(Roughly) Daily

Posts Tagged ‘AI

“In the attempt to make scientific discoveries, every problem is an opportunity and the more difficult the problem, the greater will be the importance of its solution”*…

(Roughly) Daily is headed into its traditional Holiday hibernation; regular service will begin again very early in the New Year.

It seems appropriate (especially given the travails of this past year) to end the year on a positive and optimistic note, with a post celebrating an extraordinary accomplishment– Science magazine‘s (thus, the AAAS‘) “Breakthrough of the Year” for 2021…

In his 1972 Nobel Prize acceptance speech, American biochemist Christian Anfinsen laid out a vision: One day it would be possible, he said, to predict the 3D structure of any protein merely from its sequence of amino acid building blocks. With hundreds of thousands of proteins in the human body alone, such an advance would have vast applications, offering insights into basic biology and revealing promising new drug targets. Now, after nearly 50 years, researchers have shown that artificial intelligence (AI)-driven software can churn out accurate protein structures by the thousands—an advance that realizes Anfinsen’s dream and is Science’s 2021 Breakthrough of the Year.

AI-powered predictions show proteins finding their shapes: the full story: “Protein structures for all.”

And read Nature‘s profile of the scientist behind the breakthrough: “John Jumper: Protein predictor.”

* E. O. Wilson

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As we celebrate science, we might send well-connected birthday greetings to Robert Elliot Kahn; he was born on this date in 1938. An electrical engineer and computer scientist, he and his co-creator, Vint Cerf, first proposed the Transmission Control Protocol (TCP) and the Internet Protocol (IP), the fundamental communication protocols at the heart of the Internet. Later, he and Vint, along with fellow computer scientists Lawrence Roberts, Paul Baran, and Leonard Kleinrock, built the ARPANET, the first network to successfully link computers around the country.

Kahn has won the Turing Award, the National Medal of Technology, and the Presidential Medal Of Freedom, among many, many other awards and honors.

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“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

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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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“Must it be? It must be.”*…

A long lost work, found… sort of…

When Ludwig van Beethoven died in 1827, he was three years removed from the completion of his Ninth Symphony, a work heralded by many as his magnum opus. He had started work on his 10th Symphony but, due to deteriorating health, wasn’t able to make much headway: All he left behind were some musical sketches.

Ever since then, Beethoven fans and musicologists have puzzled and lamented over what could have been. His notes teased at some magnificent reward, albeit one that seemed forever out of reach.

Now, thanks to the work of a team of music historians, musicologists, composers and computer scientists, Beethoven’s vision will come to life…

A full recording of Beethoven’s 10th Symphony is set to be released on Oct. 9, 2021, the same day as the world premiere performance scheduled to take place in Bonn, Germany – the culmination of a two-year-plus effort…

How a team of musicologists and computer scientists completed Beethoven’s unfinished 10th Symphony,” replete with a sample of the “finished” work.

* Ludwig van Beethoven

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As we size up simulacra, we might recall that it was on this date in 1964 that Teressa Bellissimo, at the Anchor Bar in Buffalo, New York, created Buffalo Hot Wings as a snack for her son and several of his college friends.  Her “invention”– an unbreaded chicken wing section (flat or drumette), generally deep-fried then coated or dipped in a sauce consisting of a vinegar-based cayenne pepper hot sauce and melted butter, and served with with celery and carrot sticks and with blue cheese dressing or ranch dressing for dipping– has become a barroom and fast food staple… and has inspired a plethora of “Buffalo” dishes (other fried foods with dipping sauces).

220px-Buffalo_-_Wings_at_Airport_Anchor_Bar

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“Some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower”*…

Humor is said to be the quintessential humor capacity, last thing that AI could– will?– conquer…

New Yorker cartoons are inextricably woven into the fabric of American visual culture. With an instantly recognizable formula — usually, a black-and-white drawing of an imagined scenario followed by a quippy caption in sleek Caslon Pro Italic — the daily gags are delightful satires of our shared human experience, riffing on everything from cats and produce shopping to climate change and the COVID-19 pandemic. The New Yorker‘s famous Cartoon Caption Contest, which asks readers to submit their wittiest one-liners, gets an average 5,732 entries each week, and the magazine receives thousands of drawings every month from hopeful artists.

What if a computer tried its hand at the iconic comics?

Playing on their ubiquity and familiarity, comics artist Ilan Manouach and AI engineer Ioannis [or Yiannis] Siglidis developed the Neural Yorker, an artificial intelligence (AI) engine that posts computer-generated cartoons on Twitter. The project consists of image-and-caption combinations produced by a generative adversarial network (GAN), a deep-learning-based model. The network is trained using a database of punchlines and images of cartoons found online and then “learns” to create new gags in the New Yorker‘s iconic style, with hilarious (and sometimes unsettling) results…

Comics artist Ilan Manouach (@IlanManouach) and AI engineer Yiannis Siglidis created The Neural Yorker: “Computer-Generated New Yorker Cartoons Are Delightfully Weird.”

