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.

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. Life requires what software does (it’s foundationally algorithmic).

1. “DNA is multibillion-year-old software,” says Chaitin (inventor of mathematical metabiology). We’re surrounded by software, but couldn’t see it until we had suitable thinking tools.

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.  

Related: olicausal sciences; Newton pattern vs. Darwin pattern; the two kinds of data (history ≠ nomothetic); life = game theoretic = fundamentally algorithmic.

(Hat tip to Bryan Atkins @postgenetic for pointer to Brian Arthur).

Further reading: Microsoft Plans to Have a DNA-Based Computer by 2020

More: Human DNA Could Store All the World's Data

Illustration by Julia Suits, The New Yorker Cartoonist & author of The Extraordinary Catalog of Peculiar Inventions.

This is what aliens would 'hear' if they flew by Earth

A Mercury-bound spacecraft's noisy flyby of our home planet.

Image source: sdecoret on Shutterstock/ESA/Big Think
Surprising Science
  • There is no sound in space, but if there was, this is what it might sound like passing by Earth.
  • A spacecraft bound for Mercury recorded data while swinging around our planet, and that data was converted into sound.
  • Yes, in space no one can hear you scream, but this is still some chill stuff.

First off, let's be clear what we mean by "hear" here. (Here, here!)

Sound, as we know it, requires air. What our ears capture is actually oscillating waves of fluctuating air pressure. Cilia, fibers in our ears, respond to these fluctuations by firing off corresponding clusters of tones at different pitches to our brains. This is what we perceive as sound.

All of which is to say, sound requires air, and space is notoriously void of that. So, in terms of human-perceivable sound, it's silent out there. Nonetheless, there can be cyclical events in space — such as oscillating values in streams of captured data — that can be mapped to pitches, and thus made audible.


Image source: European Space Agency

The European Space Agency's BepiColombo spacecraft took off from Kourou, French Guyana on October 20, 2019, on its way to Mercury. To reduce its speed for the proper trajectory to Mercury, BepiColombo executed a "gravity-assist flyby," slinging itself around the Earth before leaving home. Over the course of its 34-minute flyby, its two data recorders captured five data sets that Italy's National Institute for Astrophysics (INAF) enhanced and converted into sound waves.

Into and out of Earth's shadow

In April, BepiColombo began its closest approach to Earth, ranging from 256,393 kilometers (159,315 miles) to 129,488 kilometers (80,460 miles) away. The audio above starts as BepiColombo begins to sneak into the Earth's shadow facing away from the sun.

The data was captured by BepiColombo's Italian Spring Accelerometer (ISA) instrument. Says Carmelo Magnafico of the ISA team, "When the spacecraft enters the shadow and the force of the Sun disappears, we can hear a slight vibration. The solar panels, previously flexed by the Sun, then find a new balance. Upon exiting the shadow, we can hear the effect again."

In addition to making for some cool sounds, the phenomenon allowed the ISA team to confirm just how sensitive their instrument is. "This is an extraordinary situation," says Carmelo. "Since we started the cruise, we have only been in direct sunshine, so we did not have the possibility to check effectively whether our instrument is measuring the variations of the force of the sunlight."

When the craft arrives at Mercury, the ISA will be tasked with studying the planets gravity.

Magentosphere melody

The second clip is derived from data captured by BepiColombo's MPO-MAG magnetometer, AKA MERMAG, as the craft traveled through Earth's magnetosphere, the area surrounding the planet that's determined by the its magnetic field.

BepiColombo eventually entered the hellish mangentosheath, the region battered by cosmic plasma from the sun before the craft passed into the relatively peaceful magentopause that marks the transition between the magnetosphere and Earth's own magnetic field.

MERMAG will map Mercury's magnetosphere, as well as the magnetic state of the planet's interior. As a secondary objective, it will assess the interaction of the solar wind, Mercury's magnetic field, and the planet, analyzing the dynamics of the magnetosphere and its interaction with Mercury.

Recording session over, BepiColombo is now slipping through space silently with its arrival at Mercury planned for 2025.

Learn the Netflix model of high-performing teams

Erin Meyer explains the keeper test and how it can make or break a team.

  • There are numerous strategies for building and maintaining a high-performing team, but unfortunately they are not plug-and-play. What works for some companies will not necessarily work for others. Erin Meyer, co-author of No Rules Rules: Netflix and the Culture of Reinvention, shares one alternative employed by one of the largest tech and media services companies in the world.
  • Instead of the 'Rank and Yank' method once used by GE, Meyer explains how Netflix managers use the 'keeper test' to determine if employees are crucial pieces of the larger team and are worth fighting to keep.
  • "An individual performance problem is a systemic problem that impacts the entire team," she says. This is a valuable lesson that could determine whether the team fails or whether an organization advances to the next level.
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Photo by Martin Adams on Unsplash
Culture & Religion
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