MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques. Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations into a standard programming language for planning problems, and refines the solution.MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques. Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations into a standard programming language for planning problems, and refines the solution.[#item_full_content]
MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques. Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations into a standard programming language for planning problems, and refines the solution.MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques. Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations into a standard programming language for planning problems, and refines the solution.Robotics[#item_full_content]
Meta said Tuesday it is acquiring Moltbook, a social network built exclusively for artificial intelligence agents to make posts and interact with each other.Meta said Tuesday it is acquiring Moltbook, a social network built exclusively for artificial intelligence agents to make posts and interact with each other.Business[#item_full_content]
To stay up to date and work forward in their fields, scientists must have at their fingertips and in their minds thousands of published studies. Large language models (LLMs) show promise as a tool for exploring the vast scientific literature, but are they trustworthy when it comes to providing full and scientifically accurate answers to complex questions in specialized fields?To stay up to date and work forward in their fields, scientists must have at their fingertips and in their minds thousands of published studies. Large language models (LLMs) show promise as a tool for exploring the vast scientific literature, but are they trustworthy when it comes to providing full and scientifically accurate answers to complex questions in specialized fields?[#item_full_content]
To stay up to date and work forward in their fields, scientists must have at their fingertips and in their minds thousands of published studies. Large language models (LLMs) show promise as a tool for exploring the vast scientific literature, but are they trustworthy when it comes to providing full and scientifically accurate answers to complex questions in specialized fields?To stay up to date and work forward in their fields, scientists must have at their fingertips and in their minds thousands of published studies. Large language models (LLMs) show promise as a tool for exploring the vast scientific literature, but are they trustworthy when it comes to providing full and scientifically accurate answers to complex questions in specialized fields?Computer Sciences[#item_full_content]
For years, the guiding assumption of artificial intelligence has been simple: an AI is only as good as the data it has seen. Feed it more, train it longer, and it performs better. Feed it less, and it stumbles. A new study from the USC Viterbi School of Engineering was accepted at the IEEE SoutheastCon 2026, taking place March 12–15. It suggests something far more surprising: with the right method in place, an AI model can dramatically improve its performance in territory it was barely trained on, pushing well past what its training data alone would ever allow.For years, the guiding assumption of artificial intelligence has been simple: an AI is only as good as the data it has seen. Feed it more, train it longer, and it performs better. Feed it less, and it stumbles. A new study from the USC Viterbi School of Engineering was accepted at the IEEE SoutheastCon 2026, taking place March 12–15. It suggests something far more surprising: with the right method in place, an AI model can dramatically improve its performance in territory it was barely trained on, pushing well past what its training data alone would ever allow.Computer Sciences[#item_full_content]
For years, the guiding assumption of artificial intelligence has been simple: an AI is only as good as the data it has seen. Feed it more, train it longer, and it performs better. Feed it less, and it stumbles. A new study from the USC Viterbi School of Engineering was accepted at the IEEE SoutheastCon 2026, taking place March 12–15. It suggests something far more surprising: with the right method in place, an AI model can dramatically improve its performance in territory it was barely trained on, pushing well past what its training data alone would ever allow.For years, the guiding assumption of artificial intelligence has been simple: an AI is only as good as the data it has seen. Feed it more, train it longer, and it performs better. Feed it less, and it stumbles. A new study from the USC Viterbi School of Engineering was accepted at the IEEE SoutheastCon 2026, taking place March 12–15. It suggests something far more surprising: with the right method in place, an AI model can dramatically improve its performance in territory it was barely trained on, pushing well past what its training data alone would ever allow.[#item_full_content]
Researchers at the University at Albany and Rutgers University have developed an early-warning framework that can predict harmful social media interactions before they erupt, paving the way for interventions that can minimize harm and make platforms safer for users. Using publicly available datasets from Reddit and Instagram, two social media platforms with distinct conversation dynamics, researchers trained models to predict from just the first 10 comments whether a thread would escalate into “concentrated waves of toxic interactions”—or what they have dubbed a “negative storm” or “neg storm.”Researchers at the University at Albany and Rutgers University have developed an early-warning framework that can predict harmful social media interactions before they erupt, paving the way for interventions that can minimize harm and make platforms safer for users. Using publicly available datasets from Reddit and Instagram, two social media platforms with distinct conversation dynamics, researchers trained models to predict from just the first 10 comments whether a thread would escalate into “concentrated waves of toxic interactions”—or what they have dubbed a “negative storm” or “neg storm.”[#item_full_content]
The European Parliament on Tuesday called for new EU-wide rules to protect copyrighted content in the bloc from generative AI use.The European Parliament on Tuesday called for new EU-wide rules to protect copyrighted content in the bloc from generative AI use.Business[#item_full_content]
A new type of robotic hand developed at The University of Texas at Austin demonstrates such sensitive touch that it can grasp objects as fragile as a potato chip or a raspberry without crushing them. The technology, called Fragile Object Grasping with Tactile Sensing (FORTE), combines advanced tactile sensing with soft robotics. The breakthrough could improve robot performance when a light touch is needed, such as in health care and manufacturing.A new type of robotic hand developed at The University of Texas at Austin demonstrates such sensitive touch that it can grasp objects as fragile as a potato chip or a raspberry without crushing them. The technology, called Fragile Object Grasping with Tactile Sensing (FORTE), combines advanced tactile sensing with soft robotics. The breakthrough could improve robot performance when a light touch is needed, such as in health care and manufacturing.[#item_full_content]
