Over the past decade, deep learning has transformed how artificial intelligence (AI) agents perceive and act in digital environments, allowing them to master board games, control simulated robots and reliably tackle various other tasks. Yet most of these systems still depend on enormous amounts of direct experience—millions of trial-and-error interactions—to achieve even modest competence.Over the past decade, deep learning has transformed how artificial intelligence (AI) agents perceive and act in digital environments, allowing them to master board games, control simulated robots and reliably tackle various other tasks. Yet most of these systems still depend on enormous amounts of direct experience—millions of trial-and-error interactions—to achieve even modest competence.Hi Tech & Innovation[#item_full_content]

Understanding how humans and AI or robotic agents can work together effectively requires a shared foundation for experimentation. A University of Michigan-led team developed a new taxonomy to serve as a common language among researchers, then used it to evaluate current testbeds used to study how human-agent teams will perform.Understanding how humans and AI or robotic agents can work together effectively requires a shared foundation for experimentation. A University of Michigan-led team developed a new taxonomy to serve as a common language among researchers, then used it to evaluate current testbeds used to study how human-agent teams will perform.Robotics[#item_full_content]

As people increasingly use artificial intelligence (AI) in various areas of life to either save time or improve performance, the influence of AI systems grows—as does the risk of untrustworthy results in high-stakes decision-making, like those involving medical or financial choices.As people increasingly use artificial intelligence (AI) in various areas of life to either save time or improve performance, the influence of AI systems grows—as does the risk of untrustworthy results in high-stakes decision-making, like those involving medical or financial choices.Security[#item_full_content]

Artificial intelligence company Anthropic has signed a multibillion-dollar deal with Google to acquire more of the computing power needed for the startup’s chatbot, Claude.Artificial intelligence company Anthropic has signed a multibillion-dollar deal with Google to acquire more of the computing power needed for the startup’s chatbot, Claude.Business[#item_full_content]

Artificial intelligence is now part of everyday life. It’s in our phones, schools and homes. For young people, AI shapes how they learn, connect and express themselves. But it also raises real concerns about privacy, fairness and control.Artificial intelligence is now part of everyday life. It’s in our phones, schools and homes. For young people, AI shapes how they learn, connect and express themselves. But it also raises real concerns about privacy, fairness and control.Consumer & Gadgets[#item_full_content]

Disaster has just struck, roads are inaccessible, and people need shelter now. Rather than wait days for a rescue team, a fleet of AI-guided drones takes flight carrying materials and the ability to build shelters, reinforce infrastructure, and construct bridges to reconnect people with safety.Disaster has just struck, roads are inaccessible, and people need shelter now. Rather than wait days for a rescue team, a fleet of AI-guided drones takes flight carrying materials and the ability to build shelters, reinforce infrastructure, and construct bridges to reconnect people with safety.Engineering[#item_full_content]

Researchers at the Technical University of Munich (TUM) and TU Darmstadt have studied how text-to-image generators deal with gender stereotypes in various languages. The results show that the models not only reflect gender biases, but also amplify them. The direction and strength of the distortion depends on the language in question.Researchers at the Technical University of Munich (TUM) and TU Darmstadt have studied how text-to-image generators deal with gender stereotypes in various languages. The results show that the models not only reflect gender biases, but also amplify them. The direction and strength of the distortion depends on the language in question.Machine learning & AI[#item_full_content]

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