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December 30, 2015 - January 22, 2016
A bigger problem is that, surprisingly, having more attributes can be harmful even when they’re all relevant. You’d think that more information is always better—isn’t that the motto of our age? But as the number of dimensions goes up, the number of training examples you need to locate the concept’s frontiers goes up exponentially.
Constrained optimization is the problem of maximizing or minimizing a function subject to constraints.
If the Master Algorithm is not analogy, it must surely be something like it.
The real world is made up of objects that persist over time, not random pixels changing arbitrarily from one moment to the next.
or is it ?
A whole market is too coarse, and individual customers are too fine, so marketers divide markets into segments, which is their word for clusters.
We can represent a cluster by its prototypical element: the image of your mother that you see with your mind’s eye or the quintessential cat, sports car, country house, or tropical beach.
Once I reclassify animal crackers in the banana group, perhaps the prototypical item for that group also changes, from a banana to a cookie. This virtuous cycle, with entities assigned to better and better clusters, continues until the assignment of entities to clusters doesn’t change (and therefore neither do the cluster prototypes).
Dimensionality reduction is essential for coping with big data—like the data coming in through your senses every second. A picture may be worth a thousand words, but it’s also a million times more costly to process and remember.
Psychologists have found that personality boils down to five dimensions—extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience—which they can infer from your tweets and blog posts.
Netflix uses a similar idea. Instead of just recommending movies that users with similar tastes liked, it first projects both users and movies into a lower-dimensional “taste space” and recommends a movie if it’s close to you in this space. That way it can find movies for you that you never knew you’d love.
Like a librarian arranging books on a shelf, time places each image next to its most similar ones. Perhaps our perception of it is just a natural result of our brains’ dimensionality reduction prowess. In the road network of memory, time is the main thoroughfare, and we soon find it. Time, in other words, is the principal component of memory.
Pleasure travels back through time, so to speak, and actions can eventually become associated with effects that are quite remote from them.
A company may make a change that brings in more revenue in the short term—like selling an inferior product that costs less to make for the same price as the original superior product—but miss seeing that doing this will lose customers in the longer term.
Reinforcement learners solve this by sometimes choosing the best action and sometimes a random one. (The brain even seems to have a “noise generator” for this purpose.)
Early on, when there’s much to learn, it makes sense to explore a lot. Once you know the territory, it’s best to concentrate on exploiting it. That’s what humans do over their lifetimes: children explore, and adults exploit (except for scientists, who are eternal children). Children’s play is a lot more serious than it looks; if evolution made a creature that is helpless and a heavy burden on its parents for the first several years of its life, that extravagant cost must be for the sake of an even bigger benefit. In effect, reinforcement learning is a kind of speeded-up evolution—trying,
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The brain does it, using the neurotransmitter dopamine to propagate differences between expected and actual rewards.
You get up, get dressed, eat breakfast, and drive to work, all the while thinking about something else. Below the surface, reinforcement learning continually orchestrates and fine-tunes this prodigious symphony of motion. Snippets of reinforcement learning, also known as habits, make up most of what you do.
That’s why telephone numbers have hyphens: 1-723-458-3897 is much easier to remember than 17234583897.
Newell’s longtime collaborator and AI cofounder, had earlier found that the main difference between novice and expert chess players is that novices perceive chess positions one piece at a time while experts see larger patterns involving multiple pieces.
Chunking and reinforcement learning are not as widely used in business as supervised learning, clustering, or dimensionality reduction, but a simpler type of learning by interacting with the environment is: learning the effects of your actions (and acting accordingly).
Philosophical fine points aside, learning causality is learning the effects of your actions, and anyone with a stream of data they can affect can do it—from a one-year-old splashing around in the bathtub to a president campaigning for reelection.
In all of these cases, the best way to understand an entity—whether it’s a person, an animal, a web page, or a molecule—is to understand how it relates to other entities. This requires a new kind of learning that doesn’t treat the data as a random sample of unrelated objects but as a glimpse into a complex network.
Netflix, Watson, Kinect, and countless others use it, and it’s one of the most powerful arrows in the machine learner’s quiver.
The Netflix Prize winner used metalearning to combine hundreds of different learners. Watson uses it to choose its final answer from the available candidates. Nate Silver combines polls in a similar way to predict election results.
