When AI reasoning goes wrong: Microsoft Research shows more tokens can mean more problems

Microsoft researchers say they've developed a hyper-efficient AI model that can run on CPUs

Microsoft researchers have developed — and released — a hyper-efficient AI model that can run on CPUs, including Apple's M2.

When AI reasoning goes wrong: Microsoft Research shows more tokens can mean more problems

Not all AI scaling strategies are equal. Longer reasoning chains are not sign of higher intelligence. More compute isn't always the answer.

A new study from Microsoft Research has identified key limitations in the reasoning capabilities of large language models when inference is scaled, and found that more computing power does not always lead to better results.

Research Suggests Powerful AI Models Now Possible Without High-End Hardware

Large language models (LLMs) often require substantial computing resources that are usually met by high-performance hardware. These systems are built to handle vast amounts of data and execute the intricate calculations that power these models.  For most people, the prospect of running advanced AI technology on their everyday devices seems unrealistic. However, a recent collaborative effort by researchers from MIT, the King Abdullah University o…