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

Evolution, part 2

Even if computers today are still not terribly smart, there’s no doubt that their intelligence is rapidly increasing. As early as 1965, I. J. Good, a British statistician and Alan Turing’s sidekick on the World War II Enigma code-breaking project, speculated on a coming intelligence explosion. Good pointed out that if we can design machines that are more intelligent than us, they should in turn be able to design machines that are more intelligent than them, and so on ad infinitum, leaving human intelligence far behind. In a 1993 essay, Vernor Vinge christened this “the Singularity.” The concept has been popularized most of all by Ray Kurzweil, who argues in The Singularity Is Near that not only is the Singularity inevitable, but the point where machine intelligence exceeds human intelligence-let’s call it the Turing point-will arrive within the next few decades.

Clearly, without machine learning-programs that design programs-the Singularity cannot happen. We also need sufficiently powerful hardware, but that’s coming along nicely. We’ll reach the Turing point soon after we invent the Master Algorithm. (I’m willing to bet Kurzweil a bottle of Dom P?rignon that this will happen before we reverse engineer the brain, his method of choice for bringing about human-level AI.) Pace Kurzweil, this will not, however, lead to the Singularity. It will lead to something much more interesting.

The term singularity comes from mathematics, where it denotes a point at which a function becomes infinite. For example, the function 1/x has a singularity when x is 0, because 1 divided by 0 is infinity. In physics, the quintessential example of a singularity is a black hole: a point of infinite density, where a finite amount of matter is crammed into infinitesimal space. The only problem with singularities is that they don’t really exist. (When did you last divide a cake among zero people, and each one got an infinite slice?) In physics, if a theory predicts something is infinite, something’s wrong with the theory. Case in point, general relativity presumably predicts that black holes have infinite density because it ignores quantum effects. Likewise, intelligence cannot continue to increase forever. Kurzweil acknowledges this, but points to a series of exponential curves in technology improvement (processor speed, memory capacity, etc.) and argues that the limits to this growth are so far away that we need not concern ourselves with them.

Kurzweil is overfitting. He correctly faults other people for always extrapolating linearly-seeing straight lines instead of curves-but then falls prey to a more exotic malady: seeing exponentials everywhere. In curves that are flat-nothing happening-he sees exponentials that have not taken off yet. But technology improvement curves are not exponentials; they are S curves, our good friends from Chapter 4. The early part of an S curve is easy to mistake for an exponential, but then they quickly diverge. Most of Kurzweil’s curves are consequences of Moore’s law, which is on its last legs. Kurzweil argues that other technologies will take the place of semiconductors and S curve will pile on S curve, each steeper than the previous one, but this is speculation. 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. Trilobites in the heat of the Cambrian explosion could be forgiven for believing in exponentially accelerating progress, but then there was a big slowdown. A Tyrannosaurus Ray would probably have proposed a law of accelerating body size. Eukaryotes (us) evolve more slowly than prokaryotes (bacteria). Far from accelerating smoothly, evolution proceeds in fits and starts.

To sidestep the problem that infinitely dense points don’t exist, Kurzweil proposes to instead equate the Singularity with a black hole’s event horizon, the region within which gravity is so strong that not even light can escape. Similarly, he says, the Singularity is the point beyond which technological evolution is so fast that humans cannot predict or understand what will happen. If that’s what the Singularity is, then we’re already inside it. We can’t predict in advance what a learner will come up with, and often we can’t even understand it in retrospect. As a matter of fact, we’ve always lived in a world that we only partly understood. The main difference is that our world is now partly created by us, which is surely an improvement. The world beyond the Turing point will not be incomprehensible to us, any more than the Pleistocene was. We’ll focus on what we can understand, as we always have, and call the rest random (or divine).

The trajectory we’re on is not a singularity but a phase transition. Its critical point-the Turing point-will come when machine learning overtakes the natural variety. 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. Machine learning is the logical next stage of this progression. Computer programs are the fastest replicators on Earth: copying them takes only a fraction of a second. But creating them is slow, if it has to be done by humans. Machine learning removes that bottleneck, leaving a final one: the speed at which humans can absorb change. This too will eventually be removed, but not because we’ll decide to hand things off to our “mind children,” as Hans Moravec calls them, and go gently into the good night. Humans are not a dying twig on the tree of life. On the contrary, we’re about to start branching.

In the same way that culture coevolved with larger brains, we will coevolve with our creations. We always have: humans would be physically different if we had not invented fire or spears. We are Homo technicus as much as Homo sapiens. But a model of the cell of the kind I envisaged in the last chapter will allow something entirely new: computers that design cells based on the parameters we give them, in the same way that silicon compilers design microchips based on their functional specifications. The corresponding DNA can then be synthesized and inserted into a “generic” cell, transforming it into the desired one. Craig Venter, the genome pioneer, has already taken the first steps in this direction. At first we will use this power to fight disease: a new pathogen is identified, the cure is immediately found, and your immune system downloads it from the Internet. Health problems becomes an oxymoron. Then DNA design will let people at last have the body they want, ushering in an age of affordable beauty, in William Gibson’s memorable words. 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.

Many people worry that human-directed evolution will permanently split the human race into a class of genetic haves and one of have-nots. This strikes me as a singular failure of imagination. Natural evolution did not result in just two species, one subservient to the other, but in an infinite variety of creatures and intricate ecosystems. Why would artificial evolution, building on it but less constrained, do so?

Like all phase transitions, this one will eventually taper off too. Overcoming a bottleneck does not mean the sky is the limit; it means the next bottleneck is the limit, even if we don’t see it yet. Other transitions will follow, some large, some small, some soon, some not for a long time. But the next thousand years could well be the most amazing in the life of planet Earth.

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