today's digest covers a mix of research advances and industry news. we look at a new method for treatment rules, a study on llm reviews, and a glossary for common ai terms. on the industry side, groq seeks funding, google demos new models, and xcena raises money for memory chips. plus, practical guides for profiling pytorch and running nlp in the browser.
- deep learning for bivariate survival treatment rules - this matters because it shows how deep learning can optimize treatment decisions when two outcomes matter, like balancing efficacy and side effects.
- llm reviews show limited alignment with human judgment - this matters because it warns that using llms for peer review can be unreliable and open to manipulation by authors.
- ai terms you pretended to know, now explained - this matters because it helps non-experts understand common jargon like llm and rag, making ai discussions more accessible.
- groq seeks $650m after nvidia deal - this matters because it signals growing investment in ai inference hardware, following a major licensing deal with nvidia.
- google demos gemini omni and 3.5 flash capabilities - this matters because it shows google's push into video creation and agentic tasks, expanding what ai models can do.
- xcena raises $135m to put compute inside memory chips - this matters because it aims to cut ai inference costs by reducing data movement, a key bottleneck in current hardware.
- beginner's guide to pytorch profiling - this matters because it helps developers find and fix performance bottlenecks in their pytorch code.
that's all for today. check back tomorrow for more updates on ai research, tools, and industry news.