The Master Algorithm Quotes

Our culture has become hooked on the quick-fix, the life hack, efficiency. Everyone is on the hunt for that simple action algorithm that nets maximum profit with the least amount of effort. There’s no denying this attitude may get you some of the trappings of success, if you’re lucky, but it will not lead to a calloused mind or self-mastery. If you want to master the mind and remove your governor, you’ll have to become addicted to hard work. Because passion and obsession, even talent, are only useful tools if you have the work ethic to back them up.

A learner that uses Bayes’ theorem and assumes the effects are independent given the cause is called a Naïve Bayes classifier. That’s because, well, that’s such a naïve assumption.

If you’re a lazy and not-too-bright computer scientist, machine learning is the ideal occupation, because learning algorithms do all the work but let you take all the credit.

You could even say that the God of Genesis himself is a programmer: language, not manipulation, is his tool of creation. Words become worlds.

People often think computers are all about numbers, but they’re not. Computers are all about logic.

Believe it or not, every algorithm, no matter how complex, can be reduced to just these three operations: AND, OR, and NOT.

A good learner is forever walking the narrow path between blindness and hallucination.

Our beliefs are based on our experience, which gives us a very incomplete picture of the world, and it's easy to jump to false conclusions.

data mining means “torturing the data until it confesses.

In fact, as time goes by, it becomes easier and easier to replace humans with computer algorithms, not merely because the algorithms are getting smarter, but also because humans are professionalising. Ancient hunter-gatherers mastered a very wide variety of skills in order to survive, which is why it would be immensely difficult to design a robotic hunter-gatherer. Such a robot would have to know how to prepare spear points from flint stones, how to find edible mushrooms in a forest, how to use medicinal herbs to bandage a wound, how to track down a mammoth and how to coordinate a charge with a dozen other hunters. However, over the last few thousand years we humans have been specialising. A taxi driver or a cardiologist specialises in a much narrower niche than a hunter-gatherer, which makes it easier to replace them with AI.

Listen to your customers, not to the HiPPO,” HiPPO being short for “highest paid person’s opinion.

Homo sapiens is the species that adapts the world to itself instead of adapting itself to the world.

God created not species but the algorithm for creating species.

Learning is forgetting the details as much as it is remembering the important parts.

Your job in a world of intelligent machines is to keep making sure they do what you want, both at the input (setting the goals) and at the output (checking that you got what you asked for).

Everyone is on the hunt for that simple action algorithm that nets maximum profit with the least amount of effort. There’s no denying this attitude may get you some of the trappings of success, if you’re lucky, but it will not lead to a calloused mind or self-mastery. If you want to master the mind and remove your governor, you’ll have to become addicted to hard work. Because passion and obsession, even talent, are only useful tools if you have the work ethic to back them up.

As so often happens in computer science, we’re willing to sacrifice efficiency for generality.

Common sense is important not just because your mom taught you so, but because computers don’t have it.

every time I fire a linguist, the recognizer’s performance goes up.

Our search for the Master Algorithm is complicated, but also enlivened, by the rival schools of thought that exist within machine learning. The main ones are the symbolists, connectionists, evolutionaries, Bayesians, and analogizers. Each tribe has a set of core beliefs, and a particular problem that it cares most about. It has found a solution to that problem, based on ideas from its allied fields of science, and it has a master algorithm that embodies it.

The second simplest algorithm is: combine two bits. Claude Shannon, better known as the father of information theory, was the first to realize that what transistors are doing, as they switch on and off in response to other transistors, is reasoning. (That was his master’s thesis at MIT—the most important master’s thesis of all time.)

Human mind is subject to the law of cause and effect. IF not, THEN you have no idea about IF-THEN algorithm.

Machine learning will not single-handedly determine the future, any more than any other technology; it’s what we decide to do with it that counts, and now you have the tools to decide.

Whoever has the best algorithms and the most data wins. A new type of network effect takes hold: whoever has the most customers accumulates the most data, learns the best models, wins the most new customers, and so on in a virtuous circle (or a vicious one, if you’re the competition).

