Unhinged I'm About to Go on a Date With a New Woman. I Know Something About Her--and She Doesn't Know I Know. I don't want to scare her away. I'm not sure what the proper etiquette is for acknowledging that you've recognized a mildly famous person when you match with them on a dating app. This is now the third time this has happened to me.

Users I Read the Pope's Encyclical on A.I. I'm Astounded By What He Wrote. It's an urgent warning--and a celebration of humanity and what we can do at our best. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Nitish_Pahwa newsletter.

Just a week after an AI disproved an 80-year-old conjecture and astonished mathematicians, another conjecture that had stood for half a century has fallen, inspired by the same techniques, but this time written entirely by humans. Last week, an unreleased AI model from OpenAI disproved an important conjecture first posed by Hungarian mathematician Paul Erdős, called the unit distance problem. The puzzle, which Erdős considered his "most striking contribution to geometry" and which many mathematicians had failed to unravel, concerns the number of similar-sized connections you can make between dots arranged on a flat surface. Erdős had set an upper ceiling on this number, which many experts had assumed was correct. But the AI model showed that this number could in fact be much larger, using an obscure trick from algebraic number theory to make complex structures with extremely high dimensions, which could then be used to arrange the dots in a very different arrangement than humans had considered.

Mathematicians have never been so sought after by the world's richest people. At universities across the world, academics are seeing their colleagues mysteriously disappear and join private companies. Some of these companies are household names, like OpenAI and Google, but others are newly formed and just months old, hoping to capitalise on a moment in which mathematics is seen as the secret ingredient with which to improve artificial intelligence - which may in turn transform mathematics itself. "Last May, I was honestly kind of grieving for my scientific identity," says Ken Ono, who in 2025 went on leave from a professorship at the University of Virginia to join Axiom Math, a start-up aiming to build a maths-focused AI. Ono had been asked by a different company, called Epoch AI, to help craft a set of hard-to-solve maths problems that would test AI's problem-solving ability .

We study the problem of learning to bid when the bidder's value is dynamic, i.e., when the current value depends on past outcomes. Specifically, we consider a bidder participating in repeated second-price auctions whose value depends on the time elapsed since their last successful bid, with auctions arriving in continuous time and only aggregated feedback revealed at the end of the horizon. Such a bidder must (1) balance the immediate benefit of winning the current auction against its impact on future values and (2) learn unknown environmental parameters. We derive regret bounds for a class of learning methods that combine plug-in estimators with a differential-equation characterization of the optimal policy, and show that a specific confidence bound algorithm learns the optimal policy with a near optimal regret of $\widetilde{O}(\log N)$ for piecewise linear primitives, and $\widetilde{O}(N^{1/3})$ for general, smooth primitives, achieving these regrets without explicit randomization. These theoretical results are supported by numerical experiments.

The New Scientist Book Club read Silvia Park's near-future sci-fi novel Luminous in May, and had lots of good things to say (along with a few complaints) The New Scientist Book Club read Silvia Park's Luminous in May The New Scientist Book Club had quite a change of science-fictional pace in May, moving from the wilds of space in our April read, Kim Stanley Robinson's, to a much closer-to-home future in Silvia Park's . Like another of our reads this year, Sierra Greer's, this imagines a world where robots are integrated into society - and explores how we might deal with this on many different levels: emotionally, spiritually, practically, sexually. Set in a reunified Korea, it's a compelling blend of three storylines: a police procedural, in which detective Jun is out to discover what might have become of a robot girl who has gone missing; a ragtag bunch of kids on an adventure, in which Ruijie and her schoolmates find an abandoned robot boy in a scrapyard; and a tale of a dysfunctional family. Jun and his younger sister Morgan grew up with a third sibling, a robot who disappeared when they were young, fracturing their family. Author Silvia Park: 'No one is your enemy, not even death' Silvia Park, author of the May read for the New Scientist Book Club, 'Luminous' on emotional artificial intelligence, our inevitable love for robots and coping with grief.

Inspired by Shannon Vallor's book The AI Mirror, this compelling piece looks at how we are being affected by our deepening interactions with tech Traditional ballet with tutus and pointe shoes is my preferred night at the theatre, but I enjoyed a contemporary piece recently at London's Sadler's Wells East. The piece, Mirror, by the Alexander Whitley Dance Company, will also be at the city's Royal Opera House on 4 June. It is inspired by the book by Shannon Vallor, a professor in the ethics of data and artificial intelligence, in which she argues for and against the use of AI. Vallor wants us to find a middle ground between passively resigning ourselves to AI as a replacement for our agency, and seeing it as an existential threat that must be defeated. As a science journalist, I like the balance of Vallor's book, but, for me, this didn't translate to the dance piece.

The late Ian Watson's sci-fi The Embedding is intriguing - but dated Watson's death last month prompted sci-fi columnist Emily H. Wilson to read his acclaimed 1973 debut and find out what she'd been missing. The acclaimed British science-fiction writer Ian Watson, author of more than two dozen novels, died this April. His fame may have faded over the decades, but his debut novel The Embedding was greeted with acclaim when it was published in 1973. The Spectator declared it "the most spectacular thing in science fiction since the outstanding Solaris by Stanisław Lem". Watson's later work, both sci-fi and fantasy, included novels relating to Warhammer 40,000 games and a stint developing the script of A.I. Artificial Intelligence with Stanley Kubrick.

Plus: The Pope has called for governments to regulate AI. Despite the growing hysteria over AI's threat to white-collar jobs, there's still scant evidence that the technology has had a large-scale impact on the labor market. Analysis of US labor data shows that unemployment in occupations most exposed to AI is actually lower than in less-exposed jobs. There are also no signs that large numbers of workers are shifting from AI-threatened professions into supposedly safer manual-labor jobs. It's true that things aren't great in the job market--but the question is why. Here's what the data really says about AI and jobs .

The individual pieces create a kind of illusion. When a horse trots, is there a moment when its four feet are in the air simultaneously? In the 1870s, Leland Stanford, the railroad magnate and benefactor of the university that bears his name, funded an effort to find out. The answer shocked many equestrian experts and artists: The horse's feet leave the ground together, but not when outstretched as commonly depicted in paintings and carousels; the feet do so when they reach inward, toward the horse's belly. Surprisingly, this discovery about a horse's gait sheds light on a much more modern debate--whether A.I. is on a path to consciousness.