DNA: The Discovery Darwin Never Saw Coming

What keeps me reading extensively, and listening to lectures and audiobooks, is the world’s incredible ability to surprise us at every turn. There is still so much to discover. New insights about the origins of life are but one example. And the implications are truly mind-blowing 

For much of the twentieth century, many people assumed that new discoveries in biology would steadily reinforce Darwin's explanation for the origin of species. Instead, one of the greatest scientific discoveries of the modern era raised an entirely different question. That discovery was DNA.

Stephen Meyer recently summarized this story in a lecture at Cambridge University. His point wasn't merely that Watson and Crick discovered the double-helix structure of DNA in 1953. The larger discovery came a few years later when Francis Crick realized that DNA functions much like a digital code. 

DNA isn't simply a remarkable chemical. It contains instructions and stores information. Bill Gates once compared DNA to a computer program, only far more sophisticated than anything humans have ever written. That's an analogy, of course, but it captures something vital: living cells don't merely contain chemistry; they contain coded information that directs the construction of proteins, the molecular machinery that makes life possible.

This is where Meyer believes the discussion changes. Chemistry can explain many things. It can explain how molecules interact. But where does meaningful information come from? In our everyday experience, codes, software, languages, and written messages always originate with minds. They don't arise simply because chemicals happen to bump into one another.

Meyer argues that this presents a significant challenge for theories that attempt to explain life's origin solely through undirected material processes. Rather than asking only, "How did the chemicals get here?" he asks another question: "Where did the information come from?" 

Darwin & his ilk believe everything is possible with enough time and chance. It takes a lot of faith to believe 10,000 monkeys with 10,000 typewriters could ever produce Shakespeare's Romeo and Juliet, or Cervantes' Don Quixote. This itself would be easier than creating life out of goo, or a universe out of nothing (though the latter is a different discussion.)

Science has always advanced by following the evidence wherever it leads. Ironically, Watson and Crick themselves were not looking for evidence of intelligent design. They were searching for the material basis of life. Yet what they uncovered was something that looked remarkably like a language—an information system residing within every living cell. To me, that's one of the fascinating twists in the history of science.

Darwin gave us a powerful theory that appeared to explain many aspects of biological change. Molecular biology in the twentieth century, however, revealed levels of complexity he could never have imagined. Instead of making the question of life's origin simpler, DNA has made it deeper.

Where there is information, we naturally ask about its source. Where there is a message, we ask about the messenger. DNA may not settle the debate for everyone, but it certainly reminds us that life is far more astonishing than anyone imagined a century ago. 

The most amazing (and convincing) feature of DNA to me (according to Meyer in another talk) is that for a species to be designed the code must precede it. This contradicts the "survival of the fittest" notions that chameleons changed the colors to blend in to an environment as a matter of survival. Other examples get cited in biology textbooks, but how did the code get altered after the fact when it must precede the species' designs beforehand. It doesn't make sense. 

Bottom Line:
The molecular revolution of the twentieth century introduced questions about the origin of biological information that Darwin himself never addressed and that remain the subject of ongoing debate. The appeal of thinkers like Stephen Meyer and David Berlinski is that contemporary scientists would remain committed to honest inquiry, not dogmatism, cherry-picking or confirmation bias. 

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FWIW Dept.
According to the latest research, on average, a human gene contains about 10,000 to 30,000 "letters" (base pairs). Because the cellular machinery reads the code in three-letter "words" (called codons), this translates to roughly 3,330 to 10,000 amino acids per gene. Overall, the entire human genome across all 23 pairs of chromosomes contains about 3 billion base pairs of information.

Related Links

Why DNA Points to a Mind Behind the Universe
Berlinski and The Devil's Delusion

Tech Tuesday: The A.I. Poker King and Watson's Ever Improving Tax Preparation Skills

This past year AI achievements have been stirring imaginations. So far the best chess player and best Go player have been beaten by "smart machines," and now the best poker players have fallen. What makes this story fascinating is that poker is a game that involves psychology, not simply logic. Can a machine really learn to bluff? Cade Metz's Inside Libratus, the Poker AI That Out-Bluffed the Best Humans* tells the story on Wired.com.

You might say we've been living in the "machine age" for quite some time, if you begin with the Eli Whitney's cotton gin. All throughout modern history machines have stirred both awe and insecurity. At the center of many of the fears is that the machines will take our jobs, or worse... replace the human race altogether. Nevertheless, as this story explains, we're fascinated by the strides they have made.

The computer in this instance had been developed by Carnegie Mellon. The high stakes game that 28-year Kim Dong, a world champion poker player, played in Pittsburgh was Texas Hold 'Em.

About halfway through the competition... Kim started to feel like Libratus could see his cards. “I’m not accusing it of cheating,” he said. “It was just that good.” So good, in fact, that it beat Kim and three more of the world’s top human players—a first for artificial intelligence.

To beat top Jeopardy players last year, Watson had to understand syntax and the words used in questions. For Google's DeepMind to win at Go it analyzed millions of players' moves before using this info to develop its skills by playing against itself. Libratus used a still different set of skills here. The article explains:

Libratus didn't need an ace up his sleeve.

Through an algorithm called counterfactual regret minimization, it began by playing at random, and eventually, after several months of training and trillions of hands of poker, it too reached a level where it could not just challenge the best humans but play in ways they couldn’t—playing a much wider range of bets and randomizing these bets, so that rivals have more trouble guessing what cards it holds. “We give the AI a description of the game. We don’t tell it how to play,” says Noam Brown, a CMU grad student who built the system alongside his professor, Tuomas Sandholm. “It develops a strategy completely independently from human play, and it can be very different from the way humans play the game.”

This was only part of the process it used. In addition to learning everything it could about poker before the match, Libratus also learned from the game itself. Perhaps a little like a good poker player who plays enough hands to learn his or her opponents' tells before moving into the big money plays.

What makes poker interesting, though, is the psychological aspect. Sometimes you raise a bet when you have no good cards in your hand. Sometimes you try to make the other player believe your bluffing so they stay in the game when you have four kings. Calculating odds is one thing, but getting inside the other players' heads is another.

What's next for Libratus? Wall Street? International trade negotiations.

Sunday evening a commercial in the Super Bowl showed us what Watson, the IBM A.I. will be up to this year. Watson's going to work on tax loopholes for us.

Meantime, life goes on...

* Read Inside Libratus, the Poker AI That Out-Bluffed the Best Humans here.