For a Bayesian, in fact, there is no such thing as the truth; you have a prior distribution over hypotheses, after seeing the data it becomes the posterior distribution, as given by Bayes’ theorem, and that’s all.

Each of the five tribes of machine learning has its own master algorithm, a general-purpose learner that you can in principle use to discover knowledge from data in any domain. The symbolists’ master algorithm is inverse deduction, the connectionists’ is backpropagation, the evolutionaries’ is genetic programming, the Bayesians’ is Bayesian inference, and the analogizers’ is the support vector machine.

Learning algorithms are the seeds, data is the soil, and the learned programs are the grown plants. The machine-learning expert is like a farmer, sowing the seeds, irrigating and fertilizing the soil, and keeping an eye on the health of the crop but otherwise staying out of the way.

Bitcoin consists of: A decentralized peer-to-peer network (the bitcoin protocol) A public transaction ledger (the blockchain) A set of rules for independent transaction validation and currency issuance (consensus rules) A mechanism for reaching global decentralized consensus on the valid blockchain (Proof-of-Work algorithm)

Data alone is not enough. Starting from scratch will only get you to scratch.

Our goal is to figure out the simplest program we can write such that it will continue to write itself by reading data, without limit, until it knows everything there is to know.

(As Richard Feynman said, “What I cannot create, I do not understand.”)

Noise in machine learning just means errors in the data, or random events that you can’t predict.

Society is changing, one learning algorithm at a time.

If you had witnessed life on Earth up to ten thousand years ago, that would not have prepared you for what was to come.

You can download the learner I’ve just described from alchemy.cs .washington.edu.

Knowing how to do something isn’t much use if you can’t do it within the available time and memory,

Machine learners, like all scientists, resemble the blind men and the elephant: one feels the trunk and thinks it’s a snake, another leans against the leg and thinks it’s a tree, yet another touches the tusk and thinks it’s a bull. Our aim is to touch each part without jumping to conclusions; and once we’ve touched all of them, we will try to picture the whole elephant.

Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.

Armed with machine learning, a manager becomes a supermanager, a scientist a superscientist, an engineer a superengineer. The future belongs to those who understand at a very deep level how to combine their unique expertise with what algorithms do best.

Knowledge is traded in both directions—manually entered knowledge for use in learners, induced knowledge for addition to knowledge bases—but at the end of the day the rationalist-empiricist fault line runs right down that border, and crossing it is not easy.

Evolutionaries and connectionists have something important in common: they both design learning algorithms inspired by nature. But then they part ways. Evolutionaries focus on learning structure; to them, fine-tuning an evolved structure by optimizing parameters is of secondary importance. In contrast, connectionists prefer to take a simple, hand-coded structure with lots of connections and let weight learning do all the work. This is machine learning’s version of the nature versus nurture controversy, and there are good arguments on both sides.

We’re face-to-face with our old foe: the combinatorial explosion. Therefore we do what we always have to do in life: compromise. We make simplifying assumptions that whittle the number of probabilities we have to estimate down to something manageable. A very simple and popular assumption is that all the effects are independent given the cause. This means that, for example, having a fever doesn’t change how likely you are to also have a cough, if we already know you have the flu. Mathematically, this is saying that P(fever, cough | flu) is just P(fever | flu) × P(cough | flu). Lo and behold: each of these is easy to estimate from a small number of observations. In fact, we did it for fever in the previous section, and it would be no different for cough or any other symptom. The number of observations we need no longer goes up exponentially with the number of symptoms; in fact, it doesn’t go up at all.

Michelangelo said that all he did was see the statue inside the block of marble and carve away the excess stone until the statue was revealed. Likewise, an algorithm carves away the excess transistors in the computer until the intended function is revealed, whether it’s an airliner’s autopilot or a new Pixar movie. An

How do we learn? Is there a better way? What can we predict? Can we trust what we’ve learned? Rival schools of thought within machine learning have very different answers to these questions. The main ones are five in number, and we’ll devote a chapter to each. Symbolists view learning as the inverse of deduction and take ideas from philosophy, psychology, and logic. Connectionists reverse engineer the brain and are inspired by neuroscience and physics. Evolutionaries simulate evolution on the computer and draw on genetics and evolutionary biology. Bayesians believe learning is a form of probabilistic inference and have their roots in statistics. Analogizers learn by extrapolating from similarity judgments and are influenced by psychology and mathematical optimization.

