5 ms·

This sounds like nonsense to me. First of all, what exactly do they mean by "intelligent behavior"? I looked in the paper but I couldn't find where they defin

This sounds like nonsense to me. First of all, what exactly do they mean by "intelligent behavior"? I looked in the paper but I couldn't find where they define what they are trying to model. They do however use the vague expression "remarkably sophisticated behaviors associated with the human ‘‘cognitive niche,’’ including tool use and social cooperation." Tool use and social cooperation, sounds interesting. Let's see, that seems to be figures 3 and 4 from page 4. Once you get over the idea of modeling an animal with a disc, it seems a little bizarre that figures 3 or 4 have anything to do with intelligent behavior. Figure 3 is about one disc bouncing off another disc so that a third disc goes into a cylinder. I would say that doesn't really capture the essence of how "non-human animals" use tools. Then the social cooperation example is about a big disc attached to a string, that moves differently when 2 smaller discs touch it at the same time, and then under some specific configuration the discs have "social cooperation" to move the big disc. As far as I can tell, whether or not moving the big disc is an intelligent decision or not seems to not matter in this example. We then get very bold claims, such as "physical agents driven by causal entropic forces might be viewed from a Darwinian perspective as competing to consume future histories."

unoti 14y ago
Here's some intelligent behaviors that can be simply modelled as entropy: http://scalablegamedesign.cs.colorado.edu/wiki/Collaborative_Diffusion http://scalablegamedesign.cs.colorado.edu/wiki/Collaborative... That's a very simple model that can be used, in that example, to make a number of ghost agents work together to converge on a pac-man, splitting themselves up to cover every exit. The code to do it is very small, smaller than A*, could also be used to do pathfinding, and is based essentially on the ideas of entropy.
deleted 14y ago
[deleted]
BenoitEssiambre 14y ago
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.
tinco 14y ago
The idea is that these models could be extended to really big complex problems with the same basic principle. Therefore, it seems that intelligence of arbitrary complexity can be achieved with these algorithms. I don't think that claim is very bold at all, it is merely an observation. The idea that the algorithm tries to maintain entropy is very much like life as we know it. Life as we know it are mechanisms that despite the continuous dissipation of entropy in the universe around it try to achieve local points of high entropy and sustain those. In less intelligent animals, the mechanism to resist entropy loss is defined genetically. They can not adapt to new environments because their decision making is rigidly defined in their genes. (i.e. they are like simple computer programs) In more intelligent animals, like ourselves, instead of deriving our decisions (mainly) from our genes, we derive them from an organ (whose design is derived from genes) we call the brain (I've heard there's more decision making organs). Separating the decision making from the the dna is what makes us so highly adaptable to new environments. There our likeness to this algorithm lies, like us this algorithm can sustain its entropy no matter what the environment is (as long as its got the tools to do so).
- dusklight 14y ago
  It's pretty clear that we are not the only animals with brains, so there are other animals who use their brains to make decisions, such as how to hunt and when to sleep and so on. We make what is in our opinion more sophisticated decisions, but that arguably is a human-centric conceit. There are ant colonies that span continents, and they aren't building any nuclear weapons. Are we really sure we are more intelligent than them? I don't think it is fair to say that our brain makes us more adaptable to new environments than other species. Compared to the many single-celled species that have survived hundreds of millions of years, the bacteria that can survive hibernation for millions of years, and our current inability to agree on the existence of global warming, is it realistic to project the survival of our species beyond even the next thousand years?
  - thibauts 14y ago
    What I feel makes us different from animals is not being able to adapt, as you pointed out (many animals exhibit learned behavior and conscious decision making), it's not even being self-conscious (it's been proven animals can be too), it's in my opinion the ability to observe and act on your own thought-streams. And this is deeply linked to language, as language is a way to receive informations you don't already have and coerce your thoughts streams (or not) along them to make predictions that are not based on experience. In my opionion it is very important to make this distinction between predictions learned by experience and predictions learned by the books. I think we here can measure this difference very well. To me it looks like this added layer of brain cells we have acts as an observer on life happening on the floor below, kind of a god of your own brain, acting not on experience-learned behaviour but solely on a combination of representation learned from others (connections created willfully from other's discourse) and abstractions made from adaptive learning done on signals coming from the layers below (I envision an isolated system whose only object of observation is regular thoughts and whose only sense is signals coming through connections to layers producing those thoughts). It feel likes what makes us apart is first this simple fact we learn from our own though patterns. This enables us to make what (at least) feels like free decisions based on abstracted representation and not mere immediate experience. This enables us to think outside the box of our own experiences and form models of other's. This enables us to delibarately picture in our minds things that we have never experienced, or things that cannot be experienced (think abstract models in sciences). This finally enables us to accept information from our peers as valid signals to model our though-paths on. If this layer was anything real, to me it would mainly be an experience or information-sharing device. This thing that makes possible, and makes useful, the kind of social structure we know and live in. I may be rehashing common knowledge, I don't know.

