280 stories
Recently, I attended the Responsible AI for Social and Ethical Healthcare 2025 “2.0” Symposium organized by, among others, Harvard Medical School. The symposium featured various panels on topics surrounding generative AI, in particular multimodal LLMs, in healthcare.
An LLM that can converse in English & item IDs, and make recommendations w/o retrieval or tools.
The mall 0.2.0 update for R and Python introduces support for external LLM providers like OpenAI and Gemini. This version also features parallel processing for R users, the ability to run NLP on string vectors in Python, and a brand new cheatsheet.
Learn how to fine-tune large language models for specific needs. A practical guide covering techniques, tools, and best practices.
Evaluation metrics, how to build eval datasets, eval methodology, and a review of several benchmarks.
Recsys & search are converging with LLMs via semantic IDs, data augmentation, and unified foundation models.
How to develop real AI applications with LLM agents? We'll be looking at how agent frameworks work.
Why is building real AI applications from LLMs so hard? Discover how agent frameworks solve this problem and explore the future of modern AI development.
Discover LLM Orchestration, which transforms LLMs from simple command-takers into smart systems that solve real-world problems.
Learning to automate simple agentic workflows with Amazon Q CLI, Anthropic MCP, and tmux.
Applying the scientific method, building via eval-driven development, and monitoring AI output.
Chip Huyen and I share what we've learned, best practices, and insights at NVIDIA GTC 2025.
Model architectures, data generation, training paradigms, and unified frameworks inspired by LLMs.
Facts about the new chatbot Grok 3 Only 17 months after the launch of the first version of the chatbot Grok, the new chatbot Grok 3 is ready. Grok 3 was presented at a live launch webinar on 18 February 2025. Named after a Robert Heinlein novel “Stranger in a Strange land”, Grok means […]
DeepSeek proposes novel LLM architectures (still based on transformers) and training strategies to create powerful open source LLMs with limited training costs.
Why language models need cultural and linguistic grounding in India.
Exploring how an AI-powered reading experience could look like.
We are proud to introduce the {mall} package. With {mall}, you can use a local LLM to run NLP operations across a data frame. (sentiment, summarization, translation, etc). {mall} has been simultaneously released to CRAN and PyPi (as an extension to Polars).
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