Supermodelling

At least it should be.

Aside from raw materials in their natural location and state, every single thing of value — physical or abstract — is the product of intelligent action. Applied intelligence creates all wealth. A natural core exists in each of us, of course, and that essence is a large part of what we love and cherish in each other and our world, but all else is the product of industrious mind.

Artificial minds will be just as potent a source of value as our natural ones — actually a great deal more. Their economic impact will be incalculable, many times greater than the sum total value of everything ever created.

On a vastly smaller scale, almost trivial, Bill Gates once said: “If you invent a breakthrough in artificial intelligence, so machines can learn, that is worth 10 Microsofts.” Literally, a couple trillion dollars, though the point of the comment was not to attempt an accurate valuation.

You get the point. Money isn’t the only way of measuring worth, but it is one interesting way.

Now, here’s the puzzle: How come AGI development has produced basically zero dollars profit so far, and why can’t AGI efforts attract even small amounts of capital investment?

I have a couple of possible answers for this.

• Maybe nobody yet knows how to make significant progress toward AGI. Or at least, nobody can convince investors that they know how.
• Maybe it is beyond our ability. Humans, even in groups, are only so capable. As finite creatures, there are limits to what we can do. Maybe building AGI is just too hard.
• Maybe we are making money. There are many different overlapping and even contradictory ways of thinking about intelligence. Over the last few months I have moved toward the viewpoint that, roughly speaking, intelligence == modelling ability. Starting from there, it makes sense to think that most of the entire history of the computer industry comprises the first steps toward artificial intelligence, generating quite a lot of value and wealth in the process as we develop the means for modelling the universe on our machines. The computer industry moves forward every day, they just don’t call themselves AGI.
• What do you think the answer is?

If there was actual progress being made toward AGI, the field would not consist of a marginal fringe club of futurophiles debating consciousness, it would be an economic juggernaut. In our modern world, more and more of what we do is touched in some way by computer software, and that software is unbearably stupid. All of it. Not only does it malfunction with alarming frequency, but even when it does work it is completely clueless about the needs of its users, displays almost no fluency with the subject matter it is supposed to be about, and never learns. If we can make that software knowledgeable, smarter, more adaptable, more robust — even small steps would be huge. And that’s not even taking into account coming technological gold mines like robotics, massive recordings of video streams, ubiquitous networking, immersive virtual reality, microbilling, scientific simulation, and on and on.

Typically, the excuse given for lack of progress toward anything tangible is that all “general” intelligent tasks are AGI-Complete — meaning that the whole problem has to be solved in order to solve a piece of it.

That can’t be right. It has to be a consequence of thinking about the problem in the wrong way. So what’s a right way? I am not certain, but there are many possibilities. Here are a few:

• AGI practitioners by and large think that by breaking up intelligence into “narrow” issues and problem domains, mainstream AI research has lost the dream. But maybe that’s wrong. Maybe general intelligence really is just relatively straightforward combinations of “narrow” intelligences. If so, AGI should be all about the process of rapidly developing narrow AI technologies and making them work together to solve problems in real-world task domains. Yes, it’s hard. So make it easier!
• Maybe focusing on a particular application with large economic potential — e.g. natural language question answering, robotic control systems, or forecasting — from an AGI perspective would provide the right leverage for producing self-sustaining progress. Rather than starting with a system that does absolutely nothing (but does it in a completely general way) and try to make it do something from there, it might be better to focus single-mindedly on gradually increasing the generality of a system that does only one thing.
• What do you think is the right way to think about the problem?

Earlier I mentioned that I have been gradually coming to hold the viewpoint that intelligence == modelling ability, and I have touched on that in other blog postings about concepts being models of things in the universe. My bet on the best approach to being real, relevant, and successful is to move forward from that premise. So that’s exactly what I’m going to start to do, though I am not yet certain how to best proceed so the path will be long and winding.

I will be writing more about this anon.

1. 
   Vladimir Golovin said...
   10:48 am - November 19th, 2008

   Maybe it is beyond our ability.

   I don’t think so. Our prime example of a general intelligence, the human brain, is a finite thing made of a finite number of atoms, operating fully within known laws of physics (I think it’s unlikely that Penrose is right).

   So, I’m convinced that we humans can build it, given sufficient time and thought.

2. 
   Mentifex said...
   9:52 pm - November 19th, 2008

   So “AI-Complete” or “AGI-Complete” means that “the whole problem has to be solved in order to solve a piece of it”? What a clear, precise way of stating it — meanwhile chipping away at my own colossal ignorance. (Now I can use “AI-Complete” and know what I am talking about :-)