Physicist proposes new way to think about intelligence

Anyway, I kind of object to your definition of intelligence as "the ability to predict the unknown". Of course it comes down to personal preference, but I feel that when it comes to making AI, a better definition is something along the lines of "intelligence is the ability to make good choices when faced with a decision". In other words, I like to come at AI using the definition of rational agent[1].

Machine learning is the subject that involves predicting the unknown, and there is a ton of linear algebra involved. I am not an expert at ML, so it's possible that the second law in some form plays a role, but if so I have not come across that yet in my studies.

Now in as much as the second law applies to AI, I am skeptical, but your comment has convinced me to keep an open mind and look into it more carefully. Entropy is merely the log of the probability distribution, so when we talk about maximizing entropy for decision making, what probability distribution should we use, and should we use different distributions for different situations, and if so how do we decide which one to use?

A much more relevant subject for AI in my opinion is game theory, which is really a theory of decision making. Finding the optimal strategy for navigating a decision tree can be a hard problem, and just because maximizing entropy works to find solutions for some types of decision problems, doesn't mean that it's a magic bullet that will always, or even frequently, work out.

[1] http://en.wikipedia.org/wiki/Rational_agent

I think if he would have been alive at the right time these would have been blog posts. Before reading them, I had taken an intro class in thermodynamics which at left me completely confused.

Read THE EVOLUTION OF CARNOT'S PRINCIPLE ( http://bayes.wustl.edu/etj/articles/ccarnot.pdf ) for incredible insights on how Carnot pioneered thermodynamics by trying to optimize steam engines.

Also if you think you dislike statistics and probabilities but you like math in general his book might change your mind: Probability Theory: The Logic of Science. Free draft: http://omega.albany.edu:8008/JaynesBook.html

Amazon: http://www.amazon.com/Probability-Theory-Science-T-Jaynes/dp...

In fact understanding his stance on probabilities, the mind projection fallacy in particular might be prerequisite to understand thermodynamics, the fundamental point being that entropy is not really directly a property of matter but more of a meta property that is about knowledge or information which is taken to mean correlations across aggregate matter.

"Finally, we conclude that it would be desirable to transfer some of the generality of the underlying empowerment concept into methods for empowerment calculation in systems combining continuous and discrete dynamics, especially systems of real-world relevance."

What defines a good choice? What defines a bad choice? Saying "A true machine intelligence is one that makes good decisions" is a tautology -- it doesn't define anything. Rather, it just reframes one vague question into another.

Intelligence is notoriously difficult to describe, so don't feel bad about it if you keep thinking of circular definitions. It's a tricky topic.

Also, IMO there's a big problem with rational agents: In order to be actually useful/implementable, they must be defined in a specific logical framework. You can define a rational agent in a vague sense without any specific domain, but doing so does not solve any problems -- it again just results in tautology. But the problem with choosing a logical framework is shown by Godel's famous theorem. Simply put, any such agent in a well-defined domain will be confined to that domain of reasoning. To me, a machine that excels in some area of planning but can never think outside the box, is not intelligent on the same level as humans.

Anyone advocating rational agent AI as true machine intelligence IMO will need to either find a flaw and disprove Godel's incompleteness theorem, or provide some system of logic that encapsulates all human reasoning (absurdly impossible IMO).

On the other hand, I personally think true machine intelligence will be solved much more "organically", far separated from formal logic and more related to fuzzy pattern matching than rigid formal optimization. I really like this definition of intelligence:

http://lesswrong.com/lw/vb/efficient_crossdomain_optimizatio...

Though I wouldn't claim it's the ultimate one, as it's still a bit vague. I think the key lies in what he describes as "cross domain" -- what is colloquially called "thinking outside the box", because this is the only thing humans seem extremely good at that every computer AI to date has failed at.

Humans are capable of transcending formal logical systems and finding new truths that are unprovable from the original formal system. How is this possible? Godel proved that a formal system cannot ever determine this by itself, from axioms, without being inconsistent.

IMO a true machine intelligence will also need to be capable of this formal-system-transcending property (which you can also call "cross domain thinking", or "thinking outside the box", or "creativity", or whatever).