Sizzle. Sizzle. That's the sound of your neurons frying over the heat of a thousand GPUs as your generative AI tool of choice cheerfully churns through your workload. As it turns out, offloading all ...

On the surface, it seems obvious that training an LLM with “high quality” data will lead to better performance than feeding it any old “low quality” junk you can find. Now, a group of researchers is ...

People are growing increasingly reliant on large language model (LLM) AI as a source of information, including summarizing available data, as a study aid, to help solve academic and social problems, ...

The education technology sector has long struggled with a specific problem. While online courses make learning accessible, keeping students engaged remains difficult. Completion rates for massive open ...

TOKYO, Sept. 7, 2025 /PRNewswire/ -- Fujitsu announced the development of a new reconstruction technology for generative AI. The new technology, positioned as a core component of the Fujitsu Kozuchi ...

Large language models often lie and cheat. We can’t stop that—but we can make them own up. OpenAI is testing another new way to expose the complicated processes at work inside large language models.

The human brain vastly outperforms artificial intelligence (AI) when it comes to energy efficiency. Large language models (LLMs) require enormous amounts of energy, so understanding how they “think" ...

In long conversations, chatbots generate large “conversation memories” (KV). KVzip selectively retains only the information useful for any future question, autonomously verifying and compressing its ...

In episode 74 of The AI Fix, we meet Amazon’s AI-powered delivery glasses, an AI TV presenter who doesn’t exist, and an Ohio lawmaker who wants to stop people from marrying their chatbot. Also, we ...

A new learning paradigm developed by University College London (UCL) and Huawei Noah’s Ark Lab enables large language model (LLM) agents to dynamically adapt to their environment without fine-tuning ...

Shannon yes. Turing no. AMT, after all, was the master at extracting a small amount of information from a vast sea of apparent gibberish, but he also made the mistake of forecasting natural language ...