MIT researchers have developed a new method for designing 3D structures that can be transformed from a flat configuration into their curved, fully formed shape with only a single pull of a string.MIT researchers have developed a new method for designing 3D structures that can be transformed from a flat configuration into their curved, fully formed shape with only a single pull of a string.[#item_full_content]

Whether you’re a scientist brainstorming research ideas or a CEO hoping to automate a task in human resources or finance, you’ll find that artificial intelligence (AI) tools are becoming the assistants you didn’t know you needed. In particular, many professionals are tapping into the talents of semi-autonomous software systems called AI agents, which can call on AI at specific points to solve problems and complete tasks.Whether you’re a scientist brainstorming research ideas or a CEO hoping to automate a task in human resources or finance, you’ll find that artificial intelligence (AI) tools are becoming the assistants you didn’t know you needed. In particular, many professionals are tapping into the talents of semi-autonomous software systems called AI agents, which can call on AI at specific points to solve problems and complete tasks.[#item_full_content]

For people, matching what they see on the ground to a map is second nature. For computers, it has been a major challenge. A Cornell research team has introduced a new method that helps machines make these connections—an advance that could improve robotics, navigation systems, and 3D modeling.For people, matching what they see on the ground to a map is second nature. For computers, it has been a major challenge. A Cornell research team has introduced a new method that helps machines make these connections—an advance that could improve robotics, navigation systems, and 3D modeling.[#item_full_content]

A new approach is making it easier to visualize lifelike 3D environments from everyday photos already shared online, opening new possibilities in industries such as gaming, virtual tourism and cultural preservation.A new approach is making it easier to visualize lifelike 3D environments from everyday photos already shared online, opening new possibilities in industries such as gaming, virtual tourism and cultural preservation.[#item_full_content]

Generative AIs may not be as creative as we assume. Publishing in the journal Patterns, researchers show that when image-generating and image-describing AIs pass the same descriptive scene back and forth, they quickly veer off topic.Generative AIs may not be as creative as we assume. Publishing in the journal Patterns, researchers show that when image-generating and image-describing AIs pass the same descriptive scene back and forth, they quickly veer off topic.[#item_full_content]

Ph.D. candidate Yuchen Lian (LIACS) wants to understand why human languages look the way they do—and find inspiration to improve AI along the way. She defended her thesis on 12 December.Ph.D. candidate Yuchen Lian (LIACS) wants to understand why human languages look the way they do—and find inspiration to improve AI along the way. She defended her thesis on 12 December.[#item_full_content]

Most languages use word position and sentence structure to extract meaning. For example, “The cat sat on the box,” is not the same as “The box was on the cat.” Over a long text, like a financial document or a novel, the syntax of these words likely evolves.Most languages use word position and sentence structure to extract meaning. For example, “The cat sat on the box,” is not the same as “The box was on the cat.” Over a long text, like a financial document or a novel, the syntax of these words likely evolves.[#item_full_content]

No matter how much data they learn, why do artificial intelligence (AI) models often miss the mark on human intent? Conventional comparison learning, designed to help AI understand human preferences, has frequently led to confusion rather than clarity. A KAIST research team has now presented a new learning solution that allows AI to accurately learn human preferences even with limited data by assigning it a “private tutor.”No matter how much data they learn, why do artificial intelligence (AI) models often miss the mark on human intent? Conventional comparison learning, designed to help AI understand human preferences, has frequently led to confusion rather than clarity. A KAIST research team has now presented a new learning solution that allows AI to accurately learn human preferences even with limited data by assigning it a “private tutor.”[#item_full_content]

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