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Machine Learning
- abainbridge 9y agoTrue that. But you could replace "Machine Learning System" with "code base" and "linear algebra" with "C++" and it would be an accurate description of most corporate software development I've seen.
- H4CK3RM4N 9y agoI think this is one of Munroe's general digs at corporate culture, and not specifically aimed at machine learning, that's just the current buzzword.
- TeMPOraL 9y agoIt feels like a dig at how people do machine learning these days though. My impression is that people just take TensorFlow and a random neural net, and keep throwing gigabytes of data at it until they feel the results look like they should...
- sverhagen 9y agoCourtesy of "Machine Learning in $(some single digit number) Minutes".
- TheOtherHobbes 9y agoRandom Neural Nets should totally be a real thing. I mean more than they already are.
- mpweiher 9y agoAt University, we did neural nets via genetic algorithms. Does that count?
- majewsky 9y agoBingo. :)
- tincholio 9y agoThey are, actually: https://en.wikipedia.org/wiki/Random_neural_network https://en.wikipedia.org/wiki/Random_neural_network
- visarga 9y ago> Random Neural Nets should totally be a real thing. Reservoir computing. Some are critical of this method.
- tgb 9y agoThere's a long history of that: http://catb.org/jargon/html/koans.html#id3141241 http://catb.org/jargon/html/koans.html#id3141241
- s_kilk 9y agoEh, we (collectively) have a pretty good grip on how code-bases, linear algebra and C++ work, but we still have no idea how minds and intelligence work. And so the best we can do with ML right now is stumble blindly in the dark, poking and prodding things until it 'seems' to give the right answers, some of the time.
- bjoernbu 9y agoImho, it's more typical of machine learning. Sure, both can be very similar and are often signs of a lack of deeper understanding. I'm no ML expert by any means, but I've seen several bachelor/master thesis and even ML competitions where ensembles performed best. Sure, this isn't necessarily aimless stirring and could combine models that really capture different aspects of the data. But often enough it's just several algorithms that do the same general thing, combined to achieve a slightly higher score. Imho this is most relevant when competitions provide data that is not readable by humans (e.g. simplified: "classify these documents where all words are given as word IDs and never as actual strings"). To me this has a touch of pouring in data, stirring (build many classifiers and plug them together in an ensemble), and getting answers on the right side. Optimizing hyper parameters goes in a similar direction, imho. I can really see an analogy to stirring
- marcosdumay 9y agoYou don't throw random C++ code at the compiler to see if you get the correct result. At least, most of the time you don't.
- jey 9y ago"Just stir the pile until [the answers] start looking right" is actually a pretty decent description of gradient descent.
- denzil_correa 9y agoI'm not so sure. Gradient Descent refers to reaching the best solution available. Xkcd seems to be referring to more like the Bonferroni principle where you are looking for patterns without a hypothesis and post facto justifying it.
- ardivekar 9y agoAKA half the work done in Machine Learning nowadays.
- YeGoblynQueenne 9y agoI think the point is about how data manipulation is about 80% of machine learning work. If an algorithm is giving you crap results for some data set, once you've fiddled with its hyperparameters through cv and so on there's not much you can do besides data manipulations like PCA, ICA and the like, to try and get a better result. Most algorithms work pretty badly with raw data. I'm guessing that, in the sciences, that is a big no-no. Imagine if doctors, seeing that a new drug being trialled is failing to cure a disease, simply started chucking out the sick subjects until all the ones that were left where healthy, declaring the sick ones to be "noise" and the trial a success. Somehow, I don't think that would fly...
- denzil_correa 9y ago> I think the point is about how data manipulation is about 80% of machine learning work. If an algorithm is giving you crap results for some data set, once you've fiddled with its hyperparameters through cv and so on there's not much you can do besides data manipulations like PCA, ICA and the like, to try and get a better result. Data manipulation is different from data transformation. Manipulation changes the nature of data, transformation does not change the nature of data. PCA is data transformation, not data manipulation.
- thriftwy 9y agoAs famous russo-ukrainian sentence goes, "is it a defeat or a victory?"
- toxicFork 9y ago"Yes"
- kozak 9y agoI don't know what is the Russian equivalent of this saying, but the Ukrainian version that got very popular recent years has an interesting culturological twist. The word that is used as an antonym of "victory" does not translate to English as "defeat", but instead it literally means "betrayal". This is because in Ukrainian history we almost never lost by being defeated, and almost every time our defeats were results of betrayal. And same thing applies to the ongoing war: we don't really fear a military defeat as much as a political betrayal (either internal or external).
