Shelly Fan in Singularity Hub:
Remember the last time you visited the doctor? They likely asked you about your medical history.
For many conditions, this information isn’t just relevant for diagnosis and treatment, it’s also valuable for prevention. Thanks to AI, a range of algorithms can now predict the risk of single medical conditions, such as cardiovascular disease and cancer, based on medical records.
But diseases don’t exist in a vacuum. Some conditions may increase the risk of others. A full picture of a person’s health trajectory would predict risk across a range of diseases. This could not only inform early treatment, but also surface vulnerable groups of people for screening and other preventative measures. And it could identify people at risk for a condition—say, high blood pressure or breast cancer—that don’t necessarily fit the usual criteria. Recently, a team from the German Cancer Research Center and collaborators released an AI “oracle” that predicts a person’s risk of getting over 1,000 common diseases decades in the future. Dubbed Delphi-2M, the AI is a type of large language model, like the algorithms powering popular chatbots.
More here.
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Here’s a very short, oversimplified history of modern economics. In the 1960s and 1970s, a particular way of thinking about economics crystallized in academic departments, and basically took over the top journals. It was very math-heavy, and it modeled the economy as the sum of a bunch of rational human agents buying and selling things in a market. Although the people who invented these methods (Paul Samuelson, Ken Arrow, etc.) were not very libertarian, in the 70s and 80s a bunch of conservative-leaning economists used the models to claim that free markets were great. The models turned out to be pretty useful for saying “free markets are great”, simply because math is hard — it’s a lot easier to mathematically model a simple, well-functioning market than it is to model a complex world where markets are only part of the story, and where markets themselves have lots of pieces that break down and don’t work.
Before moving to the United States at ten, I grew up surrounded by other Iranians. Aunts, uncles, cousins, and family friends who spoke my language and understood the nuances of my life. Then, in the suburbs of America, I suddenly understood isolation and loneliness. I was the new, dark kid from the country who took Americans hostage, the kid whose name, tastes and mannerisms were easy to mock, who didn’t know the rules to American sports or culture. My refuge from alienation came through stories. I came home from school every day and buried myself in fiction because it felt like the real world had no space for me. But the irony is that the fictional worlds I was most obsessed with had no place for me either.
Senator Josh Hawley is
Following the shooting of Turning Point USA founder Charlie Kirk, Vox cofounder and podcast commentator Ezra Klein
A person is a perpetual ongoingness perpetually mistaking itself for a still point. We call this figment personality or identity or self, and yet we are
The
“Little Reunions ought to be burned,” Eileen Chang wrote to her friend and literary executor, Stephen Soong, in 1976, the year she finished what would be her last novel. When it was finally published, in 2009, fourteen years after her death, Little Reunions seemed to carry this curse with it; the book received widespread criticism for its cryptic narrative and for not sounding like Eileen Chang.
In “Super Natural,” award-winning science writer Alex Riley casts his inquisitive, generous gaze upon the extremists. No, not the far right or the far left; these are the far-deep, far-up, and far-flung life-forms that inhabit Earth’s less move-in-ready biomes. From snailfish and wood frogs to painted turtles and tardigrades, these remarkable creatures display a knack for thriving – or at least carrying on – in a niche of their own. Mr. Riley chatted via video with Monitor contributor Erin Douglass about the marvels and possibilities of such lives on the edge. The interview has been edited and condensed.
In 1983, the octogenarian geneticist Barbara McClintock stood at the lectern of the Karolinska Institute in Stockholm. She was famously publicity averse — nearly a hermit — but it’s customary for people to speak when they’re awarded a Nobel Prize, so she delivered a halting account of the experiments that had led to her discovery, in the early 1950s, of how DNA sequences can relocate across the genome. Near the end of the speech, blinking through wire-framed glasses, she changed the subject, asking: “What does a cell know of itself?”