Yet it is hard to see why artistic creation would be safe from the algorithms. Why are we so confident that computers will never be able to outdo us in the composition of music? According to the life sciences, art is not the product of some enchanted spirit or metaphysical soul, but rather of organic algorithms recognising mathematical patterns. If so, there is no reason why non-organic algorithms couldn’t master it.

Every algorithm has an input and an output: the data goes into the computer, the algorithm does what it will with it, and out comes the result. Machine learning turns this around: in goes the data and the desired result and out comes the algorithm that turns one into the other. Learning algorithms—also known as learners—are algorithms that make other algorithms. With machine learning, computers write their own programs, so we don’t have to.

prior probability that the sun will rise, since it’s prior to seeing any evidence. It’s not based on counting the number of times the sun has risen on this planet in the past, because you weren’t there to see it; rather, it reflects your a priori beliefs about what will happen, based on your general knowledge of the universe. But now the stars start to fade, so your confidence that the sun does rise on this planet goes up, based on your experience on Earth. Your confidence is now a posterior probability, since it’s after seeing some evidence. The sky begins to lighten, and the posterior probability takes another leap. Finally, a sliver of the sun’s bright disk appears above the horizon and perhaps catches “the Sultan’s turret in a noose of light,” as in the opening verse of the Rubaiyat. Unless you’re hallucinating, it is now certain that the sun will rise. The crucial question is exactly how the posterior probability should evolve as you see more evidence. The answer is Bayes’ theorem. We can think of it in terms of cause and effect. Sunrise causes the stars to fade and the sky to lighten, but the latter is stronger evidence of daybreak, since the stars could fade in the middle of the night due to, say, fog rolling in. So the probability of sunrise should increase more after seeing the sky lighten than after seeing the stars fade. In mathematical notation, we say that P(sunrise | lightening-sky), the conditional probability of sunrise given that the sky is lightening, is greater than P(sunrise | fading-stars), its conditional probability given that the stars are fading. According to Bayes’ theorem, the more likely the effect is given the cause, the more likely the cause is given the effect: if P(lightening-sky | sunrise) is higher than P(fading-stars | sunrise), perhaps because some planets are far enough from their sun that the stars still shine after sunrise, then P(sunrise | lightening sky) is also higher than P(sunrise | fading-stars).

The main ones are the symbolists, connectionists, evolutionaries, Bayesians, and analogizers. Each tribe has a set of core beliefs, and a particular problem that it cares most about. It has found a solution to that problem, based on ideas from its allied fields of science, and it has a master algorithm that embodies it. For symbolists, all intelligence can be reduced to manipulating symbols, in the same way that a mathematician solves equations by replacing expressions by other expressions. Symbolists understand that you can’t learn from scratch: you need some initial knowledge to go with the data. They’ve figured out how to incorporate preexisting knowledge into learning, and how to combine different pieces of knowledge on the fly in order to solve new problems. Their master algorithm is inverse deduction, which figures out what knowledge is missing in order to make a deduction go through, and then makes it as general as possible. For connectionists, learning is what the brain does, and so what we need to do is reverse engineer it. The brain learns by adjusting the strengths of connections between neurons, and the crucial problem is figuring out which connections are to blame for which errors and changing them accordingly. The connectionists’ master algorithm is backpropagation, which compares a system’s output with the desired one and then successively changes the connections in layer after layer of neurons so as to bring the output closer to what it should be. Evolutionaries believe that the mother of all learning is natural selection. If it made us, it can make anything, and all we need to do is simulate it on the computer. The key problem that evolutionaries solve is learning structure: not just adjusting parameters, like backpropagation does, but creating the brain that those adjustments can then fine-tune. The evolutionaries’ master algorithm is genetic programming, which mates and evolves computer programs in the same way that nature mates and evolves organisms. Bayesians are concerned above all with uncertainty. All learned knowledge is uncertain, and learning itself is a form of uncertain inference. The problem then becomes how to deal with noisy, incomplete, and even contradictory information without falling apart. The solution is probabilistic inference, and the master algorithm is Bayes’ theorem and its derivates. Bayes’ theorem tells us how to incorporate new evidence into our beliefs, and probabilistic inference algorithms do that as efficiently as possible. For analogizers, the key to learning is recognizing similarities between situations and thereby inferring other similarities. If two patients have similar symptoms, perhaps they have the same disease. The key problem is judging how similar two things are. The analogizers’ master algorithm is the support vector machine, which figures out which experiences to remember and how to combine them to make new predictions.