- rcarmo 9y agoThis is _so_ on point considering a lot of my customer interactions. Until I go in and go over establishing a process framework that goes through the models in use and sets criteria for their evaluation (even something as simple as ROC), a lot of ML work in companies is mostly tinkering with things (sometimes with wrong theoretical underpinnings) until it performs adequately. The hype is only real if you systematically work it into measurable process, not virtuoso jamming.
- Kholo 9y agoStone age tools as Chomsky calls them. The problem with simple to use tools, is the simplicity masks two key facts from the user - whether aptitude for the tool exists & amount of effort and time to mastery.
- jakozaur 9y agoSo far Deep Learning has made this Xkcd obsolete: https://xkcd.com/1425/ https://xkcd.com/1425/ Check whether a photo is of a bird.
- denzil_correa 9y agoIt DID take a research team and ~ 5 years to get there? :)
- seren 9y agoInteresting, it shows how things can move quicker than expected, even when judged by enlightened people. Is there a way to know the date (1425) was released ?
- spatulan 9y ago24th September 2014
- bryanrasmussen 9y agoProgrammers often have Scotty syndrome and pad their time estimates to look like geniuses when they solve ahead of schedule.
- afarrell 9y ago> to look like geniuses when they solve ahead of schedule No, it is because estimating software tasks is difficult, the penalty for underestimating is that people think you are dishonest/flakey, and there isn't anywhere to get an education in how to do it well. The default advice given to junior engineers is therefore: "take your intuition and triple it." I hate that this is the state of the industry. My interactions around estimation over the past 5 years since uni have literally made me feel nauseated and near fainting on multiple occasions. I would love for Joel or Klamezius or Uncle Bob or someone else to fix it and produce a good course on how to create estimates.
- pasta 9y agoIt's funny that this also seems to work for humans. When the outcome is bad/not what humanity wants, we get/give a negative response and hope the outcome next time will be better.
- lngnmn 9y agoI like a different analogy. In old times the oracles and shamans danced in masks wielding amulets of power and magical weapons. Modern day shamans dance with datasets and clusters, but the main principle is the same. If my performance produced a desired effect (correlation does not imply causation), that is, obviously, due to my undoubted magic powers. If it fail, which is usually the case, that is because the data was not big enough and there was not enough money given to perform big-enough sacrifice to please the gods. We actually might learn a lot from Tibetan tantric practitioners. It seems that Wall street guys and economists did.
- sgt101 9y agoMy advice : if you see a pile of sticks growing in the car park, make a swift exit.
- stared 9y agoFor webcomics and ML, see this footnote: http://p.migdal.pl/2017/04/30/teaching-deep-learning.html#fn:webcomics http://p.migdal.pl/2017/04/30/teaching-deep-learning.html#fn... "It made a few episodes of webcomics obsolete: xkcd: Tasks (totally, by Park or Bird?), xkcd: Game AI) (partially, by AlphaGo), PHD Comics: If TV Science was more like REAL Science (not exactly, but still it’s cool, by LapSRN)"
- Houshalter 9y agoReminds me of this horrifying stack exchange post: https://stats.stackexchange.com/questions/185507/what-happens-if-the-explanatory-and-response-variables-are-sorted-independently https://stats.stackexchange.com/questions/185507/what-happen...
- SBCRec 9y agowow, that is actually horrifying.
- v4nn4 9y agoWtf wow
- jmkni 9y agoELI5?
- TeMPOraL 9y agoThe manager of the person who asked the question thinks that if you take data in form of pairs (X, Y), split them up, sort independently and combine again, you'll get better results. In fact, such operation obviously destroys the relationship X had to Y, so the result is meaningless.
- rufusroflpunch 9y agoNot hot dog.
- lottin 9y agoThere is this famous paper "I Just Ran Two Million Regressions". Any half decent statistician will tell you what's wrong with that type of approach.
- marcosdumay 9y agoIn machine learning circles, this is called "overfitting".
- joe_the_user 9y agoCould one describe machine learning as "combine enough bad statistical tests that the cries of 'it seems to work' from managers drown out all the 'it's wrong' cries from the statisticians"
- Sophira 9y agoTom Scott yesterday made a video for laypeople on the topic of 'black box' machine learning and how it can be difficult to get it to behave as you want, too.[0] It's an interesting watch - I'd recommend it if you're interested in learning about it. (Heck, I'd recommend the channel. Tom does some great videos on a number of different topics.) [0] https://www.youtube.com/watch?v=BSpAWkQLlgM https://www.youtube.com/watch?v=BSpAWkQLlgM
- in9 9y agoHis second channel is very nice as well. More casual and conversation based, and a bit less interesting on the topics it self. But nice nonetheless
- flinty 9y agoIsn't he the guy from the hello internet podcast?
