I'm Peter Singer AI," the avatar says. I am almost expecting it to continue, like a reincarnated Clippy: "It looks like you're trying to solve a problem. The problem I am trying to solve is why Peter Singer, the man who has been called the world's most influential living philosopher, has created a chatbot. And also, whether it is any good. Me: Why do you exist?

"It reflects the broader competitiveness of China's frontier AI ecosystem," says Scott Singer, a visiting scholar in the Technology and International Affairs Program at the Carnegie Endowment for International Peace. That ecosystem includes DeepSeek's R1 and Tencent's Hunyuan model, which Anthropic co-founder Jack Clark has said is by some measures "world-class." That said, assessments of Alibaba's latest model are preliminary, both due to the inherent challenge of measuring model capabilities, and because so far, the model has only been assessed by Alibaba itself. "The information environment is not very rich right now," says Singer. Since the release of DeepSeek's R1 model in January sent waves through the global stock market, China's tech ecosystem has been in the spotlight--particularly as the U.S. increasingly sees itself as racing against China to create artificial general intelligence (AGI)--highly advanced AI systems capable of performing most cognitive work, from graphic design to machine-learning research.

Australian philosopher Peter Singer's book Animal Liberation, published in 1975, exposed the realities of life for animals in factory farms and testing laboratories and provided a powerful moral basis for rethinking our relationship to them. Now, nearly 50 years on, Singer, 76, has a revised version titled Animal Liberation Now. It comes on the heels of an updated edition of his popular Ethics in the Real World, a collection of short essays dissecting important current events, first published in 2016. Singer, a utilitarian, is a professor of bioethics at Princeton University. In addition to his work on animal ethics, he is also regarded as the philosophical originator of a philanthropic social movement known as effective altruism, which argues for weighing up causes to achieve the most good.

A radio transmission between several Russian soldiers in Ukraine in early March, captured from an unencrypted channel, reveals panicked and confused comrades retreating after coming under artillery fire. "Vostok, I am Sneg 02. On the highway we have to turn left, fuck," one of the soldiers says in Russian using code names meaning "East" and "Snow 02." No need to move further. Later, a third soldier tries to make contact with another codenamed "South 95": "Yug 95, do you have contact with a senior? The third Russian soldier continues, becoming increasingly agitated: "Get on the radio.

In the surreal past few months, P.W. Singer has watched the concept for his new science fiction thriller come to life. Singer is an author, an expert on 21st-century warfare at New America and, lately more than ever, a consultant helping companies in tech and other industries grapple with the repercussions of global unrest. His soon-to-be-released novel about social upheaval, automation and artificial intelligence, "Burn-in," is named after the practice of pushing a new technology to the breaking point. But Singer didn't expect the future of technology to arrive as fast as it has amid the fallout from COVID-19, forcing tech companies, governments and people everywhere to adapt on the fly. Get what matters in tech, in your inbox every morning. "Has Pandora ever been put back in a box?" Singer asked during a recent phone interview from his home near Washington, D.C. "Roles and applications that would have previously seen a more gradual transition over the course of years have been pushed forward in a matter of weeks."

A new algorithm could dramatically slash the time it can take computers to recommend movies or route taxis. The new algorithm developed by Harvard University researchers solves optimization problems exponentially faster than previous algorithms by cutting the number of steps required. Surprisingly, this approach works "without sacrificing the quality of the resulting solution," says study senior author Yaron Singer at Harvard University. Optimization problems seek to find the best answer from all possible solutions, such as mapping the fastest route from point A to point B. Many algorithms designed to solve optimization problems have not changed since they were first described in the 1970s. Previous optimization algorithms generally worked in a step-by-step process, with the number of steps proportional to the amount of the data analyzed.

The local search algorithm WSat is one of the most successful algorithms for solving the satisfiability (SAT) problem. It is notably effective at solving hard Random 3-SAT instances near the so-called `satisfiability threshold', but still shows a peak in search cost near the threshold and large variations in cost over different instances. We make a number of significant contributions to the analysis of WSat on high-cost random instances, using the recently-introduced concept of the backbone of a SAT instance. The backbone is the set of literals which are entailed by an instance. We find that the number of solutions predicts the cost well for small-backbone instances but is much less relevant for the large-backbone instances which appear near the threshold and dominate in the overconstrained region. We show a very strong correlation between search cost and the Hamming distance to the nearest solution early in WSat's search. This pattern leads us to introduce a measure of the backbone fragility of an instance, which indicates how persistent the backbone is as clauses are removed. We propose that high-cost random instances for local search are those with very large backbones which are also backbone-fragile. We suggest that the decay in cost beyond the satisfiability threshold is due to increasing backbone robustness (the opposite of backbone fragility). Our hypothesis makes three correct predictions. First, that the backbone robustness of an instance is negatively correlated with the local search cost when other factors are controlled for. Second, that backbone-minimal instances (which are 3-SAT instances altered so as to be more backbone-fragile) are unusually hard for WSat. Third, that the clauses most often unsatisfied during search are those whose deletion has the most effect on the backbone. In understanding the pathologies of local search methods, we hope to contribute to the development of new and better techniques.