If you rely on AI to recommend what to watch, read, or buy, new research indicates that some systems may be basing these results from memory rather than skill: instead of learning to make useful suggestions, the models often recall items from the datasets used to evaluate them, leading to overestimated performance and recommendations that […]<br /> The post Large Language Models Are Memorizing the Datasets Meant to Test Them appeared first on Unite.AI. [...]
Picture this scenario: An Anthropic Skill scanner runs a full analysis of a Skill pulled from ClawHub or skills.sh. Its markdown instructions are clean, and no prompt injection is detected. No shell c [...]
The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-tim [...]
San Francisco-based AI lab Arcee made waves last year for being one of the only U.S. companies to train large language models (LLMs) from scratch and release them under open or partially open source l [...]
I came into this review thinking of Private Internet Access (PIA) as one of the better VPNs. It's in the Kape Technologies portfolio, along with the top-tier ExpressVPN and the generally reliable [...]
The next big trend in AI providers appears to be "studio" environments on the web that allow users to spin up agents and AI applications within minutes. Case in point, today the well-funded [...]
IBM today announced the release of Granite 4.0, the newest generation of its homemade family of open source large language models (LLMs) designed to balance high performance with lower memory and cost [...]
Meta has just released a new multilingual automatic speech recognition (ASR) system supporting 1,600+ languages — dwarfing OpenAI’s open source Whisper model, which supports just 99. Is architectu [...]