The Master Algorithm How the Quest for the Ultimate

A thought provoking and wide ranging exploration of machine learning and the race to build computer intelligences as flexible as our ownIn the world's top research labs and universities the race is on to invent the ultimate learning algorithm one capable of discovering any knowledge from data and doing anything we want before we even ask In The Master Algorithm Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google and your smartphone He assembles a blueprint for the future universal learner the Master Algorithm and discusses what it will mean for business science and society If data ism is today's philosophy this book is its bible


10 thoughts on “The Master Algorithm How the Quest for the Ultimate Learning Machine Will Remake Our World

  1. says:

    Domingos wants to cover all of machine learning for the layman but it winds up being a big mess This is quite possibly the single worst thing I have read in my life about machine learningThe level of explanation veers wildly from ridiculously oversimplified to technical minutiae It is confusing than enlightening as it goes through topics in an almost random order scattering them all throughout the book You would think that Hume's problem of induction the underdetermination of data Occam's razor the curse of dimensionality and overfitting would all be discussed in one and the same place in order to set the stage for how the various 'tribes' work but you would be wrong The manic stream of consciousness writing style also drives me nuts and the little medieval fantasy passages come off as puerile I smiled Once The explanations are almost uniformly terrible another reviewer asks if this is the worst explanation of Bayesian inference one has ever read I would have to say that at least for me this is competitive for that distinction and most are explained as briefly as decision trees are endlessly waxed upon Major premises like there being any really universal algorithm are poorly presented compare Domingo's argument for there being a neural algorithm with say Jacob Cannell's The Brain as a Universal Learning Machine where Domingos is provoking rather than thought provokingContent wise I have to seriously question the inclusion of evolutionary programming as a top tier paradigm and analogies hardly seem much relevant a grouping either and all that space comes at a huge cost of extreme superficiality about what deep learning is doing right now Let me remark on how astounding it is to read a book whose self proclaimed goal is to de mystify machine learning for the layman explain recent advances in deep learning that have created such media hype and sparked so much commercial public research interest and which seems to only go from strength to strength to the point where sometimes it feels like one can hardly even skim a fascinating paper before yet another one has been uploaded to Arxiv and which winds up doing little but explaining what backpropagation is and then passing grandiosely onto other topics and not y'know covering anything like solving ImageNet caption generation logical inference using reading of passages etc Or to read a decent capsule description of the general paradigm of reinforcement learning and then see deep reinforcement learning described in a few sentences mostly to the effect that learning can be unstable really? That is what laymen need to know about deep reinforcement learning that whatever it is it can be unstable?Oh and he offers us his thoughts on AI risk the fruit of his decades of experience with machine learningRelax The chances that an AI equipped with the Master Algorithm will take over the world are zero The reason is simple unlike humans computers don’t have a will of their own They’re products of engineering not evolution Even an infinitely powerful computer would still be only an extension of our will and nothing to fearThe optimizer then does everything in its power to maximize the evaluation function—no and no less—and the evaluation function is determined by us A powerful computer will just optimize it better There’s no risk of it getting out of control even if it’s a genetic algorithm A learned system that didn’t do what we want would be severely unfit and soon die out In fact it’s the systems that have even a slight edge in serving us better that will generation after generation multiply and take over the gene pool Of course if we’re so foolish as to deliberately program a computer to put itself above us then maybe we’ll get what we deserve The same reasoning applies to all AI systems because they all—explicitly or implicitly—have the same three components They can vary what they do even come up with surprising plans but only in service of the goals we set them A robot whose programmed goal is “make a good dinner” may decide to cook a steak a bouillabaisse or even a delicious new dish of its own creation but it can’t decide to murder its owner any than a car can decide to fly away The purpose of AI systems is to solve NP complete problems which as you may recall from Chapter 2 may take exponential time but the solutions can always be checked efficiently We should therefore welcome with open arms computers that are vastly powerful than our brains safe in the knowledge that our job is exponentially easier than theirsHow I wish I was making up these arguments Aside from the invocation of complexity theory which is not even wrong as many problems we want AI to solve are not expressible as decision problems the ones which are can fall into anything of 'much easier than NP complete' or 'much harder' and a problem falling into a particular complexity class is no guarantee of safety in the first place this sort of naiveté is sad coming from someone so enthusiastic about genetic algorithms where researchers routinely discover that defining a good rewardfitnessevaluation function is quite difficult and they have to fight their algorithms to get a useful rather than a hilariously perversely correct answerOverall I would absolutely recommend against this book for any laymen interested in statistics or machine learning The explanations are so poor and garbled that you will either not learn anything or what you take away will be as likely to be misleading as not You will be better off with Silver's The Signal and the Noise reading random presentations on Schmidhuber's website Bostrom's Superintelligence or even Hutter's Machine Super Intelligence or Domingos's own A Few Useful Things to Know about Machine Learning which was really good or anything really Suggestions are welcomed on things I can recommend for laymen instead of this