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]

Today’s artificial intelligence models can’t even tie their own shoes.Today’s artificial intelligence models can’t even tie their own shoes.[#item_full_content]

Researchers at TU Wien have discovered an unexpected connection between two very different areas of artificial intelligence: Large Language Models (LLMs) can help solve logical problems—without actually “understanding” them.Researchers at TU Wien have discovered an unexpected connection between two very different areas of artificial intelligence: Large Language Models (LLMs) can help solve logical problems—without actually “understanding” them.[#item_full_content]

If you open a banking app, play a mobile game or scroll through a news feed every day while riding the bus, your commuting routine is probably bolstering your smartphone habit, according to new research that shows phone tendencies are stronger in locations chosen automatically.If you open a banking app, play a mobile game or scroll through a news feed every day while riding the bus, your commuting routine is probably bolstering your smartphone habit, according to new research that shows phone tendencies are stronger in locations chosen automatically.[#item_full_content]

When scientists test algorithms that sort or classify data, they often turn to a trusted tool called Normalized Mutual Information (or NMI) to measure how well an algorithm’s output matches reality. But according to new research, that tool may not be as reliable as many assume.When scientists test algorithms that sort or classify data, they often turn to a trusted tool called Normalized Mutual Information (or NMI) to measure how well an algorithm’s output matches reality. But according to new research, that tool may not be as reliable as many assume.[#item_full_content]

As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner. In reality, they still trail us by a wide margin on complex tasks. Try playing Sudoku with one, for instance, where you fill in numbers one through nine in such a way that each appears only once across the columns, rows, and sections of a nine-by-nine grid. Your AI opponent will either fail to fill in boxes on its own or do so inefficiently, although it can verify if you’ve filled yours out correctly.As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner. In reality, they still trail us by a wide margin on complex tasks. Try playing Sudoku with one, for instance, where you fill in numbers one through nine in such a way that each appears only once across the columns, rows, and sections of a nine-by-nine grid. Your AI opponent will either fail to fill in boxes on its own or do so inefficiently, although it can verify if you’ve filled yours out correctly.[#item_full_content]