The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

by Pedro Domingos

In the same way that a bank without databases can’t compete with a bank that has them, a company without machine learning can’t keep up with one that uses it. While the first company’s experts write a thousand rules to predict what its customers want, the second company’s algorithms learn billions of rules, a whole set of them for each individual customer. It’s about as fair as spears against machine guns. Machine learning is a cool new technology, but that’s not why businesses embrace it. They embrace it because they have no choice.

In the old days, the NSA used keyword matching, but that’s easy to get around. (Just call the bombing a “wedding” and the bomb the “wedding cake.”) In the twenty-first century, it’s a job for machine learning. Secrecy is the NSA’s trademark, but its director has testified to Congress that mining of phone logs has already halted dozens of terrorism threats.

Mechanical Sympathy

Some may say that seeking a universal learner is the epitome of techno-hubris. But dreaming is not hubris. Maybe the Master Algorithm will take its place among the great chimeras, alongside the philosopher’s stone and the perpetual motion machine. Or perhaps it will be more like finding the longitude at sea, given up as too difficult until a lone genius solved it. More likely, it will be the work of generations, raised stone by stone like a cathedral. The only way to find out is to get up early one day and set out on the journey.

In the 1970s, so-called knowledge-based systems scored some impressive successes, and in the 1980s they spread rapidly, but then they died out. The main reason they did was the infamous knowledge acquisition bottleneck: extracting knowledge from experts and encoding it as rules is just too difficult, labor-intensive, and failure-prone to be viable for most problems.

A multilayer perceptron is a passable model of the cerebellum, the part of the brain responsible for low-level motor control, but the cortex is another story. It’s missing the backward connections needed to propagate errors, for one, and yet it’s where the real learning wizardry resides. In his book On Intelligence, Jeff Hawkins advocated designing algorithms closely based on the organization of the cortex, but so far none of these algorithms can compete with today’s deep networks.

Prudently, he picked a more conservative topic for his dissertation—Boolean circuits with cycles—and in 1959 he earned the world’s first PhD in computer science. His PhD advisor, Arthur Burks, nevertheless encouraged Holland’s interest in evolutionary computation and was instrumental in getting him a faculty job at Michigan and shielding him from senior colleagues who didn’t think that stuff was computer science. Burks himself was so open-minded because he had been a close collaborator of John von Neumann, who had proved the possibility of self-reproducing machines. Indeed, it had fallen to him to complete the work when von Neumann died of cancer in 1957. That von Neumann could prove that such machines are possible was quite remarkable, given the primitive state of genetics and computer science at the time. But his automaton just made exact copies of itself; evolving automata had to wait for Holland.

This kind of modeling never occurs to me initially.

The core of Nick Bostrom’s argument

In machine learning, as elsewhere in computer science, there’s nothing better than getting such a combinatorial explosion to work for you instead of against you. What’s clever about genetic algorithms is that each string implicitly contains an exponential number of building blocks, known as schemas, and so the search is a lot more efficient than it seems. This is because every subset of the string’s bits is a schema, representing some potentially fit combination of properties, and a string has an exponential number of subsets.

Not Paul Graham with "A Plan for Spam"?

These days all kinds of algorithms are used to recommend items to users, but weighted k-nearest-neighbor was the first widely used one, and it’s still hard to beat.

In fact, the most important thing about an equation is all the quantities that don’t appear in it: once we know what the essentials are, figuring out how they depend on each other is often the easier part.

If computers are like idiot savants, learning algorithms can sometimes come across like child prodigies prone to temper tantrums. That’s one reason humans who can wrangle them into submission are so highly paid.

Robopsychologists

The more you teach it, the better it can serve you—or manipulate you. Life is a game between you and the learners that surround you. You can refuse to play, but then you’ll have to live a twentieth-century life in the twenty-first. Or you can play to win. What model of you do you want the computer to have? And what data can you give it that will produce that model? Those two questions should always be in the back of your mind whenever you interact with a learning algorithm—as they are when you interact with other people.

Reputational networks

Social science is entering a golden age, where it finally has data commensurate with the complexity of the phenomena it studies, and the benefits to all of us could be enormous—provided the data is accessible to researchers, policy makers, and citizens. This does not mean letting others peek into your private life; it means letting them see the learned models, which should contain only statistical information. So between you and them there needs to be an honest data broker that guarantees your data won’t be misused, but also that no free riders share the benefits without sharing the data.

So far I haven’t uttered the word privacy. That’s not by accident. Privacy is only one aspect of the larger issue of data sharing, and if we focus on it to the detriment of the whole, as much of the debate to date has, we risk reaching the wrong conclusions. For example, laws that forbid using data for any purpose other than the originally intended one are extremely myopic. (Not a single chapter of Freakonomics could have been written under such a law.)

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.

It’s natural to worry about intelligent machines taking over because the only intelligent entities we know are humans and other animals, and they definitely have a will of their own. But there is no necessary connection between intelligence and autonomous will; or rather, intelligence and will may not inhabit the same body, provided there is a line of control between them. In The Extended Phenotype, Richard Dawkins shows how nature is replete with examples of an animal’s genes controlling more than its own body, from cuckoo eggs to beaver dams. Technology is the extended phenotype of man.