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“Learning never exhausts the mind”*…

As regular readers know, each year Tom Whitwell shares a list of the more intriguing things he’s learned over the year; happily, 2021 is no exception…

10% of US electricity is generated from old Russian nuclear warheads. [Geoff Brumfiel]

The entire global cosmetic Botox industry is supported by an annual production of just a few milligrams of botulism toxin. Pure toxin would cost ~$100 trillion per kilogram. [Anthony Warner]

Wearing noise cancelling headphones in an open-plan office helps a little bit — reducing cognitive errors by 14% — but actual silence reduces those errors by one third. [Benjamin Müller & co]

Until 1873, Japanese hours varied by season. There were six hours between sunrise and sunset, so a daylight hour in summer was 1/3rd longer than an hour in winter. [Sara J. Schechner]

48 other fascinating finds at: “52 things I learned in 2021,” from @TomWhitwell.

* Leonardo da Vinci


As we live and learn, we might recall that it was on this date in 1545, in response to the Protestant Reformation, that the Council of Trent (Concilium Tridentinum) was convened by the Roman Catholic Church. Its work concluded in 1563; and its results were published in 1564, condemning what the Catholic Church deemed to be the heresies of Protestants.  The embodiment of the Counter-Reformation, The Council of Trent established a firm and permanent distinction between the two practices of faith.

Council of Trent (painting in the Museo del Palazzo del Buonconsiglio, Trento)


“Alchemy. The link between the immemorial magic arts and modern science. Humankind’s first systematic effort to unlock the secrets of matter by reproducible experiment.”*…

Science has entered a new era of alchemy, suggests Robbert Dijkgraaf, Director of the Institute for Advanced Study at Princeton– and, he argues, that’s a good thing…

Is artificial intelligence the new alchemy? That is, are the powerful algorithms that control so much of our lives — from internet searches to social media feeds — the modern equivalent of turning lead into gold? Moreover: Would that be such a bad thing?

According to the prominent AI researcher Ali Rahimi and others, today’s fashionable neural networks and deep learning techniques are based on a collection of tricks, topped with a good dash of optimism, rather than systematic analysis. Modern engineers, the thinking goes, assemble their codes with the same wishful thinking and misunderstanding that the ancient alchemists had when mixing their magic potions.

It’s true that we have little fundamental understanding of the inner workings of self-learning algorithms, or of the limits of their applications. These new forms of AI are very different from traditional computer codes that can be understood line by line. Instead, they operate within a black box, seemingly unknowable to humans and even to the machines themselves.

This discussion within the AI community has consequences for all the sciences. With deep learning impacting so many branches of current research — from drug discovery to the design of smart materials to the analysis of particle collisions — science itself may be at risk of being swallowed by a conceptual black box. It would be hard to have a computer program teach chemistry or physics classes. By deferring so much to machines, are we discarding the scientific method that has proved so successful, and reverting to the dark practices of alchemy?

Not so fast, says Yann LeCun, co-recipient of the 2018 Turing Award for his pioneering work on neural networks. He argues that the current state of AI research is nothing new in the history of science. It is just a necessary adolescent phase that many fields have experienced, characterized by trial and error, confusion, overconfidence and a lack of overall understanding. We have nothing to fear and much to gain from embracing this approach. It’s simply that we’re more familiar with its opposite.

After all, it’s easy to imagine knowledge flowing downstream, from the source of an abstract idea, through the twists and turns of experimentation, to a broad delta of practical applications. This is the famous “usefulness of useless knowledge,” advanced by Abraham Flexner in his seminal 1939 essay (itself a play on the very American concept of “useful knowledge” that emerged during the Enlightenment).

A canonical illustration of this flow is Albert Einstein’s general theory of relativity. It all began with the fundamental idea that the laws of physics should hold for all observers, independent of their movements. He then translated this concept into the mathematical language of curved space-time and applied it to the force of gravity and the evolution of the cosmos. Without Einstein’s theory, the GPS in our smartphones would drift off course by about 7 miles a day.

But maybe this paradigm of the usefulness of useless knowledge is what the Danish physicist Niels Bohr liked to call a “great truth” — a truth whose opposite is also a great truth. Maybe, as AI is demonstrating, knowledge can also flow uphill.

In the broad history of science, as LeCun suggested, we can spot many examples of this effect, which can perhaps be dubbed “the uselessness of useful knowledge.” An overarching and fundamentally important idea can emerge from a long series of step-by-step improvements and playful experimentation — say, from Fröbel to Nobel.

Perhaps the best illustration is the discovery of the laws of thermodynamics, a cornerstone of all branches of science. These elegant equations, describing the conservation of energy and increase of entropy, are laws of nature, obeyed by all physical phenomena. But these universal concepts only became apparent after a long, confusing period of experimentation, starting with the construction of the first steam engines in the 18th century and the gradual improvement of their design. Out of the thick mist of practical considerations, mathematical laws slowly emerged…

One could even argue that science itself has followed this uphill path. Until the birth of the methods and practices of modern research in the 17th century, scientific research consisted mostly of nonsystematic experimentation and theorizing. Long considered academic dead ends, these ancient practices have been reappraised in recent years: Alchemy is now considered to have been a useful and perhaps even necessary precursor to modern chemistry — more proto-science than hocus-pocus.

The appreciation of tinkering as a fruitful path toward grand theories and insights is particularly relevant for current research that combines advanced engineering and basic science in novel ways. Driven by breakthrough technologies, nanophysicists are tinkering away, building the modern equivalents of steam engines on the molecular level, manipulating individual atoms, electrons and photons. Genetic editing tools such as CRISPR allow us to cut and paste the code of life itself. With structures of unimaginable complexity, we are pushing nature into new corners of reality. With so many opportunities to explore new configurations of matter and information, we could enter a golden age of modern-day alchemy, in the best sense of the word.

However, we should never forget the hard-won cautionary lessons of history. Alchemy was not only a proto-science, but also a “hyper-science” that overpromised and underdelivered. Astrological predictions were taken so seriously that life had to adapt to theory, instead of the other way around. Unfortunately, modern society is not free from such magical thinking, putting too much confidence in omnipotent algorithms, without critically questioning their logical or ethical basis.

Science has always followed a natural rhythm of alternating phases of expansion and concentration. Times of unstructured exploration were followed by periods of consolidation, grounding new knowledge in fundamental concepts. We can only hope that the current period of creative tinkering in artificial intelligence, quantum devices and genetic editing, with its cornucopia of useful applications, will eventually lead to a deeper understanding of the world…

Today’s powerful but little-understood artificial intelligence breakthroughs echo past examples of unexpected scientific progress: “The Uselessness of Useful Knowledge,” from @RHDijkgraaf at @the_IAS.

Pair with: “Neuroscience’s Existential Crisis- we’re mapping the brain in amazing detail—but our brain can’t understand the picture” for a less optimistic view.

*  John Ciardi


As we experiment, we might recall that it was on this date in 1993 that the Roman Catholic Church admitted that it had erred in condemning Galileo.  For over 359 years, the Church had excoriated Galileo’s contentions (e.g., that the Earth revolves around the Sun) as anti-scriptural heresy.  In 1633, at age 69, Galileo had been forced by the Roman Inquisition to repent, and spent the last eight years of his life under house arrest.  After 13 years of inquiry, Pope John Paul II’s commission of historic, scientific and theological scholars brought the pontiff a “not guilty” finding for Galileo; the Pope himself met with the Pontifical Academy of Sciences to help correct the record.

Galileo (standing; white collar, dark smock) showing the Doge of Venice (seated) how to use the telescope. From a fresco by Giuseppe Bertini


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