to the local potential. It will dissipate all the negentropy in its wake, and in doing so maintain its structure. But once there is no more energy differential to dissipate, the hurricane will itself dissipate as it is not able to break free of the local potential and use information to seek out non-local negentropy sources. The question for research is what is necessary to make that jump from self-organization to intelligence, given that operationalization.

Don't wildfires have the ability to break free of a local potential? All it takes is a small spark, carried on the wind.

Likewise for seeds, fish eggs (carried in the gut of birds), etc.

Wind is a local potential in this example. An intelligent wildfire would be one whose sparks can go against the wind, because it perceives more fuel in that direction.

Also: my definition is meant to include fish and birds, even plants, as intelligent.

I've seen sparks go against the wind, at least on a small scale. Sparks can be launched pretty far when the burning wood falls and breaks apart.

You've responded to the second part of: "able to respond to low-energy deposits (information) with high-energy reactions (e.g., movement)"

How does your comment about sparks address the alternatively stated requirement: "because it perceives more fuel in that direction"

How do you define perception?

* A spark flies out randomly and contacts a fuel source

* A blind person reaches out randomly and finds a glass of water

What is the essential difference between these events?

Either the blind person is responding to some low-energy distribution (scattered sound waves, perhaps, or past samplings of the energy distribution, i.e. memory) or the blind person isn't perceiving any more than the spark is (in this example).

In any case my post above implied a definition for perception: responding to low-energy distributions with an asymmetric high-energy response.

It's not just arbitrary examples, there is reasoning behind it. You can look at humans building spaceships and using tools and demonstrating understanding of abstract concepts. There are a number of tests you could do that would confirm something is intelligent like looking for any of those things.

Some rough and imperfect, but still useful, definitions of intelligence could be the ability to make good predictions based on past data, the ability to solve optimization problems well, and learning ability.

Has anyone bothered to develop a full mathematical definition of "optimization power"? Because I've been thinking about how to do it.

http://lesswrong.com/lw/va/measuring_optimization_power/

Bugger. That bastard has 10 years' head start on me; simply not fair.

OTOH, looks like not very much was actually formalized.

demonstrating understanding of abstract concepts.

What sort of test can show that a subject demonstrates an understanding of abstract concepts?

So far, from what I've seen, if a test can be written then software can be written to solve the test.

You could talk to it or you could have it solve a difficult problem.

Right. That's commonly called the Turing test. This just pushes back the problem of defining intelligence to one of creating a proper Turing test. How do we do that?

The Turing test is actually a pretty decent and straightforward test.

I don't understand why this is an issue though. Testing intelligence was never the hard part of AI. There are so many tasks that computers currently suck at that we would be happy if they were solved, regardless what label you gave the solution. And I don't think many people could see a computer doing tasks like having conversations or solving difficult problems and deny that it is intelligence. Even if there is no formal test to perform that is 100% certain.

The Turing test is actually a pretty decent and straightforward test.

How so? To me, it appears completely open-ended. You could sit there forever asking questions and never reach a definitive result.

I don't see how. Have you ever tried talking to a chatbot? It becomes apparent pretty quickly that it's not intelligent.

The point (maybe irrelevant to the larger discussion) is that as soon as we figure out how to implement intelligent behavior in a machine, it stops seeming intelligent. Chess used to be a prime example of intelligent human strategic thinking. Now it's just an item on that long list of things computers can beat us at (incidentally, I predict Jeopardy will go off-air sometime in the next 10 years due to declining interest now that we have Watson).

Once we figure out all the issues of general intelligence, it will stop seeming so special. We may even begin to think that humans are really bad at it afterall.

Chess playing programs worked because they use unfathomable amounts of computing power to essentially brute force the problem. I don't think there are any chess programs that play anything like a human does.

Because of this there are a number of games that computers still can't beat because just stupidly trying every possible move doesn't work like it does for chess.

Watson actually does use a lot of natural language processing and machine learning so it is kind of intelligent. Though at it's core it's still just a glorified search engine. Jeopardy was always just a game of memorizing facts, not a demonstration of intelligence.

I suggest actually looking into the architecture of deep blue and followon programs, because right now you are exhibiting the very fallacy I was talking about. Exhaustive search over board states would take longer than the lifetime of the universe to compute a single move.master chess for grams work by using sophisticated algorithms to manage the search process. it's not the process that humans use, but it is intelligent nonetheless. Of course now that it is a solved problem, the common perception is different...

It's a guided search, so what? There is no fallacy here, deep blue is not intelligent. You can solve any problem with enough computing power and a basic search. No one has ever claimed otherwise or said that it would be intelligent.

What people did predict wrong is that it would take general intelligence to solve chess. As in, if you solved chess, you could also pass the Turing test and everything else. Here is a quote from Douglas Hofstadter:

>There may be programs which can beat anyone at chess, but they will not be exclusively chess players. They will be programs of general intelligence, and they will be just as temperamental as people. "Do you want to play chess?" "No, I'm bored with chess. Let's talk about poetry." That may be the kind of dialogue you could have with a program that could beat everyone.

And they would have been right if computers hadn't become exponentially faster.

Yes I think the same. Understanding how thinking machines self organise out of a set of sufficient conditions is very important. I am commenting mainly so I can read your link later.

Go read Juergen Schmidhuber's research and then tell me we're so far away we can't even see Hard AI on the horizon.

Read the paper I linked and you'll understand why I'm not impressed.

I wasn't talking about his neural-networks work. That's just what he does to get funding ;-).

I'm already familiar with some of his work. What did you have in mind specifically? In any case, he may be one of the best in the traditional computational approach to AI, but I think framing intelligence in terms of computation is inherently misguided. Of course I'm not going to get far with that unorthodox perspective on HN :)

I had meant AIXI and Goedel Machines, but if you've got a non-computational view that's also scientifically grounded, I'd love to hear it.

I think the first true strong AI will come from research on non-equilibrium thermodynamics. We need to get down to the basics: where do entities come from that self-organize, more specifically that are able to use information in structured energy arrays to find and dissipate negentropy deposits, and dissipate that energy in order to maintain their own state away from equilibrium and hence avoid dissipating themselves? In short, strong AI will not come from top-down research on problem solving or learning, but bottom-up research on what makes autonomy and agency possible.

Goedel machines might actually be the closest thing to this in the computing literature, my reaction is less about the work itself than the rhetoric surrounding it, to be honest. JS should collaborate with a physicist on the thermodynamic side of the problem.

If you're intrigued, you could start with the article I linked above, or if you have journal access, anything from the same special issue. I chose that article just because it's the only one not behind a paywall.

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