When I was a kid people used to say one could travel the entire world just by sitting in a library and reading books. Sadly, in the age of billionaire-controlled social media functioning and governing bodies and minds based on carefully engineered algorithms, I don’t believe this is true anymore. The saying should be revised in our times to be ‘one could hate the entire world and see everyone as a villain or an enemy just by browsing through reels and social posts carefully selected to confirm one’s limited knowledge, perspective, and prejudices.’ With that in mind, we need more than ever to master the art of traveling, whether we go near or far. We need to undo the unreasonable, amplified, and exaggerated fear of strangers." [From “Can We Travel Without Being Tourists?” published on CounterPunch on March 15, 2024]

Here are some practical Dataist guidelines for you: ‘You want to know who you really are?’ asks Dataism. ‘Then forget about mountains and museums. Have you had your DNA sequenced? No?! What are you waiting for? Go and do it today. And convince your grandparents, parents and siblings to have their DNA sequenced too – their data is very valuable for you. And have you heard about these wearable biometric devices that measure your blood pressure and heart rate twenty-four hours a day? Good – so buy one of those, put it on and connect it to your smartphone. And while you are shopping, buy a mobile camera and microphone, record everything you do, and put in online. And allow Google and Facebook to read all your emails, monitor all your chats and messages, and keep a record of all your Likes and clicks. If you do all that, then the great algorithms of the Internet-of-All-Things will tell you whom to marry, which career to pursue and whether to start a war.’ But where do these great algorithms come from? This is the mystery of Dataism. Just as according to Christianity we humans cannot understand God and His plan, so Dataism declares that the human brain cannot fathom the new master algorithms. At present, of course, the algorithms are mostly written by human hackers. Yet the really important algorithms – such as the Google search algorithm – are developed by huge teams. Each member understands just one part of the puzzle, and nobody really understands the algorithm as a whole. Moreover, with the rise of machine learning and artificial neural networks, more and more algorithms evolve independently, improving themselves and learning from their own mistakes. They analyse astronomical amounts of data that no human can possibly encompass, and learn to recognise patterns and adopt strategies that escape the human mind. The seed algorithm may initially be developed by humans, but as it grows it follows its own path, going where no human has gone before – and where no human can follow.

Sure, we can hear the reverberating echoes of the Big Bang. Yet that cosmic vibration tells us nothing about what was before the Big Bang, or what was before that, or how or why there was even a bang to be binged at all. This mostly wet ball full of ptarmigans, ponytails, and poverty is floating in space among a billion other balls, and there are galaxies swirling and there is a universe expanding, which itself may actually just be an undulating freckle on the cusp of something we can’t even conceive of, amid an endless soup of ever more unfathomables. And I find such a situation to be utterly, manifestly, psychedelically amazing—and far more spine-tinglingly awe-inspiring than any story I’ve ever read in the Bible, the Quran, the Vedas, the Upanishads, Dianetics, the Doctrine and Covenants, or the Tibetan Book of the Dead. So smell that satchel of tangerines and nimbly hammer a dulcimer or pluck a chicken and listen to your conscience or master a new algorithm or walk to work or hitch a ride. Because we’re here. And we will never, ever know why or exactly how this all comes about. That’s the situation. Deal with it. Accept it. Let the mystery be.