unoti 14y ago

Here's some intelligent behaviors that can be simply modelled as entropy: http://scalablegamedesign.cs.colorado.edu/wiki/Collaborative_Diffusion http://scalablegamedesign.cs.colorado.edu/wiki/Collaborative... That's a very simple model that can be used, in that example, to make a number of ghost agents work together to converge on a pac-man, splitting themselves up to cover every exit. The code to do it is very small, smaller than A*, could also be used to do pathfinding, and is based essentially on the ideas of entropy.

deleted 14y ago

[deleted]

BenoitEssiambre 14y ago

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.

tinco 14y ago

The idea is that these models could be extended to really big complex problems with the same basic principle. Therefore, it seems that intelligence of arbitrary complexity can be achieved with these algorithms. I don't think that claim is very bold at all, it is merely an observation. The idea that the algorithm tries to maintain entropy is very much like life as we know it. Life as we know it are mechanisms that despite the continuous dissipation of entropy in the universe around it try to achieve local points of high entropy and sustain those. In less intelligent animals, the mechanism to resist entropy loss is defined genetically. They can not adapt to new environments because their decision making is rigidly defined in their genes. (i.e. they are like simple computer programs) In more intelligent animals, like ourselves, instead of deriving our decisions (mainly) from our genes, we derive them from an organ (whose design is derived from genes) we call the brain (I've heard there's more decision making organs). Separating the decision making from the the dna is what makes us so highly adaptable to new environments. There our likeness to this algorithm lies, like us this algorithm can sustain its entropy no matter what the environment is (as long as its got the tools to do so).

dusklight 14y ago
It's pretty clear that we are not the only animals with brains, so there are other animals who use their brains to make decisions, such as how to hunt and when to sleep and so on. We make what is in our opinion more sophisticated decisions, but that arguably is a human-centric conceit. There are ant colonies that span continents, and they aren't building any nuclear weapons. Are we really sure we are more intelligent than them? I don't think it is fair to say that our brain makes us more adaptable to new environments than other species. Compared to the many single-celled species that have survived hundreds of millions of years, the bacteria that can survive hibernation for millions of years, and our current inability to agree on the existence of global warming, is it realistic to project the survival of our species beyond even the next thousand years?
- thibauts 14y ago
  What I feel makes us different from animals is not being able to adapt, as you pointed out (many animals exhibit learned behavior and conscious decision making), it's not even being self-conscious (it's been proven animals can be too), it's in my opinion the ability to observe and act on your own thought-streams. And this is deeply linked to language, as language is a way to receive informations you don't already have and coerce your thoughts streams (or not) along them to make predictions that are not based on experience. In my opionion it is very important to make this distinction between predictions learned by experience and predictions learned by the books. I think we here can measure this difference very well. To me it looks like this added layer of brain cells we have acts as an observer on life happening on the floor below, kind of a god of your own brain, acting not on experience-learned behaviour but solely on a combination of representation learned from others (connections created willfully from other's discourse) and abstractions made from adaptive learning done on signals coming from the layers below (I envision an isolated system whose only object of observation is regular thoughts and whose only sense is signals coming through connections to layers producing those thoughts). It feel likes what makes us apart is first this simple fact we learn from our own though patterns. This enables us to make what (at least) feels like free decisions based on abstracted representation and not mere immediate experience. This enables us to think outside the box of our own experiences and form models of other's. This enables us to delibarately picture in our minds things that we have never experienced, or things that cannot be experienced (think abstract models in sciences). This finally enables us to accept information from our peers as valid signals to model our though-paths on. If this layer was anything real, to me it would mainly be an experience or information-sharing device. This thing that makes possible, and makes useful, the kind of social structure we know and live in. I may be rehashing common knowledge, I don't know.