Evolution is randomized reproduction. That’s all.

Bernard,

This NY Times article is terminally polluted with anthropomorphic, wind-god-like descriptions of birds developing brilliant strategies to adapt to climate change.

But its real content can be completely captured in two simple observations:

1. Because sexual reproduction randomizes offspring, some progeny will happen to nest earlier than usual, some other progeny will happen to prefer higher altitudes, yet other progeny will happen to behave in some other different way, and so forth.
2. Birds that happened to nest later than usual would have tended to lose eggs to overheating and not reproduced effectively. Birds that happened to prefer lower altitudes and warmer temperatures also would have tended not to reproduce effectively. And so forth. Those lineages tend to fall away, get subtracted.

Life on our planet is sun-driven, randomly-varying replicating processes thrown at a usually-slowly-randomly-varying planetary environment.

The basic mechanism of evolution is far simpler than we stuck-in-the-adaptation-and-selection-metaphor humans tend to make it.

Now, here are the same two observations, this time in the context of evolutionary software development with purpose-driven artificial selection:

“Evolutionary computing uses a different approach [from machine-learning neural nets]. It starts with code generated entirely at random. And not just one version of it, but lots of versions, sometimes hundreds of thousands of randomly assembled pieces of code.

“Each of these codes is tested to see whether it achieves the required goal. And of course, all the code is awful because it is randomly generated.

“But just by chance, some pieces of code are a little better than others. These pieces are then reproduced in a new generation of code, which includes more copies of the better codes.

“However, the next generation cannot be an identical copy of the first. Instead, it must change in some way. These changes can involve switching two terms in the code—a kind of point mutation. Or they can involve two codes that are cut in half and the halves exchanged—like sexual recombination.”

Evolution is conceptually simple. Its essence is randomization during reproduction. Period. And specifically not anthropomorphic adaptation and/or natural selection.

You might ask, where then is natural selection? Well, it’s here, always here, but not as a force or an agent. To exist is to persist through time within an external environment. That environment comprises the selective agent. But it exerts no force and does not itself act. So “agent” is the wrong noun, and “select” is the wrong verb. The environment, whatever happens to be in it, is the context within which a living being persists (or not) and reproduces (or not). The environment as a whole is mute, uncaring, passive. For the standpoint of any one living being, the environment might be either thoroughly benign or instantly deadly, either frictionless or terminal.

Wayne

AlphaGo Zero Masters Chess in a Few Hours

Bernard,

Having mastered the game of Go over the course of months, Google’s AlphaZero machine-learning AI was supplied the rules of chess and no other information whatsoever about the game. It learned to be a superhuman chess master after 4 hours of playing games against another instance of itself. Later that same day it also mastered Shogi, the Japanese version of chess, which is more difficult than Western chess. More here.

Wayne

 

Go-ing: Gone

Bernard,

In March 2016 Google’s AlphaGo program defeated one of the top Go players in the world, a breakthrough for so-called artificial intelligence. AlphaGo learned the game starting from records of thousands of Go games played by masters around the world – mostly in Japan, Korea and China – for the past few hundred years.

The latest version of AlphaGo, named AlphaGo Zero, started learning last year with only the rules of Go and no input whatsoever from humanity’s history of the game. Zero learned by playing millions of Go games against another instance of itself, remembering what worked well and what did not. Then it played one hundred games against last year’s AlphaGo. Score: Zero 100 wins, AlphaGo none.

From an article in the MIT Technology Review: “The most striking thing is we don’t need any human data anymore … By not using human data or human expertise, we’ve actually removed the constraints of human knowledge…” [italics added].

Game over, for humans.

Wayne

So-Called Artificial Intelligence: Google Translate Awakens

Bernard,

This weekend’s New York Times has a fine article on AI, The Great A.I. Awakening. I commented briefly on the article on the Times’s site but I want to say a lot more.

The article vividly documents Google Translate’s recent revolution in how it works. Until now, auto-translation engines have modeled languages explicitly via rules, dictionaries, and the like. The new Translate, and its Chinese competitor on Baidu, instead enable a multi-layer neural net – a simulated brain, basically – to learn language translation by being fed thousands or millions of existing examples of translations. Researchers fed Google Translate the complete English and French versions of the Canadian Parliament’s proceedings, for instance, presumably along with many translated classic books, newspapers, and so forth. The new engines learn like human toddlers do, by unconsciously copying behaviors they observe, over and over again, until they evolve to proficiency. And like humans, the new engines will continue learning their entire “lives” by observing and copying new examples with new words and new phrases in all languages. But note: the new engines will not be able to go out in the world themselves to find worthy new examples, not for a very long time yet. They’ll need human care and feeding for the foreseeable future.

From near the end of the article:

A neural network built to translate could work through millions of pages of documents of legal discovery in the tiniest fraction of the time it would take the most expensively credentialed lawyer. The kinds of jobs taken by automatons will no longer be just repetitive tasks that were once — unfairly, it ought to be emphasized — associated with the supposed lower intelligence of the uneducated classes. We’re not only talking about three and a half million truck drivers who may soon lack careers. We’re talking about inventory managers, economists, financial advisers, real estate agents.

All true. But all decades out in the future, maybe several or many decades. Why? I see it like this. A toddler computer learns from a team of humans whose only job today is to feed and raise this child quickly to do one thing well. The toddler computer has no eyes, ears, nose, mouth, hands, legs or feet. The toddler computer processes fed-in data 24×7 and learns its one thing quickly in its tiny simulated brain, much faster than a human child would. But a human child processes many orders of magnitude of far, far richer data per time period than the computer child can: visual, auditory, tactile, olfactory, the physics of standing and walking and making sounds, the complexities of language, and so forth, all integrated and organized within its real brain. See for example this discussion.

The human child’s brain is the culmination of millions of generations of evolving, increasingly more powerful prototypes equipped with extraordinarily capable sensors of several kinds. The human brain perceives and processes the world around it continuously at a huge data rate. The human body moves freely in space. With respect to attaining human-like intelligence and self awareness, then, the computer toddler has a truly enormous gap yet to cross. The crossing cannot possibly be quick, meaning in just a few years or a decade. It’s been only a few years since the total computing power on the planet exceeded just one human brain’s computing power.

Not to diminish or underplay the Google Translate achievements in any way. They are stunning. But I view them as like Watt’s invention of rotary steam motion in the late 1700s: an enormous enabler of a revolution, but still just the very beginning. And I’m not one bit worried by the article’s conclusion that “once machines can learn from human speech, even the comfortable job of the programmer is threatened.” No, not for a very long time yet to come.

As the article says, “The goal posts for ‘artificial intelligence’ are thus constantly receding.” Each step seems major, and Google Translate’s awakening is indeed major, but it’s still tiny in the big picture of true intelligence and self awareness.

Wayne