Lovelace defined as an ‘operation’ the control of material and symbolic entities beyond the second-order language of mathematics (like the idea, discussed in chapter 1, of an algorithmic thinking beyond the boundary of computer science). In a visionary way, Lovelace seemed to suggest that mathematics is not the universal theory par excellence but a particular case of the science of operations. Following this insight, she envisioned the capacity of numerical computers qua universal machines to represent and manipulate numerical relations in the most diverse disciplines and generate, among other things, complex musical artefacts: [The Analytical Engine] might act upon other things besides number, were objects found whose mutual fundamental relations could be expressed by those of the abstract science of operations, and which should be also susceptible of adaptations to the action of the operating notation and mechanism of the engine … Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent.

In many cases we can do this and avoid the exponential blowup. Suppose you’re leading a platoon in single file through enemy territory in the dead of night, and you want to make sure that all your soldiers are still with you. You could stop and count them yourself, but that wastes too much time. A cleverer solution is to just ask the first soldier behind you: “How many soldiers are behind you?” Each soldier asks the next the same question, until the last one says “None.” The next-to-last soldier can now say “One,” and so on all the way back to the first soldier, with each soldier adding one to the number of soldiers behind him. Now you know how many soldiers are still with you, and you didn’t even have to stop. Siri uses the same idea to compute the probability that you just said, “Call the police” from the sounds it picked up from the microphone. Think of “Call the police” as a platoon of words marching across the page in single file. Police wants to know its probability, but for that it needs to know the probability of the; and the in turn needs to know the probability of call. So call computes its probability and passes it on to the, which does the same and passes the result to police. Now police knows its probability, duly influenced by every word in the sentence, but we never had to construct the full table of eight possibilities (the first word is call or isn’t, the second is the or isn’t, and the third is police or isn’t). In reality, Siri considers all words that could appear in each position, not just whether the first word is call or not and so on, but the algorithm is the same. Perhaps Siri thinks, based on the sounds, that the first word was either call or tell, the second was the or her, and the third was police or please. Individually, perhaps the most likely words are call, the, and please. But that forms the nonsensical sentence “Call the please,” so taking the other words into account, Siri concludes that the sentence is really “Call the police.” It makes the call, and with luck the police get to your house in time to catch the burglar.

If the combination of mindless, profit-seeking algorithms, dedicated geopolitical adversaries, and corrupt US opportunists over the past few years has taught us anything, it is that serious applied thinking is a form of critical infrastructure. The best hackers are masters of applied thinking, and we cannot afford to ignore them.

They will watch as algorithms and robots easily outperform them at tasks and skills they spent their whole lives mastering. It will lead to a crushing feeling of futility, a sense of having become obsolete in one’s own skin.

[M]achine learning will bring about not just a new era of civilization, but a new stage in the evolution of life on earth.

All knowledge - past, present, and future - can be derived from data by a single, universal learning algorithm.

Evolution is the ultimate example of how much a simple learning algorithm can achieve given enough data. Its input is the experience and fate of all living creatures that ever existed. (Now that's big data.) On the other hand, it's been running for over three billion years on the most powerful computer on Earth: Earth itself.

[T]he greatest mystery in the universe is not how it begins or ends, or what infinitesimal threads it's woven from, it's what goes on in a small child's mind: how a pound of gray jelly can grow into the seat of consciousness.

Tomorrow's cyberspace will be a vast parallel world that selects only the most promising things to try out in the real one. It will be like a new, global subconscious, the collective id of the human race.

[T]he greatest benefit of machine learning may ultimately be not what the machines learn but what we learn by teaching them.

And then Homo technicus will evolve into a myriad different intelligent species, each with its own niche, a whole new biosphere as different from today's as today's is from the primordial ocean.

The scale shift of labour composition from the nineteenth to the twentieth centuries affected also the logic of automation, that is, the scientific paradigms involved in this transformation. The relatively simple industrial division of labour and its seemingly rectilinear assembly lines could easily be compared to a simple algorithm, a rulebased procedure with an ‘if/then’ structure which has its equivalent in the logical form of deduction. Deduction, not by coincidence, is the logical form that via Leibniz, Babbage, Shannon, and Turing innervated into electromechanical computation and eventually symbolic AI. Deductive logic is useful for modelling simple processes, but not systems with a multitude of autonomous agents, such as society, the market, or the brain.

