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The article is kind of written in gibberish. It also implies that the idea of linking thermodynamics to intelligence is novel when a lot of experts actually see

The article is kind of written in gibberish. It also implies that the idea of linking thermodynamics to intelligence is novel when a lot of experts actually see it as a fundamental of thermodynamics and the universe. It's even possible that this physicist is naive about the whole debate an thinks that he discovered this. However while the idea is not new, it does seem like he made a software implementation of some some aspects of it and this could be interesting. I find the link between thermodynamics and intelligence very interesting. Here is how it works according to me. First the best definition of intelligence IMO is the ability to predict the unknown (whether because it's hidden or because it's in the future) from current knowledge. In order to have 'intelligence' you have to have some information in your head. That is to say, a part of you has to be physically correlated with a part of the world, some particles in your head have to have patterns that approximately and functionally 'mirror' part of the world. Thermodynamics is about order and this is also about particles having properties that correlate with each other. This means that in a low entropy situation knowing something about a particle tells you something about some other particles. Intelligence would by useless if the universe had too high entropy. You can't get much insight from white noise even if you are very smart. There is a saying that "knowledge is power". This is truer than you might think. It is true in a very physical sense. For example, take the thermodynamic textbook example of a container with a gas on one side and void on the other. If there is no barrier between the two side the gas should move to fill the void and settle in the higher entropy case of evenly filling the space. Thermodynamics says that you would need to spend energy to push the gas back to one side. However, if you were to put a wall in the middle of the container with a little door large enough to let a molecule go through, and you knew exactly the position and velocity of each molecule in the container, you could open and close the door at exactly the right time when a molecule is about to go from, say, left to right through the door and close it when one is about to go right to left. Using very little energy you could get all molecules to end up on one side of the container. This should violate the second law of thermodynamics but it does not! Why is that you ask? It's because the knowledge you have of the position and velocity of all these molecules is a source of low entropy. Knowledge is low entropy and the correlation between the particles in your head and the real world is what allows you to make predictions and extract useful energy from things. Note that an interesting comp sci result of this is that low entropy information is more easy to compress and the more a system is good at predicting data from other data, the better it is at compressing such that there is also a tight link between the concepts of compression algorithms and artificial intelligence. But that's another story.

calhoun137 14y ago
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.

calhoun137 14y ago

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.