You don't need to worry about recursive-self-improving AI - yet One of the world's leading artificial intelligence companies has implored the industry to pause development on AI, because the latest models could be reaching a tipping point where they become capable of redesigning themselves, growing ever more powerful and finally escaping our control. At least, that's what the headlines said. In truth, Anthropic's co-founder Jack Clark and the boss of spin-out think-tank The Anthropic Institute, Marina Favaro, have published a long blog post bigging up the capabilities of their Claude model, shortly before the company floats on the stock exchange in an initial public offering (IPO) for a rumoured $1 trillion. Let's, for a moment, ignore the vast financial elephant in the room and look at the technological claims. An AI that becomes capable of designing a more powerful version of itself, which is in turn able to pull off the same feat, is an obvious gamechanger, but it is also not a new idea.

Despite AI's dizzying improvements in mathematical ability, its successes show just how integral human mathematicians are to the scientific process In 1915, Albert Einstein stood before the Prussian Academy of Science and revealed the now-famous equations of his general theory of relativity. Einstein and relativity are synonymous today with genius, but these revelations were initially met with indifference, in part because the maths was too radical for his peers to fully digest. Today, tech firms would have us believe we are on the brink of "superintelligent" artificial intelligence capable of outperforming experts in most domains, producing scientific breakthroughs on a par with Einstein. As Anthropic CEO Dario Amodei put it, we will see " a country of geniuses in a datacenter ". Claims like these are often provided with little evidence, and identifying genius or elevated intelligence is a murky endeavour.

Some of the optical components used in Atom Computing's quantum computer The race to build the first truly useful quantum computer just got more exciting. A quantum computer made from extremely cold atoms has now passed some of the most important milestones towards usefulness, joining a small group of equally able and promising machines. Though there is wide agreement that sufficiently powerful quantum computers would transform our ability to discover new materials and drugs, and break the encryption that underpins the internet, there are many competing ideas about how best to build them. Industry mainstays such as Google and IBM have spent a decade building quantum computers from tiny superconducting circuits, and this approach is currently the front-runner. But an alternate approach that uses electrically neutral ultracold atoms has recently been gaining traction.

Alan Turing proposed a test for machine intelligence: could a computer convince a human it was human? Recently, a friend told me over coffee about some disheartening feedback she had received. "They said it was good," she said, "but that it read like it was written by AI." Knowing her, I understood immediately what had happened. Her credibility was being questioned not because her work was poor, but because it was too good - too clear, too fluent, too polished. The rapid acceleration of artificial intelligence tools is changing how we think about good writing.

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.