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.

Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management.

For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency

Human feedback is central to the alignment of Large Language Models (LLMs).

Moreover, there remains a considerable gap between the ability to answer exam questions and the application of this knowledge in real-world situations. To bridge the gap and thoroughly assess LLMs in supporting the crop science field, we introduce CROP.