In its early days, the AI initiative known as Project Maven had its fair share of skeptics at the Pentagon. Today, many of them are true believers. The rise of AI warfare speaks to the biggest moral and practical question there is: Who--or what--gets to decide to take a human life? And who bears that cost? In 2018, more than 3,000 Google workers protested the company's involvement in "the business of war" after finding out the company was part of Project Maven, then a nascent Pentagon effort to use computer vision to rifle through copious video footage taken in America's overseas drone wars. They feared Project Maven's AI could one day be used for lethal targeting. In my yearslong effort to uncover the full story of Project Maven for my book,, I learned that is exactly what happened, and that the undertaking was just as controversial inside the Pentagon. Today, the tool known as Maven Smart System is being used in US operations against Iran . How the US military's top brass moved from skepticism about the use of AI in war to true believers has a lot to do with a Marine colonel named Drew Cukor. In early September 2024, during the cocktail hour at a private retreat for tech investors and defense leaders, Vice Admiral Frank "Trey" Whitworth found his way to Drew Cukor. Now Project Maven's founding leader and his skeptical successor were standing face-to-face. Three years earlier, Whitworth had been the Pentagon's top military official for intelligence, advising the chairman of the Joint Chiefs of Staff and running one of the most sensitive and potentially lethal parts of any military process: targeting.

US President Donald Trump has renewed his criticism of the UK government over its response to the Iran conflict, after Prime Minister Sir Keir Starmer said the country would not be drawn into the wider war. Trump told reporters on Monday he was not happy with the UK, adding it should be involved enthusiastically in efforts to reopen the Strait of Hormuz - a vital oil shipping channel . He later told a press conference there were some countries that greatly disappointed me before he singled out the UK, which he said had been considered the Rolls-Royce of allies. Trump's remarks came after Sir Keir said the UK was working with allies on a viable, collective plan to reopen the strait. Sir Keir also said the UK already had minehunters in the region but there was no decision yet on what action would be taken.

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Flexport CEO Ryan Petersen says the conflict is stranding cargo and threatening inflation. After years of chaos in the global supply chain, Ryan Petersen, CEO of the logistics company Flexport, felt 2026 might offer some modicum of order. The pandemic was firmly in the rearview mirror. Red Sea shipping channels--which had been closed due to the Gaza crisis--were finally opening. The Supreme Court struck down many of Donald Trump's tariffs, and some Flexport customers were hoping for refunds.

The US and Iran are trading blows in the Gulf with a simple drone that costs as little as $50,000 to make. But why is a slow, cheap and relatively primitive drone seeing use in 2026 alongside hypersonic missiles and stealth jets? Iran invented the relatively simple Shahed 136 attack drone, but is now fending off US copies launched against it in combat. Why, when the US military has expensive, cutting-edge and hi-tech weapons, is it making flimsy drones powered by a motorbike engine? Iranian company Shahed Aviation Industries originally designed the 136.

Protein inverse folding has attracted increasing attention in recent years.

We present the Manuscripts of Handwritten Arabic (Muharaf) dataset, which is a machine learning dataset consisting of more than 1,600 historic handwritten page images transcribed by experts in archival Arabic.

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.

Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in the treatment of different languages arises at the tokenization stage, well before a model is even invoked. The same text translated into different languages can have drastically different tok-enization lengths, with differences up to 15 times in some cases. These disparities persist even for tokenizers that are intentionally trained for multilingual support.