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Just so you know, before I started spending all of my time programming I was a physicist, and statistical physics was my best subject. Anyway, I kind of object

Just so you know, before I started spending all of my time programming I was a physicist, and statistical physics was my best subject. 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 http://en.wikipedia.org/wiki/Rational_agent

BenoitEssiambre 14y ago
I think your definition of intelligence is good and is simply broader than mine. I split your definition in two parts and only labelled the first part 'intelligence'. part 1: Predicting the unknown which can be the probabilities of outcome of your actions and such. part 2. Evaluating these outcomes and their likelihoods to make a decision based on some utility or preference function. I can see contexts where the word 'intelligence' would encompass both parts. In the context of thermodynamics or information theory I think intelligence is more about the prediction part because pure matter or even computers don't really have preference of outcome so the second part is mostly irrelevant to their 'intelligence'. The original article seems to take low entropy as a stated goal or preference of the system which is not a bad sub goal in that it is thermodynamically efficient and saves usable energy longer but I don't think being thermodynamically efficient should be the only goal of an intelligent system.
ac500 14y ago
> "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"." 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_optimization/ 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).
- calhoun137 14y ago
  For a rational agent, a decision is good or bad based on it's preferences, which are described mathematically as part of the definition of the rational agent. So I don't believe it's tautological. I do agree with your statement that intelligence is difficult to describe. And for the record I never claimed the rational agent approach will achieve "true machine intelligence". Frankly, I don't even know what "true" machine intelligence is or would be. What I am very interested in however is how to create an AI that is capable of fooling a person into thinking it's intelligent, in other words an AI that can pass the Turing test. For that purpose I find the concept of a rational agent to be very helpful, in that it provides a solid foundation to work from. I'm also not worried about Godel's theorem at all. The statements that are undecidable are very obscure and basically never come into play in human decision making. I think Godel's result has very little practical application, if it has any at all; I certainly don't think it's relevant for AI. I agree that choosing a logical framework to work in presents many problems, but I don't agree that you have to disprove Godel's theorem to create an AI that can think outside the box.
sramsay 14y ago
"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" I love it. Mine is even more constructivist (and Wittgensteinian). Something like "intelligence is the ability to detect patterns and act accordingly." The problem, as many philosophers have noted, is that such definitions -- in fact, most definitions -- make it hard to argue that the thermostat is not intelligent (or conscious, or sentient). And that, of course, is at the heart of Turing's most famous argument. I, myself, find it hard to argue my way around what is essentially a "walks like a duck, talks like a duck" argument when it comes to these matters -- which is to say, defining something in terms of its activities, behaviors, likenesses. That perhaps tilts practical AI toward the statistical, though, and it's hard for me to believe that that's the whole story. I have never presented the arguments back-and-forth on this issue without watching my students collapse into confusion and desperation.
- Evbn 14y ago
  The great personal insight is when one finally discards prejudice and bigotry and realizes that a thermostat is intelligent, and that a neuron is a "molecule" of intelligence.

BenoitEssiambre 14y ago

I think your definition of intelligence is good and is simply broader than mine. I split your definition in two parts and only labelled the first part 'intelligence'. part 1: Predicting the unknown which can be the probabilities of outcome of your actions and such. part 2. Evaluating these outcomes and their likelihoods to make a decision based on some utility or preference function. I can see contexts where the word 'intelligence' would encompass both parts. In the context of thermodynamics or information theory I think intelligence is more about the prediction part because pure matter or even computers don't really have preference of outcome so the second part is mostly irrelevant to their 'intelligence'. The original article seems to take low entropy as a stated goal or preference of the system which is not a bad sub goal in that it is thermodynamically efficient and saves usable energy longer but I don't think being thermodynamically efficient should be the only goal of an intelligent system.

ac500 14y ago

> "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"." 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_optimization/ 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).

calhoun137 14y ago
For a rational agent, a decision is good or bad based on it's preferences, which are described mathematically as part of the definition of the rational agent. So I don't believe it's tautological. I do agree with your statement that intelligence is difficult to describe. And for the record I never claimed the rational agent approach will achieve "true machine intelligence". Frankly, I don't even know what "true" machine intelligence is or would be. What I am very interested in however is how to create an AI that is capable of fooling a person into thinking it's intelligent, in other words an AI that can pass the Turing test. For that purpose I find the concept of a rational agent to be very helpful, in that it provides a solid foundation to work from. I'm also not worried about Godel's theorem at all. The statements that are undecidable are very obscure and basically never come into play in human decision making. I think Godel's result has very little practical application, if it has any at all; I certainly don't think it's relevant for AI. I agree that choosing a logical framework to work in presents many problems, but I don't agree that you have to disprove Godel's theorem to create an AI that can think outside the box.

sramsay 14y ago

"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" I love it. Mine is even more constructivist (and Wittgensteinian). Something like "intelligence is the ability to detect patterns and act accordingly." The problem, as many philosophers have noted, is that such definitions -- in fact, most definitions -- make it hard to argue that the thermostat is not intelligent (or conscious, or sentient). And that, of course, is at the heart of Turing's most famous argument. I, myself, find it hard to argue my way around what is essentially a "walks like a duck, talks like a duck" argument when it comes to these matters -- which is to say, defining something in terms of its activities, behaviors, likenesses. That perhaps tilts practical AI toward the statistical, though, and it's hard for me to believe that that's the whole story. I have never presented the arguments back-and-forth on this issue without watching my students collapse into confusion and desperation.

Evbn 14y ago
The great personal insight is when one finally discards prejudice and bigotry and realizes that a thermostat is intelligent, and that a neuron is a "molecule" of intelligence.