For decades, handmade narco subs have been some of the cocaine trade's most elusive and productive workhorses, ferrying multi-ton loads of illicit drugs from Colombian estuaries toward markets in North America and, increasingly, the rest of the world. Now off-the-shelf technology--Starlink terminals, plug-and-play nautical autopilots, high-resolution video cameras--may be advancing that cat-and-mouse game into a new phase. Uncrewed subs could move more cocaine over longer distances, and they wouldn't put human smugglers at risk of capture. And law enforcement around the world is just beginning to grapple with what this means for the future. This story is from the next print issue of magazine, which is all about crime. Google DeepMind is calling for the moral behavior of large language models--such as what they do when called on to act as companions, therapists, medical advisors, and so on--to be scrutinized with the same kind of rigor as their ability to code or do math.

End-of-life decisions can be extremely upsetting for surrogates--the people who have to make those calls on behalf of another person. Friends or family members may disagree over what's best for their loved one, which can lead to distressing situations. David Wendler, a bioethicist at the US National Institutes of Health, and his colleagues have been working on an idea for something that could make things easier: an artificial intelligence-based tool that can help surrogates predict what the patients themselves would want in any given situation. Wendler hopes to start building their tool as soon as they secure funding for it, potentially in the coming months. But rolling it out won't be simple.

End-of-life decisions can be extremely upsetting for surrogates, the people who have to make those calls on behalf of another person, says David Wendler, a bioethicist at the US National Institutes of Health. Wendler and his colleagues have been working on an idea for something that could make things easier: an artificial-intelligence-based tool that can help surrogates predict what patients themselves would want in any given situation. The tool hasn't been built yet. But Wendler plans to train it on a person's own medical data, personal messages, and social media posts. He hopes it could not only be more accurate at working out what the patient would want, but also alleviate the stress and emotional burden of difficult decision-making for family members.

Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function that is sparse in the Fourier domain. In this work, we present a new family of algorithms for learning Fourier-sparse set functions. They require at most $nk - k \log_2 k + k$ queries (set function evaluations), under mild conditions on the Fourier coefficients, where $n$ is the size of the ground set and $k$ the number of non-zero Fourier coefficients. In contrast to other work that focused on the orthogonal Walsh-Hadamard transform, our novel algorithms operate with recently introduced non-orthogonal Fourier transforms that offer different notions of Fourier-sparsity. These naturally arise when modeling, e.g., sets of items forming substitutes and complements. We demonstrate effectiveness on several real-world applications.