Steven Strogatz in the NYT:
The question now is whether machine learning can help humans discover similar truths about the things we really care about: the great unsolved problems of science and medicine, such as cancer and consciousness; the riddles of the immune system, the mysteries of the genome.
The early signs are encouraging. Last August, two articles in Nature Medicine explored how machine learning could be applied to medical diagnosis. In one, researchers at DeepMind teamed up with clinicians at Moorfields Eye Hospital in London to develop a deep-learning algorithm that could classify a wide range of retinal pathologies as accurately as human experts can. (Ophthalmology suffers from a severe shortage of experts who can interpret the millions of diagnostic eye scans performed each year; artificially intelligent assistants could help enormously.)
The other article concerned a machine-learning algorithm that decides whether a CT scan of an emergency-room patient shows signs of a stroke, an intracranial hemorrhage or other critical neurological event. For stroke victims, every minute matters; the longer treatment is delayed, the worse the outcome tends to be. (Neurologists have a grim saying: “Time is brain.”) The new algorithm flagged these and other critical events with an accuracy comparable to human experts — but it did so 150 times faster. A faster diagnostician could allow the most urgent cases to be triaged sooner, with review by a human radiologist.
What is frustrating about machine learning, however, is that the algorithms can’t articulate what they’re thinking.
More here.



Richard Marshall interviews Sander Verhaegh in 3:AM Magazine:
Dylan Riler in the New Left Review:
Everything is formed by habit. The crow’s feet that come from squinting or laughter, the crease in a treasured and oft-opened letter, the ruts worn in a path frequently traveled—all are created by repeatedly performing the same action. Even neurons are formed by habit. When continuously exposed to a fixed stimulus, neurons become steadily less sensitive to that stimulus—until they eventually stop responding to it altogether. Anything that’s habitually encountered—the landscape of a daily commute, storefronts passed on a walk to the bus stop, photographs arranged on a mantelpiece—tends toward invisibility. The more we see a thing, the less we see it. Familiarity breeds neglect. Once perception settles into a comfortable pattern, we fall asleep to it. Only when the pattern is broken do we notice there is a pattern at all. The chains of mental habit are too weak to be felt until they are too strong to be broken, to paraphrase Samuel Johnson. Wit, whether visual or verbal, can make the commonplace uncommon again by breaking the habits that render perception routine. We tend to define the quality of wit as merely being deft with a clever comeback. But true wit is richer, cannier, more riddling. And the best of it is often based on a biological phenomenon called supernormal stimuli.
Emerging economies showed some of the largest increases in research output in 2018, according to estimates from the publishing-services company Clarivate Analytics. Egypt and Pakistan topped the list in percentage terms, with rises of 21% and 15.9%, respectively. China’s publications rose by about 15%, and India, Brazil, Mexico and Iran all saw their output grow by more than 8% compared with 2017 (See ‘Countries with biggest rises in research output’). Globally, research output rose by around 5% in 2018, to an estimated 1,620,731 papers listed in a vast science-citation database Web of Science, the highest ever (see ‘Research output rose again in 2018’). This diversification of players in science is a phenomenal success, says Caroline Wagner, a science and technology policy analyst at Ohio State University, and a former adviser to the US government. “In 1980, only 5 countries did 90% of all science — the United States, the United Kingdom, France, Germany and Japan,” she says. “Now there are 20 countries within the top producing group.”
Pablo Calvi in The Believer:
Fara Dabhoiwala in The Guardian:
An interview with Adam Tooze in Jacobin:
Sheri Berman in Dissent:
His book is a lie, a black, infernal creation of a twisted, distorted mind.” It was a Democratic representative from Oklahoma who gave this verdict of The Grapes of Wrath, John Steinbeck’s chronicle of migrants leaving the Dust Bowl for California. Disdained by the political elite and much of the literary set, it was nonetheless the best-selling book of 1939. Today’s parallels with the 1930s give Steinbeck’s work renewed urgency. He writes about farm labourers, shopkeepers and the denizens of village taverns – the kinds of people who, before the enormous political upheaval of 2016, the chattering classes barely remembered. In the age of Trump, mass-migration and the phenomenon of the ‘left behind’, Steinbeck’s work is just as relevant as when he wrote it. But more than that: reading Steinbeck fifty years after his death is the perfect antidote to the culture war that has gripped America.
Jan Mattlin was having what counts as a bad day in Kauniainen. He had driven to the town’s train station and found nowhere to park. Mildly piqued, he called the local newspaper to suggest a small article about the lack of parking spots. To Mr. Mattlin’s surprise, the editor put the story on the front page. “We have very few problems here,” recalled Mr. Mattlin, a partner at a private equity firm. “Maybe they didn’t have any other news available.” Such is the charmed life in Kauniainen (pronounced: COW-nee-AY-nen), a small and wealthy Finnish town that can lay claim to being the happiest place on the planet. Finland was
In reviewing “Intimations of Ghalib”, a new translation of selected ghazals of the Urdu poet Ghalib by M. Shahid Alam, let it be said at the outset that translating classical Urdu ghazal into any language – possibly excepting Persian – is an almost impossible task, and translating Ghalib’s ghazals even more so. The use of symbolism, the aphoristic aspect of each couplet, the frequent play on words, and the packing of multiple meanings into a single verse are all too easy to lose in translation. And no Urdu poet used all these devices more pervasively and subtly than Ghalib, and even learned scholars can disagree strongly on the “correct” meaning of particular verses. As such, Alam set himself an impossible task, and the result is, among other things, a demonstration of this.