The symbolists’ formal language is logic, of which rules and decision trees are special cases. The connectionists’ is neural networks. The evolutionaries’ is genetic programs, including classifier systems. The Bayesians’ is graphical models, an umbrella term for Bayesian networks and Markov networks. The analogizers’ is specific instances, possibly with weights, as in an SVM.
For one, the proof of the pudding is in the eating,
What did evolution assume when it began its long journey from the first bacteria to all the life-forms around today? I think there’s a simple assumption from which all else follows: the learner is part of the world. This means that the learner as a physical system obeys the same laws as its environment, whatever they are, and therefore already “knows” them implicitly and is primed to discover them.
One of Alchemy’s largest applications to date was to learn a semantic network (or knowledge graph, as Google calls it) from the web.
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.
And, after the match was made, it would team up with your date’s model to pick some restaurants you might both like. Which is where things start to get really interesting.
Facebook’s main use for all this knowledge is to target ads to you. In return, it provides the infrastructure for your sharing. That’s the bargain you make when you use Facebook. As its learning algorithms improve, it gets more and more value out of the data, and some of that value returns to you in the form of more relevant ads and better service. The only problem is that Facebook is also free to do things with the data and the models that are not in your interest, and you have no way to stop it.
But everyone has only a sliver of it. Google sees your searches, Amazon your online purchases, AT&T your phone calls, Apple your music downloads, Safeway your groceries, Capital One your credit-card transactions. Companies like Acxiom collate and sell information about you, but if you inspect it (which in Acxiom’s case you can, at aboutthedata.com), it’s not much, and some of it is wrong. No one has anything even approaching a complete picture of you.
In sum, all four kinds of data sharing have problems. These problems all have a common solution: a new type of company that is to your data what your bank is to your money. Banks don’t steal your money (with rare exceptions). They’re supposed to invest it wisely, and your deposits are FDIC-insured. Many companies today offer to consolidate your data somewhere in the cloud, but they’re still a far cry from your personal data bank.
As Alexis Madrigal of the Atlantic points out, today your profile can be bought for half a cent or less, but the value of a user to the Internet advertising industry is more like $1,200 per year. Google’s sliver of your data is worth about $20, Facebook’s $5, and so on.
You wouldn’t let the first or second half of your brain have divided loyalties, so why would you let the third?
How much of your brain does your job use? The more it does, the safer you are.
Robots assemble cars, but they haven’t replaced construction workers. On the other hand, machine-learning algorithms have replaced credit analysts and direct marketers.
As it turns out, evaluating credit applications is easier for machines than walking around a construction site without tripping,
The best chess players these days are so-called centaurs, half-man and half-program. The same is true in many other occupations, from stock analyst to baseball scout. It’s not man versus machine; it’s man with machine versus man without. Data and intuition are like horse and rider, and you don’t try to outrun a horse; you ride it.
Who gets credit, who buys what, who gets what job and what raise, which stocks will go up and down, how much insurance costs, where police officers patrol and therefore who gets arrested, how long their prison terms will be, who dates whom and therefore who will be born: machine-learned models already play a part in all of these. The point where we could turn off all our computers without causing the collapse of modern civilization has long passed.
The reason is simple: unlike humans, computers don’t have a will of their own. They’re products of engineering, not evolution.
Technology is the extended phenotype of man. This means we can continue to control it even if it becomes far more complex than we can understand.
Picture two strands of DNA going for a swim in their private pool, aka a bacterium’s cytoplasm, two billion years ago. They’re pondering a momentous decision. “I’m worried, Diana,” says one. “If we start making multicellular creatures, will they take over?” Fast-forward to the twenty-first century, and DNA is still alive and well. Better than ever, in fact, with an increasing fraction living safely in bipedal organisms comprising trillions of cells. It’s been quite a ride for our tiny double-stranded friends since they made their momentous decision. Humans are their trickiest creation yet;
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all the decisions that computers make today—who gets credit, for example—you’ll find that they’re often needlessly bad. Yours would be too, if your brain was a support vector machine and all your knowledge of credit scoring came from perusing one lousy database.
People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.
He goes even further to claim that the entire history of life on Earth, not just human technology, shows exponentially accelerating progress, but this perception is at least partly due to a parallax effect: things that are closer seem to move faster.
Natural learning itself has gone through three phases: evolution, the brain, and culture. Each is a product of the previous one, and each learns faster.
humans would be physically different if we had not invented fire or spears.
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.