For comparison’s sake, see “142 Of The Funniest New Yorker Cartoons Ever.”

Alan Kay

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As we go for the guffaw, we might recall that it was on this date in 1922 that the first chapter in Walt Disney’s career as an animator came to a close when he released the 7th and next-to-last “Laugh-O-Gram” cartoon adaption of a fairy tale, “Jack the Giant Killer.”

Disney’s first animated films began in 1920 as after-work projects when Disney was a commercial artist for an advertising company in Kansas City. He made these cartoons by himself and with the help of a few friends.

He started by persuading Frank Newman, Kansas City’s leading exhibitor, to include short snippets of animation in the series of weekly newsreels Newman produced for his chain of three theaters. Tactfully called “Newman Laugh-O-grams,” Disney’s footage was meant to mix advertising with topical humor…

The Laugh-O-grams were a hit, leading to commissions for animated intermission fillers and coming attractions slides for Newman’s theaters. Spurred by his success, the 19-year-old Disney decided to try something more ambitious: animated fairy tales. Influenced by New York animator Paul Terry’s spoofs of Aesop’s Fables, which had premiered in June 1920, Disney decided not only to parody fairy-tale classics but also to modernize them by having them playing off recent events. With the help of high school student Rudy Ising, who later co-founded the Warner Brothers and MGM cartoon studios, and other local would-be cartoonists, Disney [made 7 animated shorts, of which “Jack, the Giant Killer” was the penultimate].

Walt Disney’s Laugh-O-grams

“Foresight begins when we accept that we are now creating a civilization of risk”*…

There have been a handful folks– Vernor Vinge, Don Michael, Sherry Turkle, to name a few– who were, decades ago, exceptionally foresightful about the technologically-meditated present in which we live. Philip Agre belongs in their number…

In 1994 — before most Americans had an email address or Internet access or even a personal computer — Philip Agre foresaw that computers would one day facilitate the mass collection of data on everything in society.

That process would change and simplify human behavior, wrote the then-UCLA humanities professor. And because that data would be collected not by a single, powerful “big brother” government but by lots of entities for lots of different purposes, he predicted that people would willingly part with massive amounts of information about their most personal fears and desires.

“Genuinely worrisome developments can seem ‘not so bad’ simply for lacking the overt horrors of Orwell’s dystopia,” wrote Agre, who has a doctorate in computer science from the Massachusetts Institute of Technology, in an academic paper.

Nearly 30 years later, Agre’s paper seems eerily prescient, a startling vision of a future that has come to pass in the form of a data industrial complex that knows no borders and few laws. Data collected by disparate ad networks and mobile apps for myriad purposes is being used to sway elections or, in at least one case, to out a gay priest. But Agre didn’t stop there. He foresaw the authoritarian misuse of facial recognition technology, he predicted our inability to resist well-crafted disinformation and he foretold that artificial intelligence would be put to dark uses if not subjected to moral and philosophical inquiry.

Then, no one listened. Now, many of Agre’s former colleagues and friends say they’ve been thinking about him more in recent years, and rereading his work, as pitfalls of the Internet’s explosive and unchecked growth have come into relief, eroding democracy and helping to facilitate a violent uprising on the steps of the U.S. Capitol in January.

“We’re living in the aftermath of ignoring people like Phil,” said Marc Rotenberg, who edited a book with Agre in 1998 on technology and privacy, and is now founder and executive director for the Center for AI and Digital Policy…

As Reed Albergotti (@ReedAlbergotti) explains, better late than never: “He predicted the dark side of the Internet 30 years ago. Why did no one listen?

Agre’s papers are here.

* Jacques Ellul

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As we consider consequences, we might recall that it was on this date in 1858 that Queen Victoria sent the first official telegraph message across the Atlantic Ocean from London to U. S. President James Buchanan, in Washington D.C.– an initiated a new era in global communications.

Transmission of the message began at 10:50am and wasn’t completed until 4:30am the next day, taking nearly eighteen hours to reach Newfoundland, Canada. Ninety-nine words, containing five hundred nine letters, were transmitted at a rate of about two minutes per letter.

After White House staff had satisfied themselves that it wasn’t a hoax, the President sent a reply of 143 words in a relatively rapid ten hours. Without the cable, a dispatch in one direction alone would have taken rouighly twelve days by the speediest combination of inland telegraph and fast steamer.

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