DNA Is Multibillion-Year-Old Software
Nature invented software billions of years before we did. “The origin of life is really the origin of software,” says Gregory Chaitin (inventor of mathematical metabiology). Life requires what software does. It is fundamentally algorithmic. And its complexity needs better thinking tools.
Nature invented software billions of years before we did. “The origin of life is really the origin of software,” says Gregory Chaitin. Life requires what software does (it’s foundationally algorithmic).
2. Alan Turing described modern software in 1936, inspiring John Von Neumann to connect software to biology. Before DNA was understood, Von Neumann saw that self-reproducing automata needed software. We now know DNA stores information; it's a biochemical version of Turning’s software tape, but more generally: All that lives must process information. Biology's basic building blocks are processes that make decisions.
3. Casting life as software provides many technomorphic insights (and mis-analogies), but let’s consider just its informational complexity. Do life’s patterns fit the tools of simpler sciences, like physics? How useful are experiments? Algebra? Statistics?
4. The logic of life is more complex than the inanimate sciences need. The deep structure of life’s interactions are algorithmic (loosely algorithms = logic with if-then-else controls). Can physics-friendly algebra capture life’s biochemical computations?
5. Describing its “pernicious influence” on science, Jack Schwartz says, mathematics succeeds in only “the simplest of situations” or when “rare good fortune makes [a] complex situation hinge upon a few dominant simple factors.”
6. Physics has low “causal density” — a great Jim Manzi coinage. Nothing in physics chooses. Or changes how it chooses. A few simple factors dominate, operating on properties that generally combine in simple ways. Its parameters are independent. Its algebra-friendly patterns generalize well (its equations suit stable categories and equilibrium states).
7. Higher-causal-density domains mean harder experiments (many hard-to-control factors that often can’t be varied independently). Fields like medicine can partly counter their complexity by randomized trials, but reliable generalization requires biological “uniformity of response.”
8. Social sciences have even higher causal densities, so “generalizing from even properly randomized experiments” is “hazardous,” Manzi says. “Omitted variable bias” in human systems is “massive." Randomization ≠ representativeness of results is guaranteed.
9. Complexity economist Brian Arthur says science’s pattern-grasping toolbox is becoming “more algorithmic ... and less equation-based.” But the nascent algorithmic era hasn’t had its Newton yet.
10. With studies in high-causal-density fields, always consider how representative data is, and ponder if uniform or stable responses are plausible. Human systems are often highly variable; our behaviors aren’t homogenous; they can change types; they’re often not in equilibrium.
11. Bad examples: Malcolm Gladwell puts entertainment first (again) by asserting that “the easiest way to raise people’s scores” is to make a test less readable (n = 40 study, later debunked). Also succumbing to unwarranted extrapolation, leading data-explainer Ezra Klein said, "Cutting-edge research shows that the more information partisans get, the deeper their disagreements.” That study neither represents all kinds of information, nor is a uniform response likely (in fact, assuming that would be ridiculous). Such rash generalizations = far from spotless record.
Mismatched causal density and thinking tools creates errors. Entire fields are built on assuming such (mismatched) metaphors and methods.
(Hat tip to Bryan Atkins @postgenetic for pointer to Brian Arthur).
Further reading: Microsoft Plans to Have a DNA-Based Computer by 2020
Illustration by Julia Suits, The New Yorker Cartoonist & author of The Extraordinary Catalog of Peculiar Inventions.
What can 3D printing do for medicine? The "sky is the limit," says Northwell Health researcher Dr. Todd Goldstein.
- Medical professionals are currently using 3D printers to create prosthetics and patient-specific organ models that doctors can use to prepare for surgery.
- Eventually, scientists hope to print patient-specific organs that can be transplanted safely into the human body.
- Northwell Health, New York State's largest health care provider, is pioneering 3D printing in medicine in three key ways.
An ordained Lama in a Tibetan Buddhist lineage, Lama Rod grew up a queer, black male within the black Christian church in the American south. Navigating all of these intersecting, evolving identities has led him to a life's work based on compassion for self and others.
- "What I'm interested in is deep, systematic change. What I understand now is that real change doesn't happen until change on the inside begins to happen."
- "Masculinity is not inherently toxic. Patriarchy is toxic. We have to let that energy go so we can stop forcing other people to do emotional labor for us."
We were gaining three IQ points per decade for many, many years. Now, that's going backward. Could this explain some of our choices lately?
There's a new study out of Norway that indicates our—well, technically, their—IQs are shrinking, to the tune of about seven IQ points per generation.
Here's why generalists triumph over specialists in the new era of innovation.
- Since the explosion of the knowledge economy in the 1990s, generalist inventors have been making larger and more important contributions than specialists.
- One theory is that the rise of rapid communication technologies allowed the information created by specialists to be rapidly disseminated, meaning generalists can combine information across disciplines to invent something new.
- Here, David Epstein explains how Nintendo's Game Boy was a case of "lateral thinking with withered technology." He also relays the findings of a fascinating study that found the common factor of success among comic book authors.
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