For Brouwer, and his followers (the intuitionists) , the constructive real numbers described above do hot constitute all of the real number system. In addition there are incompletely determined real numbers, corresponding to sequences of rational numbers whose terms are not specified by a master algorithm. Such sequences are called "free-choice sequences", because the creating subject, who defines the sequence, does not completely commit himself in advance but allows himself some freedom of choice along the way in defining the individual terms of the sequence.

As any word implies a grammar, any number hides an algorithm – that is, a procedure for representing quantities and for performing operations with quantities. In conclusion, all numbers are algorithmic numbers as they are manufactured by those algorithms that are the systems of numerations. Numerals count nothing (so to speak); they are simply position holders in a procedure – an algorithm – of quantification.

Every algorithm, no matter how complex, can be reduced to just these three operations: AND, OR, and NOT.

The shadowy masters of industrial data mining eviscerate personal privacy from behind a veil of corporate secrecy. We’ll see this dynamic repeatedly: corporate secrecy expands as the privacy of human beings contracts.

why is it that the simple, abstract language of mathematics can accurately capture so much of our infinitely complex world?

Computers are useless,” said Picasso. “They can only give you answers.” Computers aren’t supposed to be creative; they’re supposed to do what you tell them to. If what you tell them to do is be creative, you get machine learning. A learning algorithm is like a master craftsman: every one of its productions is different and exquisitely tailored to the customer’s needs.

Columbus was not the first person to discover America, but the last.

They point out that we never know for sure which hypothesis is the true one, and so we shouldn’t just pick one hypothesis, like a value of 0.7 for the probability of heads; rather, we should compute the posterior probability of every possible hypothesis and entertain all of them when making predictions. The sum of the probabilities of all the hypotheses must be one, so if one becomes more likely, the others become less. For a Bayesian, in fact, there is no such thing as the truth; you have a prior distribution over hypotheses, after seeing the data it becomes the posterior distribution, as given by Bayes’ theorem, and that’s all.

Symbolists don’t like probabilities and tell jokes like “How many Bayesians does it take to change a lightbulb? They’re not sure. Come to think of it, they’re not sure the lightbulb is burned out.

How many Bayesians does it take to change a lightbulb? They’re not sure.

Thousands of decision tree users can’t be wrong, you think, and sketch one to predict your friend’s reply when you ask her out: According to this tree, tonight she’ll say yes.

theorem says that P( cause | effect) = P( cause) × P( effect | cause) / P( effect). Replace cause by A and effect by B and

In this book we will synthesize a single algorithm will all these capabilities: Our quest will take us across the territory of each of the five tribes.

The Industrial Revolution automated manual work and the Information Revolution did the same for mental work, but machine learning automates automation itself.

Evolution is an algorithm.

Motivated by my research and examples such as Feynman, I decided that focusing my attention on a bottom-up understanding of my own field’s most difficult results would be a good first step toward revitalizing my career capital stores. To initiate these efforts, I chose a paper that was well cited in my research niche, but that was also considered obtuse and hard to follow. The paper focused on only a single result—the analysis of an algorithm that offers the best-known solution to a well-known problem. Many people have cited this result, but few have understood the details that support it. I decided that mastering this notorious paper would prove a perfect introduction to my new regime of self-enforced deliberate practice. Here

Perhaps in a future cyber-court, in session somewhere on Amazon’s cloud, a robo-lawyer will beat the speeding ticket that RoboCop issued to your driverless car, all while you go to the beach, and Leibniz’s dream of reducing all argument to calculation will finally have come true.

Your digital future begins with a realization: every time you interact with a computer—whether it’s your smart phone or a server thousands of miles away—you do so on two levels. The first one is getting what you want there and then: an answer to a question, a product you want to buy, a new credit card. The second level, and in the long run the most important one, is teaching the computer about you.

By knowing what learners optimize, we can make certain they optimize what we care about, rather than what came in the box.

Before we can discover deep truths with machine learning, we have to discover deep truths about machine learning.

At the time of this writing, the difficulty is so high that it is profitable only to mine with application-specific integrated circuits (ASIC), essentially hundreds of mining algorithms printed in hardware, running in parallel on a single silicon chip.

Once upon a time we relied on shamans and soothsayers for this, but they were much too fallible. Science’s predictions are more trustworthy, but they are limited to what we can systematically observe and tractably model.

