Meta has introduced KernelLLM, an 8-billion-parameter language model fine-tuned from Llama 3.1 Instruct, aimed at automating the translation of PyTorch modules into efficient Triton GPU kernels. This initiative seeks to lower the barriers to GPU programming by simplifying kernel development…

Google has officially rolled out the NotebookLM mobile app, extending its AI-powered research assistant to Android devices. The app aims to bring personalized learning and content synthesis directly to users’ pockets by introducing new features that combine mobility, context-awareness, and…

While RAG enables responses without extensive model retraining, current evaluation frameworks focus on accuracy and relevance for answerable questions, neglecting the crucial ability to reject unsuitable or unanswerable requests. This creates high risks in real-world applications where inappropriate responses can…

The ability to search high-dimensional vector representations has become a core requirement for modern data systems. These vector representations, generated by deep learning models, encapsulate data’s semantic and contextual meanings. This enables systems to retrieve results not based on exact…

Chain-of-thought (CoT) prompting has become a popular method for improving and interpreting the reasoning processes of large language models (LLMs). The idea is simple: if a model explains its answer step-by-step, then those steps should give us some insight into…

As autonomous AI agents move from theory into implementation, their impact on the financial services sector is becoming tangible. A recent whitepaper from IBM Consulting, titled “Agentic AI in Financial Services: Opportunities, Risks, and Responsible Implementation”, outlines how these AI…