Re: Mechanisms for learning
[{"sender": "Joscha Bach", "recipients": ["jeffrey E. [jeevacation@gmail.com]"], "timestamp": "Sun, Jul 10, 2016 at 12:42 AM", "subject": "Re: Mechanisms for learning", "body": "Dear Jeffrey, \n\nthank you for your support and encouragement, even where I fail. \n\nSorry for being such an embarrassment today. I will spell out today's argument a bit better and cohesive when \nI get to it. Also, I should have recognized that the main point I tried to make would trigger Noam (who was as \nalways very generous, patient, kind and humble on the personal level, even though he did not feel like \nconceding anything on the conceptual one). Almost all of Noam's work focused on the idea that humans have \nvery specific circuits or modules (even when most people in his field began to have other ideas), and his \nfrustration is that it is so hard to find or explain them. \n\nI found Noam's hypothesis very compelling in the past. I still think that the idea that language is somehow a \ncultural or social invention of our species is wrong. But I think that there is a chance (we don't know that, but \nit seems to most promising hypothesis IMHO) that the difference between humans and apes is not a very \nintricate special circuit, but genetically simple developmental switches. The bootstrapping of cognition works \nlayer by layer during the first 20 years of our life. Each layer takes between a few months and a few years to \ntrain in humans. While a layer is learned, there is not much going on in the higher layers yet, and after the low \nlevel learning is finished, it does not change very much. This leads to the characteristic bursts in child \ndevelopment, that have famously been described by Piaget. \n\nThe first few layers are simple perceptual stuff, the last ones learn social structure and self-in-society. The \nswitching works with something like a genetic clock, very slowly in humans, but much more quickly in other \napes, and very fast in small mammals. As a result, human children take nine months before their brains are \nmature enough to crawl, and more than a year before they can walk. Many African populations are quite a bit \nfasters. In the US, black children outperform white children in motor development, even in very poor and \nsocially disadvantaged households, but they lag behind (and never catch up) in cognitive development even \nafter controlling for family income. \n\nGorillas can crawl after 2 months, and build their own nests after 2.5 years. They leave their mothers at 3-4 \nyears. Human children are pretty much useless during the first 10-12 years, but during each phase, their brains \nhave the opportunity to encounter many times as much training data as a gorilla brain. Humans are literally \nsmarter on every level, and because the abilities of the higher levels depend on those of the lower levels, they \ncan perform abstractions that mature gorillas will never learn, no matter how much we try to train them. \n\nThe second set of mechanisms is in the motivational system. Motivation tells the brain what to pay attention \nto, by giving reward and punishment. If a brain does not get much reward for solving puzzles, the individual \nwill find mathematics very boring and won't learn much of it. If a brain gets lots of rewards for discovering \nother people's intentions, it will learn a lot of social cognition. \n\nLanguage might be the result of three things that are different in humans: \n- extended training periods per layer (after the respective layer is done, it is difficult to learn a new set of \nphonemes or the first language) \n- more layers \n- different internal rewards. Perhaps the reward for learning grammatical structure is the same that makes us \nlike music. Our brains may enjoy learning compositional regular structure, and they enjoy making themselves \nunderstood, and everything else is something the universal cortical learning figures out on its own. \n\nThis is a hypothesis that is shared by a growing number of people these days. In humans, it is reflected for \ninstance by the fact that races with faster motor development have lower IQ. (In individuals of the same \ngroup, slower development often indicates defects, of course.) \n\nAnother support comes from machine learning: we find that the same learning functions can learn visual and \nauditory pattern recognition, and even end-to-end-learning. Google has built automatic image recognition into \ntheir current photo app: \nhttp://blogs.wsj . com/digits/2015/07/01/go ogle-mistakenly-tags-black-p eop le-as-gorillas-showing-limits-o f-\nalgorithms/  \n\nThe state of the art in research can do better than that: it can begin to \"imagine\" things. I.e. when the \nexperimenter asks the system to \"dream\" what a certain object looks like, the system can produce a somewhat \ncompelling image, which indicates that it is indeed learning visual structure. This stuff is something nobody \ncould do a few months ago: \nhttp://www.creativeai.net/posts/Mv4WG6rdzAerZF7ch/synthesizing-preferred-inputs-via-deep-generator-\nnetworks  \n\nA machine learning program that can learn how to play an Atari game without any human supervision or \nhand-crafted engineering (the feat that gave DeepMind 500M from Google) now just takes about 130 lines of \nPython code. \n\nThese models do not have interesting motivational systems, and a relatively simple architecture. They \ncurrently seem to mimic some of the stuff that goes on in the first few layers of the cortex. They learn object \nfeatures, visual styles, lighting and rotation in 3d, and simple action policies. Almost everything else is \nmissing. But there is a lot of enthusiasm that the field might be on the right track, and that we can learn motor \nsimulations and intuitive physics soon. (The majority of the people in Al do not work on this, however. They \ntry to improve the performance for the current benchmarks.) \n\nNoam's criticism of machine translation mostly applies to the Latent Semantic Analysis models that Google \nand others have been using for many years. These models map linguistic symbols to concepts, and relate \nconcepts to each other, but they do not relate the concepts to \"proper\" mental representations of what objects \nand processes look like and how they interact. Concepts are probably one of the top layers of the learning \nhierarchy, i.e. they are acquired *after* we learn to simulate a mental world, not before. Classical linguists \nignored the simulation of a mental world entirely. \n\nIt seems miraculous that purely conceptual machine translation works at all, but that is because concepts are \nshared between speakers, so the structure of the conceptual space can be inferred from the statistics of \nlanguage use. But the statistics of language use have too little information to infer what objects look like and \nhow they interact. \n\nMy own original ideas concern a few parts of the emerging understanding of what the brain does. The \n\"request-confirmation networks\" that I have introduced at a NIPS workshop in last the December are an \nattempt at modeling how the higher layers might self-organize into cognitive programs. \n\nCheers! \n\nJoscha"}, {"sender": "Joscha Bach", "recipients": [], "timestamp": "Sat, Jul 23, 2016 at 3:02 AM", "subject": "Re: Mechanisms for learning", "body": "Some thoughts I meant to send back for a long time:"}, {"sender": "jeffrey E. <jeevacation@gmail.com>", "recipients": [], "timestamp": "Am 23.07.2016 um 04:41", "subject": "Re: Mechanisms for learning", "body": "Cells seem to be mostly indistinguishable (except for )04 I thought? \nSize is different on average, but males with small brains are still male. \nVariation of all traits is higher for males, so there are more high IQ men (wider bell-curve). Given average brain \nsize difference, the small difference in average IQ is surprising, I think \ngender differenece. unlikely motivational, every cell is different. size is didfferent. . \nthe more radical idea is that brains are the nucleus of the human\" cell: the body the membrane, . the \norganizm the colletcions and interactions of brains, wisdom of crowds . mirroring. etc. . money used \nas signals. as is spoken language. . what does social behavior mean, it is the cooridination of indiv brains. = \nI think that mathematics based thinking does not allow for censorship. money people concepts if they are \nall math , then poliiical correctness and their structures are only subsets of a larger group of thoughts. its not \nemotinal for me. \nmost people have censorship built in at a deep level, social reasoning gets to different results than equivalent \nabstract reasoning"}, {"sender": "Joscha Bach", "recipients": ["jeffrey E. [jeevacation@gmail.com]"], "timestamp": "7/23/2016 10:02:05 PM", "subject": "Re: Mechanisms for learning", "body": "no worry, if i understand correctly you are suggsting there are layers 1 through N . lets cal them L 1 - \nLn. there are times T 1- Tn / and then conjecture that changing the time . correlations ( by genetic swtch or \nother method. , you might be able to make blacks smarter by changing the time for motor layer \ndevelopment and changing the time for other layers. . ). like telemeres for the cell , are their equivalents for \nthe layers. . as you talked about culling the unused neurons in each layer, each neuron in each layer would get \ndfferent ( kill yourself if you are not being used instructions ). \n\nand CONCEPTS could be layer to layer communicaiton. . are gender differences also a matter of time , and \nstructure of interactions. . \n\nre taboo \n, maybe climate change is a good way of dealing with overpopulation. . the earths forest fire. potentailly \na good thing for the species \n\ntoo many people, so many mass executions of the elderly and infirm make sense \nis the fundamental fact that everyone dies at some time .make it imporrisbole to ask so why not earilier. if \nthe brain discards unused neurons, why shold socieity keep their equivalent"}]