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Text of the page (random words):
tes as coming from lunar volcanos in 1802 and then called them myths in 1803 so where does this story come from in cosmic debris meteorites in history john burke gives this account there is also a problem with respect to the number of french scientists who after pictet published a résumé of howard s article in the may 1802 issue of the bibliothèque britannique continued to oppose the idea that stones fell from the atmosphere one can infer from a statement of lamétherie that there was considerable opposition for he reported that when pictet read a memoir to the institut on the results of howard s report he met with such disfavor that it required a great deal of fortitude for him to finish his reading however biot s description of the session varies a good deal pictet s account he wrote was received with a cautious eagerness though the desire to explain everything caused the phenomenon to be rejected for a long time there were in fact only three scientists who publicly expressed their opposition the brothers jean andré and guillaume antoine deluc of geneva and eugène patrin an associate member of the mineralogy section of the institut and librarian at the école des mines when pictet early in 1801 published a favorable review of chladni s treatise it drew immediate fire from the deluc brothers jean a strict calvinist employed the same explanation of a fall that the fougeroux committee had used thirty years before stones did not fall the event was imagined when lightning struck close to the observer just as no fragment of our globe separate and become lost in space he wrote fragments could not be detached from another planet it was also very unlikely that solid masses had been wandering in space since the creation because they would have long since fallen into the sphere of attraction of some planet and even if they did fall they would penetrate the earth to a great depth and shatter into a thousand pieces p 51 it seems quite possible that pictet s reading a memoir here and meeting with disfavor regardless of details since burke notes it had different descriptions at the time is the same incident that jaki describes as having been met with we ve had enough of such myths when pictet urged a reconsideration of the evidence if these words were ever said then they were presumably said by one of these brothers or someone else and not by laplace how does this sort of thing happen if we charitably assume that jaki was not being fundamentally dishonest as stated above it seems likely that he knew he did not have a source he may even have been consciously aware that it might not have been laplace who made this statement if anyone did but he was sure there was a dispute about the matter and presumably thought that it just wasn t too important who it was or the details of the situation since the main point was that scientists are frequently reluctant to accept facts when those facts occur rarely and are not deliberately reproducible and if we reduce jaki s position to these two things namely 1 that scientists at one point disputed the reality and meteorites and 2 this sort of thing frequently happens with rare and hard to reproduce phenomena then the position is accurate but this behavior the description of situations with the implication that the details just don t matter much is very bad and directly contributes to the reluctance of many scientists to accept the reality of extraordinary phenomena even in situations where they are in fact real 3 comments what you learned before you were born august 8 2020 august 24 2020 entirelyuseless metaphysics science ai alphago evolution francis bacon gpt 3 knowledge language language model learning meno natural selection plato reinforcement learning socrates universals in plato s meno socrates makes the somewhat odd claim that the ability of people to learn things without being directly told them proves that somehow they must have learned them or known them in advance while we can reasonably assume this is wrong in a literal sense there is some likeness of the truth here the whole of a human life is a continuous learning process generally speaking without any sudden jumps we think of a baby s learning as different from the learning of a child in school and the learning of the child as rather different from the learning of an adult but if you look at that process in itself there may be sudden jumps in a person s situation such as when they graduate from school or when they get married but there are no sudden jumps from not knowing anything about a topic or an object to suddenly knowing all about it the learning itself happens gradually it is the same with the manner in which it takes place adults do indeed learn in a different manner from that in which children or infants learn but if you ask how that manner got to be different it certainly did so gradually not suddenly but in addition to all this there is a kind of knowledge that is not learned at all during one s life but is possessed from the beginning from the beginning people have the ability to interact with the world in such a way that they will survive and go on to learn things thus from the beginning they must know how to do this now one might object that infants have no such knowledge and that the only reason they survive is that their parents or others keep them alive but the objection is mistaken infants know to cry out when they hungry or in pain and this is part of what keeps them alive similarly an infant knows to drink the milk from its mother rather than refusing it and this is part of what keeps it alive similarly in regard to learning if an infant did not know the importance of paying close attention to speech sounds it would never learn a language when was this knowledge learned not in the form of a separated soul but through the historical process of natural selection selection and artificial intelligence this has significant bearing on our final points in the last post is the learning found in ai in its current forms more like the first kind of learning above or like the kind found in the process of natural selection there may be a little of both but the vast majority of learning in such systems is very much the second kind and not the first kind for example alphago is trained by self play where moves and methods of play that tend to lose are eliminated in much the way that in the process of natural selection manners of life that do not promote survival are eliminated likewise a predictive model like gpt 3 is trained through a vast number of examples to avoid predictions that turn out to be less accurate and to make predictions that tend to be more accurate now whether or not this is done in individual cases you might take a model of this kind and fine tune it based on incoming data perhaps even in real time which is a bit more like the first kind of learning but in our actual situation the majority of what is known by our ai systems is based on the second kind of learning this state of affairs should not be surprising because the first kind of learning described above is impossible without being preceded by the second the truth in socrates claim is that if a system does not already know how to learn of course it will not learn anything intelligence and universality elsewhere i have mentioned the argument often made in great annoyance that people who take some new accomplishment in ai or machine learning and proclaim that