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(A text with some decent discussion on the topic)[http://www.inference.phy.cam.ac.uk/mackay/itila/book.html]. At least one group that has a shot at winning a major speech recognition benchmark competition uses information-theoretic ideas for the development of their speech recognizer. Another development has been the use of error-correcting codes to assist in multi-class classification problems (google "error correcting codes machine learning")[http://www.google.com/search?sourceid=chrome&ie=UTF-8&q=error+correcting+codes+machine+learning] (arguably this has been the clearest example of a paradigm shift that comes from thinking about compression which had a big impact in machine learning). I don't know how many people think about these problems in terms of information theory questions (since I don't have much access to their thoughts): but I do... (read more)
I have a minor disagreement, which I think supports your general point. There is definitely a type of compression going on in the algorithm, it's just that the key insight in the compression is not to just "minimize entropy" but rather make the outputs of the encoder behave in a similar manner as the observed data. Indeed, that was one of the major insights in information theory is that one wants the encoding scheme to capture the properties of the distribution over the messages (and hence over alphabets).
Namely, in Hinton's algorithm the outputs of the encoder are fed through a logistic function and then the cross-entropy is minimized... (read more)
This attacks a straw-man utilitarianism, in which you need to compute precise results and get the one correct answer. Functions can be approximated; this objection isn't even a problem.
Not every function can be approximated efficiently, though. I see the scope of morality as addressing human activity where human activity is a function space itself. In this case the "moral gradient" that the consequentialist is computing is based on a functional defined over a function space. There are plenty of function spaces and functionals which are very hard to efficiently approximate (the Bayes predictors for speech recognition and machine vision fall into this category) and often naive approaches will fail... (read more)
I would like you to elaborate on the incoherence of deontology so I can test out how my optimization perspective on morality can handle the objections.
To be clear I see the deontologist optimization problem as being a pure "feasibility" problem: one has hard constraints and zero gradient (or approximately zero gradient) on the moral objective function given all decisions that one can make.
Of the many, many critiques of utilitarianism some argue that its not sensible to actually talk about a "gradient" or marginal improvement in moral objective functions. Some argue this on the basis of computational constraints: there's no way that you could ever reasonably compute a moral objective function (because the consquences of any activity are much to complicated) to other critiques that argue the utilitarian notion of "utility" is ill-defined and incoherent (hence the... (read more)
I would argue that deriving principles using the categorical imperative is a very difficult optimization problem and that there is a very meaningful sense in which one is a deontologist and not a utilitarian. If one is a deontologist then one needs to solve a series of constraint-satisfaction problems with hard constraints (i.e. they cannot be violated). In the Kantian approach: given a situation, one has to derive the constraints under which one must act in that situation via moral thinking then one must accord to those constraints.
This is very closely related to combinatorial optimization problems. I would argue that often there is a "moral dual" (in the sense... (read more)
I agree with the beginning of your comment. I would add that the authors may believe they are attacking utilitarianism, when in fact they are commenting on the proper methods for implementing utilitarianism.
I disagree that attacking utilitarianism involves arguing for different optimization theory. If a utilitarian believed that the free market was more efficient at producing utility then the utilitarian would support it: it doesn't matter by what means that free market, say, achieved that greater utility.
Rather, attacking utilitarianism involves arguing that we should optimize for something else: for instance something like the categorical imperative. A famous example of this is Kant's argument that one should never lie (since it could never be willed to be a universal law, according to him), and the utilitarian philosopher loves to retort that lying is essential if one is hiding a Jewish family from the Nazis. But Kant would be unmoved (if you believe his writings), all that would matter are these universal principles.
Bear in mind that having more fat means that the brain gets starved of (glucose)[http://www.loni.ucla.edu/~thompson/ObesityBrain2009.pdf] and blood sugar levels have (impacts on the brain generally)[http://ajpregu.physiology.org/cgi/content/abstract/276/5/R1223]. Some research has indicated that the amount of sugar available to the brain has a relationship with self-control. A moderately obese person may have fat cells that steal so much glucose from their brain that their brain is incapable of mustering the will in order to get them to stop eating poorly. Additionally, the marginal fat person is likely fat because of increased sugar consumption (which has been the main sort of food whose intake has increased since the origins of the obesity epidemic... (read more)
I think that this post has something to say about political philosophy. The problem as I see it is that we want to understand how our local decision-making affects the global picture and what constraints should we put on our local decisions. This is extremely important because, arguably, people make a lot of local decisions that make us globally worse off: such as pollution ("externalities" in econo-speak). I don't buy the author's belief that we should ignore these global constraints: they are clearly important--indeed its the fear of the potential global outcomes of careless local decision-making that arguably led to the creation of this website.
However, just like a computers... (read more)
This isn't precisely what Daniel_Burfoot was talking about but its a related idea based on "sparse coding" and it has recently obtained good results in classification:
http://www.di.ens.fr/~fbach/icml2010a.pdf
Here the "theories" are hierarchical dictionaries (so a discrete hierarchy index set plus a set of vectors) which perform a compression (by creating reconstructions of the data). Although they weren't developed with this in mind, support vector machines also do this as well, since one finds a small number of "support vectors" that essentially allow you to compress the information about decision boundaries in classification problems (support vector machines are one of the very few things from machine learning that have had significant and successful impacts elsewhere since neural networks).
The hierarchical dictionaries learned do contain a "theory" of the visual world in a sense, although an important idea is that they do so in a way that is sensitive to the application at hand. There is much left out by Daniel_Burfoot about how people actually go about implementing this line of thought.