All knowledge—past, present, and future—can be derived from data by a single, universal learning algorithm.

Evolution is the ultimate example of how much a simple learning algorithm can achieve given enough data.

If computers make us smarter, computers running the Master Algorithm will make us feel like geniuses.

At the epicenter of Google’s bulging portfolio is one master project: The company wants to create machines that replicate the human brain, and then advance beyond. This is the essence of its attempts to build an unabridged database of global knowledge and its efforts to train algorithms to become adept at finding patterns, teaching them to discern images and understand language. Taking on this grandiose assignment, Google stands to transform life on the planet, precisely as it boasted it would. The laws of man are a mere nuisance that can only slow down such work. Institutions and traditions are rusty scrap for the heap. The company rushes forward, with little regard for what it tramples, on its way toward the New Jerusalem. (less)

One of the early stage AI companies Google purchased is DeepMind, based in London. In 2015 researchers at DeepMind published a paper in Nature describing how they taught an AI to learn to play 1980s-era arcade video games, like Video Pinball. They did not teach it how to play the games, but how to learn to play the games—a profound difference. They simply turned their cloud-based AI loose on an Atari game such as Breakout, a variant of Pong, and it learned on its own how to keep increasing its score. A video of the AI’s progress is stunning. At first, the AI plays nearly randomly, but it gradually improves. After a half hour it misses only once every four times. By its 300th game, an hour into it, it never misses. It keeps learning so fast that in the second hour it figures out a loophole in the Breakout game that none of the millions of previous human players had discovered. This hack allowed it to win by tunneling around a wall in a way that even the game’s creators had never imagined. At the end of several hours of first playing a game, with no coaching from the DeepMind creators, the algorithms, called deep reinforcement machine learning, could beat humans in half of the 49 Atari video games they mastered.

Despite all its successes, machine learning is still in the alchemy stage of science.

Evolution, then, is another promising path to the Master Algorithm.

Overfitting happens when you have too many hypotheses and not enough data to tell them apart.

OK, some say, machine learning can find statistical regularities in data, but it will never discover anything deep, like Newton’s laws. It arguably hasn’t yet, but I bet it will.

The Master Algorithm would provide a unifying view of all of science and potentially lead to a new theory of everything.

Even plain old electricity is a kind of unifier: you can generate it from many different sources—coal, gas, nuclear, hydro, wind, solar—and consume it in an infinite variety of ways. A power station doesn’t know or care how the electricity it produces will be consumed, and your porch light, dishwasher, or brand-new Tesla are oblivious to where their electricity supply comes from. Electricity is the Esperanto of energy. The

Science’s predictions are more trustworthy, but they are limited to what we can systematically observe and tractably model. Big data and machine learning greatly expand that scope. Some everyday things can be predicted by the unaided mind, from catching a ball to carrying on a conversation. Some things, try as we might, are just unpredictable. For the vast middle ground between the two, there’s machine learning.

Machine learning plays a part in every stage of your life. If

Netflix’s algorithm has a deeper (even if still quite limited) understanding of your tastes than Amazon’s, but ironically that doesn’t mean Amazon would be better off using it. Netflix’s business model depends on driving demand into the long tail of obscure movies and TV shows, which cost it little, and away from the blockbusters, which your subscription isn’t enough to pay for. Amazon has no such problem; although it’s well placed to take advantage of the long tail, it’s equally happy to sell you more expensive popular items, which also simplify its logistics. And we, as customers, are more willing to take a chance on an odd item if we have a subscription than if we have to pay for it separately.

If your main interest is in the business uses of machine learning, this book can help you in at least six ways: to become a savvier consumer of analytics; to make the most of your data scientists; to avoid the pitfalls that kill so many data-mining projects; to discover what you can automate without the expense of hand-coded software; to reduce the rigidity of your information systems; and to anticipate some of the new technology that’s coming your way. I’ve seen too much time and money wasted trying to solve a problem with the wrong learning algorithm, or misinterpreting what the algorithm said. It doesn’t take much to avoid these fiascoes. In fact, all it takes is to read this book.