it is not real intelligence or that the algorithm is still fundamentally stupid and other things of that kind are moving the goalposts especially since in many such cases there really were people who said that something that could do such a thing would be intelligent as i said in the linked post however there is no problem of moving goalposts unless you originally had them in the wrong place and attaching intelligence to any particular accomplishment such as playing chess well or even producing a sensible sounding text or anything else with that sort of particularity is misplacing the goalposts as we might remember what excited francis bacon was the thought that there were no clear limits at all on what science namely the working out of intelligence might accomplish in fact he seems to have believed that there were no limits at all which is false nonetheless he was correct that those limits are extremely vague and that much that many assumed to be impossible would turn out to be possible in other words human intelligence does not have very meaningful limits on what it can accomplish and artificial intelligence will be real intelligence in the same sense that artificial diamonds can be real diamonds when artificial intelligence has no meaningful limits on what it can accomplish i have no time for playing games with objections like but humans can t multiply two 1000 digit numbers in one second and no amount of thought will give them that ability if you have questions of this kind please answer them for yourself and if you can t sit still and think about it until you can i have full confidence in your ability to find the answers given sufficient thought what is needed for real intelligence then is universality in a sense everyone knew all along that this was the right place for the goalposts even if someone said if a machine can play chess it will be intelligent they almost certainly meant that their expectation was that a machine that could play chess would have no clear limits on what it could accomplish if you could have told them for a fact that the future would be different that a machine would be able to play chess but that that particular machine would never be able to do anything else they would have conceded that the machine would not be intelligent training and universality current ai systems are not universal and clearly have no ability whatsoever to become universal without first undergoing deep changes in those systems changes that would have to be initiated by human beings what is missing the problem is the training data the process of evolution produced the general ability to learn by using the world itself as the training data in contrast our ai systems take a very small subset of the world like a large set of go games or a large set of internet text and train a learning system on that subset why take a subset because the world is too large to fit into a computer especially if that computer is a small part of the world this suggests that going from the current situation to artificial but real intelligence is not merely a question of making things better and better little by little there is a more fundamental problem that would have to be overcome and it won t be overcome simply by larger training sets by faster computing and things of this kind this does not mean that the problem is impossible but it may turn out to be much more difficult than people expected for example if there is no direct solution people might try to create robin hanson s ems where one would more or less copy the learning achieved by natural selection or even if that is not done directly a better understanding of what it means to know how to learn might lead to a solution although probably one that would not depend on training a model on massive amounts of data what happens if there is no solution or no solution is found at times people will object to the possibility of such a situation along these times this situation is incoherent since obviously people will be able to keep making better and better machine learning systems so sooner or later they will be just as good as human intelligence but in fact the situation is not incoherent if it happened various types of ai system would approach various asymptotes and this is entirely coherent we can already see this in the case of gpt 3 where as i noted there is an absolute bound on its future performance in general such bounds in their realistic form are more restrictive than their in principle form i do not actually expect some successor to gpt 3 to write sensible full length books note however that even if this happened as long as the content itself was not fundamentally better than what humans have done i would not be moving the goalposts i do not expect that to happen but its happening would not imply any fundamental difference since this is still within the absolute bounds that we have discussed in contrast if a successor to gpt 3 published a cure for cancer this would prove that i had made some mistake on the level of principle 2 comments some remarks on gpt n august 1 2020 august 1 2020 entirelyuseless metaphysics probability science ai ai risk alexander kruel alphago eliezer yudkowsky final causes goals gpt 3 language language model nick bostrom openai sam altman self self reference technology truth at the end of may openai published a paper on gpt 3 a language model which is a successor to their previous version gpt 2 while quite impressive the reaction from many people interested in artificial intelligence has been seriously exaggerated sam altman openai s ceo has said as much himself the gpt 3 hype is way too much it s impressive thanks for the nice compliments but it still has serious weaknesses and sometimes makes very silly mistakes ai is going to change the world but gpt 3 is just a very early glimpse we have a lot still to figure out i used gpt n in the title here because most of the comments i intend to make are almost completely general and will apply to any future version that uses sufficiently similar methods what it does gpt 3 is a predictive language model that is given an input text it tries to predict what would come next much in the way that if you read the first few words of this sentence with the rest covered up you might try to guess what would be likely to come next to the degree that it does this well it can be used to generate text from a prompt that is we give it something like a few words or a few sentences and then add whatever it predicts should come next for example let s take this very blog post and see what gpt 3 would like to say what it doesn t do while gpt 3 does seem to be able to generate some pretty interesting results there are several limitations that need to be taken into account when using it first and foremost and most importantly it can t do anything without a large amount of input data if you want it to write like a real human you need to give it a lot of real human writing for most people this means copying and pasting a lot and while the program is able to read through that and get a feel for the way humans communicate you can t exactly use it to write essays or research papers the best you could do is use it as a fill in the blank tool to write stories and that s not even very impressive while the program does learn from what it reads and is quite good at predicting words and phrases based on what has already been written this method isn t very effective at producing realistic prose the best you could hope for is s...