They power our phones, cars, dishwashers--and the A.I. systems that prop up the economy. "We don't have a rating scale for complexity, but rockets are maybe a five," an investor said. His partner added, "Semiconductors are an eleven." There are a handful of truly monumental collective achievements in our history: Machu Picchu, the Great Wall of China, the Pyramids of Giza, and perhaps, if we're being generous, the Roman aqueducts. Modern global civilization has made one contribution to this eternal pantheon. It is not the moon landing. It is the semiconductor supply chain. A microchip is a bauble of metal and refined sand. It doesn't appear to have epic proportions. Upon close examination, however, the epic proportions are there in miniature. Imagine a perfect replica of a sprawling metropolis, a Tokyo or a São Paulo, engraved at atomic dimensions on a tile the size of a fingernail. The urban details would be faithfully rendered to the last golf ball or egg--each reduced to ...

How far can we push computing? Nowadays big advances are made by scaling up--think more chips packed into ever-bigger data centers. But as we near the ceiling of what we can build, there's still plenty of room below, at the fundamental physical limits of computing. Richard Feynman speaks at CERN in Geneva, Switzerland, in 1965. On the evening of December 29, 1959, at an American Physical Society meeting at Caltech, the physicist Richard Feynman gave a lecture called "There's Plenty of Room at the Bottom," on how we might manipulate and control things on an extremely small scale. There is no physical law stopping us from writing the entire on the head of a pin, he argued, or from storing every book ever written in something the size of a dust speck--or, ultimately, from arranging matter atom by atom. Biology already did versions of this (a living cell keeps its instructions in a wisp of DNA), so all of this was plainly permitted by the laws of physics. We simply had not built the tools yet.

A woman facing a felony murder charge in a Georgia mother's death after an illegal silicone injection was caught when she tried to renew a stolen identity driver's license.

Andreessen Horowitz investor Olivia Moore created an AI-generated TikTok influencer for Bama Rush to test how viewers react to artificial intelligence content.

Rep. Riley Moore, a former welder, argues AI automation could be a force-multiplier for manufacturing and labor jobs if harnessed correctly to complement workers.

Pat Gelsinger wants to pave the way to ever more powerful artificial intelligence using tiny beams of light. After Pat Gelsinger departed Intel as CEO in late 2024, he held 100 meetings in 100 days. The idea, he told WIRED recently, was to whittle down his options until he figured out what to do next. The following March, Gelsinger announced that he had taken a position as general partner at Playground Capital, a venture capital firm specializing in deep tech, taking bets on fledgling technologies built on new science. Gelsinger took the role with the aim of helping a new generation of semiconductor startups reawaken Moore's law . Decades ago, Intel cofounder Gordon Moore predicted that the number of transistors on a chip would double roughly every two years, with a corresponding leap in performance.

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Adapting large-scale foundation models in multi-task scenarios often suffers from task conflict and oblivion. To mitigate such issues, we propose a novel "model MoEization" strategy that leads to a conflict-and oblivion-resistant multi-task adaptation method. Given a weight matrix of a pre-trained model, our method applies SVD to it and introduces a learnable router to adjust its singular values based on tasks and samples. Accordingly, the weight matrix becomes a Mixture of Orthogonal Rank-one Experts (MoORE), in which each expert corresponds to the outer product of a left singular vector and the corresponding right one. We can improve the model capacity by imposing a learnable orthogonal transform on the right singular vectors. Unlike low-rank adaptation (LoRA) and its MoE-driven variants, MoORE guarantees the experts' orthogonality and maintains the column space of the original weight matrix. These two properties make the adapted model resistant to the conflicts among the new tasks and the oblivion of its original tasks, respectively. Experiments on various datasets demonstrate that MoORE outperforms existing multi-task adaptation methods consistently, showing its superiority in terms of conflict-and oblivion-resistance. The code is available at https://github.

Plus: Anthropic has called for a global slowdown in AI development. The Meta hack shows there's more to AI security than Mythos On Monday, reports emerged that attackers had used Meta's AI customer support agent to steal Instagram accounts. Their approach was simple: they asked the agent to link the accounts to email addresses they controlled, and it complied. Since Anthropic announced that its Mythos model was too good at hacking for a general release, cybersecurity concerns have focused on the risk of superpowered AI systems overwhelming computer infrastructure. But the Instagram hack shows that far simpler exploits can still cause damage. As companies offload more work to AI, these comparatively unsophisticated attacks are becoming harder to ignore.

The Chinese company is adapting to the demise of Moore's Law, which guides chip production. It could complicate US chip dominance. Tingbo He, president of Huawei's chip-design subsidiary HiSilicon, says her company's engineers have developed a novel way to optimize semiconductors--and she believes it will close the performance gap between Chinese and Western chips over the next few years. Huawei's method, in short, focuses on speeding up computations across chips, circuits, and entire computing systems, rather than squeezing ever-more components onto a single piece of silicon. "We found a new path," He said at the IEEE International Symposium on Circuits and Systems in Shanghai last weekend.