"Repetition sometimes works in poetry, but rarely in prose. The musical provocateur John Cage once wrote a lecture in which a single page was repeated fourteen times, with the refrain "If anybody is sleep let him go to sleep" (Cage, 1961). Midway through, the artist Jean Reynal stood up and screamed, "John, I dearly love you, but I can't bear another minute.
"We should be far more worried about "genetic enhancement"- efforts to artificially construct "improved humans." Here I side with Fukuyama: Although the technology for improvement is close at hand, it comes with great risks, and some of the greatest risks stem from the complexity of the underlying biology. As we have seen, the basic logic by which genes operate-the regulatory IF conjoined with protein template THEN- is straightforward- which is why genetic enhancement might be possible, in principle. But the combined effects of 30,000 genes far exceed our comprehension; if we know the general principles, we don't know the details, and what we don't know really could hurt us."
In the years to come, some of our best minds will try to dig deeper into that computer program, to figure out its individual lines of code (the IF-THENS that we call genes), the products of those lines (what we call proteins), how all those lines of biological code fit together, and how they make room for nurture.
In the long run, the effects on society will be profound. Take, for example, the advances that our increasing understanding of genes will lead to in medicine. Because, as we have seen, the brain is built like the rest of the body, it is also amenable to many of the same types of treatment. For example, stem cell therapies originally developed for leukemia are being adapted to treat Parkinson's disease and Huntington's disease. Gene therapies developed for cystic fibrosis may someday help treat brain tumors. Both work by harnessing the body's own toolkit for development.
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If there is not preformation, and no blueprint, there is also no getting away from the environment. Genes do not guarantee particular products; rather, they provide particular options: To every gene there is an IF, and with that IF comes an option. In many cases, those options are selected based on cues from the environment, and it is for that reason, more than any other, that the answer to the nature-nurture question is not one or the other, but both.
To mention a colorful example, the nineteenth-century German scientist Karl Vogt once wrote that “thoughts stand in the same relation to the brain as gall does to the liver or urine to the kidneys.” When he expressed this idea in public, a philosopher interjected that the longer one listens to Professor Vogt, the more one tends to believe him. Clearly, more sophisticated ideas and models are in demand.
To the extent that genomes can be thought of as compressed encodings of biological structures, they are spectacularly efficient. All the trillions of cells in the human body-not just the tens of billions in the brain-are guided in one way or another by the information contained in 30,000 or so genes. The best high-quality set of pictures of the body- the National Institutes of Health Visible Human Project, a series of high-resolution digital photos of slices taken from volunteer Joseph Paul Jernigan (deceased)-takes up about 60 gigabytes, enough (if left uncompressed) to fill about 100 CD-ROMs-and still not enough detail to capture individual cells. The genome, in contrast, contains only about 3 billion nucleotides, the equivalent (at two bits per nucleotide) of less than two-thirds of a gigabyte, or a single CD-ROM.
The CD-ROM's worth of information in the genome really wouldn't be enough to paint a bitmapped picture of an embryo, but it is enough to describe a process for building one. An artist who only wants to paint a picture that looks like a kind of tree has much less to remember than an artist who wants to paint a particular Ponderosa Pine from memory; in a similar way, if some alien's genome had to encode every cell in a body, it would need much more information (many more nucleotides) than our genomes do, because ours specify a general way to build a creature rather than an exact picture of every detail of the finished product. Our genomes are lossy because they specify methods rather than pictures, but it is precisely that lossiness that allows them to so efficiently supervise the construction of complex biological structure.
In place of a view of the genome as a static blueprint that operates independently of experience and only up to the moment of birth, we have come to understand the genome as a complex, dynamic set of self-regulating recipes that actively modulate every step of life. Nature is not a dictator hell-bent on erecting the same building regardless of the environment, but a flexible Cub Scout prepared with contingency plans for many occasions.
What propels an embryo from one stage to the next-and makes one species different from another-is not a blueprint but rather an enormous autonomous library of the instructions contained within its genome. Each gene does double duty, specifying both a recipe for a protein and a set of regulatory conditions for when and where it should be built. Taken together suites of these IF-THEN genes give cells the power to act as parts of complicated improvisational orchestras. Like real musicians, what they play depends on both their own artistic impulses and what the other members of the orchestra are playing. As we will see in the next chapter, every bit of this process-from the Cellular Big 4 to the combination of regulatory cues-holds as much for development of the brain as it does for the body.
Cell differentiation can turn neurons into everything from clocks that control circadian rhythms to photoreceptors that convert light into electrical-chemical impulses or decision makers that tally votes and decide courses of action. In the retina (often used as a case study because it can be directly and naturally stimulated), there are at least fifty different kinds of neurons specialized to different tasks, such as looking for motion, recognizing colors, detecting objects in low light, and measuring brightness and contrast. In the brain as a whole, there may be as many as 10,000 different kinds of neurons, each contributing to a different aspect of mental life.
In essence, the bee's azimuth system acts like a sundial run in reverse, and like a sundial, it has to be calibrated. A sundial, which must be oriented with respect to a known compass direction, calculates the time of day based on where the sun is; the navigational centers in the bee's brain calculate where the sun should be based on the time of day. As a consequence, the one thing that bees can't cope with is the discalibration that results from jet lag. In a famous 1960s experiment, Max Renner packed up a hive of bees in Long Island, New York, flew them to Davis, California, and tested their ability to navigate with the sun as a landmark. The jetlagged bees consistently misoriented themselves by 45 degrees, precisely as though they believed it was three hours later. The complex circuitry that allows the bee to use the sun as a guide is built in, but it is not that genes trump the environment (or the other way around). Instead, genes enable creatures to make sensible use of their particular environment. Learning is not the antithesis of innateness but one of its most important products.
But the real story is how narrow Duplex was. For all the fantastic resources of Google (and its parent company, Alphabet), the system that they created was so narrow it could handle just three things: restaurant reservations, hair salon appointments, and the opening hours of a few selected businesses. By the time the demo was publicly released, on Android phones, even the hair salon appointments and the opening hour queries were gone. Some of the world’s best minds in AI, using some of the biggest clusters of computers in the world, had produced a special-purpose gadget for making nothing but restaurant reservations. It doesn’t get narrower than that.
There are, of course, many reasons to think that brains operate mostly in parallel. Individual neurons are too slow to allow brains to operate in strict serial von Neumann fashion, and ample data suggest that in any given laboratory task (and by extension, any real-world situation) many different parts of the brain are engaged simultaneously.