- cec 9y agoNo, HI is Brady Haran and CGP Grey, though I suspect Brady may have collaborated with Tom.
- vsbosvubo 9y agoCouldn't a DNN be trained to inspect other DNNs and generate human-readable explanations of how they work ? In the fewest words, with maximum poetry
- YeGoblynQueenne 9y agoBrilliant idea! Why not try it out yourself and see how it goes?
- vsbosvubo 9y agodon't know anything about DNNs!
- YeGoblynQueenne 9y agoSeems to be releated to this recent one: https://xkcd.com/1831/ https://xkcd.com/1831/ Something tells me Rundall Munroe had a run-in with some over-eager data scientists recently.
- YeGoblynQueenne 9y agoSeems to be releated to this recent one: https://xkcd.com/1831/ https://xkcd.com/1831/ Something tells me Rundall Munroe had a run-in with some over-eager data scientists recently.
- MarkMMullin 9y agoI'm waiting for the followon - I stirred it up, I got good results, and now I can't seem to reproduce them again :-(
- canjobear 9y agoThat's some nice snarkiness about how modern machine learning works. But let's not forget that this apparently "dumb" approach has beaten out much more intelligent seeming systems on many tasks. To me, this means we shouldn't be so confident that we know what an intelligent system looks like. Maybe effectiveness in AI doesn't have much to do with human interpretability, and even less to do with whether humans find the approach intellectually satisfying.
- kbutler 9y agoIs it survivorship bias? ML attempts that fail are cancelled and never heard from again, those that succeed are publicized. The same approach works with pig entrails: a bunch of people make predictions, the ones that fail go away, the ones that happen to succeed a few times "must work". Or in stock market terms, "past performance is no guarantee of future results."
- canjobear 9y agoIn a way you're exactly correct and that's exactly why machine learning works. The models specify a huge range of possible input-output mappings and then do a dumb search to find the ones that work; the mappings that don't succeed are discarded. It turns out, surprisingly, this is the best approach we have come up with. > Or in stock market terms, "past performance is no guarantee of future results." Past performance isn't a guarantee, but under mild conditions it is very strong evidence that there will be future results.
- YeGoblynQueenne 9y agoAnd lets' not forget that one lucky hit with pig entrails is enough to build a career that lasts a couple of decades. I'm trying to remember where I read about this but, allegedly, there used to be a gentleman in New Orleans (if memory serves) who went around handing, at random, sealed envelopes with "boy" or "girl" written on a piece of paper inside, to pregnant women. The idea was that he could expect to hit the right sex of the unborn child a few times and that those lucky hits would make people think he had some sort of gift for seeing the future. As to the misses, the women would be too preoccupied with having just given birth to raise a stink. Note the envelopes were handed out for free. It was his advertisement, see.
- itissid 9y agoSo I am curious, like to a software engineer who has never learnt much of stats how difficult has it been for them using these things in the field to solve problems with, if they are any doing so? Like do they have a problem because they don't have a grounding in stats or do these things pretty much "work" assuming the models are coped to fit the problems.
- 21 9y agoCan we load all XKCDs into a neural net (LSTM, or something), and train it to "dream" new ones? Like the neural nets which generated non-sense, but realistic looking C++ code.
- jacquesm 9y agoThis thread is hilarious. ML is powering most or even all self driving car efforts underway, powers online translation services, numerous vision projects and speech recognition besides winning competitions meeting or exceeding human performance on the same data. I'm just as allergic to those that hype some technology as I am to those that will snarkily discard something with arguments that have already been laid to rest, in some cases multiple years ago. Xkcd is fun, but isn't necessarily prescient nor does it have to be accurate, when this cartoon was published the writing was already on the wall and that's 3 years ago. It's fine to be skeptical about new technology but before you start criticizing it make sure that you have at least a rough idea of where things stand lest you end up looking foolish. Sure, ML is abused and if we're not careful we will see another AI winter because of silly hype and ascribing near magical properties to ML. But at the same time snark, condescension and a-priori dismissal of what is most likely the biggest landslide in computing since the smartphone is - especially on a site that deals with both hacking and novelty - something that I would not expect. Compared to the HN love for the next JS framework or language fad this attitude is surprising to say the least.
- ganwar 9y agoThe second part quite accurately describes most of the generalization techniques. Especially when it comes to deep learning.
- topologie 9y agoDo you think it's connected in any way to https://www.wired.com/2013/02/big-data-means-big-errors-people/ https://www.wired.com/2013/02/big-data-means-big-errors-peop... ??? If not, what is your interpretation of the XKCD cartoon?