AI & ML Research 3 Days

Last updated: July 23, 2026, 11:31 AM ET

AI Infrastructure & Deployment

OpenAI is expanding its enterprise offerings with the introduction of OpenAI Presence, a platform designed for deploying trusted voice and chat agents. This follows a commitment to responsible AI infrastructure development, including Project Camellia in Effingham County, Georgia, which promises community investment, job creation, and access to Codex. For smaller businesses, OpenAI has launched the Chat GPT for Small Businesses program, aiming to help entrepreneurs build AI skills and automate workflows. On a larger scale, NTT DATA Group has leveraged Chat GPT Enterprise and Codex to significantly reduce incident analysis time to 30 minutes for its 9,000 employees, demonstrating improved efficiency and secure AI adoption.

LLM Development & Optimization

Building and optimizing Large Language Model (LLM) systems remains a key focus. One team discovered that adding more AI agents to their system actually, uncovering bottlenecks in tiny CPU tasks while scaling hundreds of LLM agents. For those looking to build their own inference runtimes, a guide details the journey of packing weights, managing barriers, and capturing CUDA graphs on an H100. Context engineering is also proving crucial for RAG (Retrieval Augmented Generation) systems, with four specific "bricks of context engineering" highlighted as effective in stopping RAG hallucinations by ensuring faithful answers from the correct context. For those interested in iterative RAG generation, a technique for iterating top-k candidates one at a time has been proposed. Furthermore, practical guides are emerging, such as how to build an LLM agent capable of writing and running code using the OpenAI Agents SDK and Docker, and how to manage long-running coding agents for extended productivity, exemplified by Claude code agents running for over 24 hours.

Machine Learning Practices & Research

Effective machine learning practices are essential for successful projects. A guide offers a fix for messy ML experiments, providing a hands-on approach to tracking experiments, logging models, and ensuring reproducibility with ML Flow. For those working with robot AI models, a reproducible 100-step LoRA fine-tuning run for Open VLA on Colab has been documented, including dataset checks, setup, and training metrics. In broader ML research, a quantum computer that learns from its errors is being developed, pushing the boundaries of machine intelligence. Lessons learned after 8.5 years in ML emphasize the importance of patience, optimism, discipline, project execution, and team collaboration.

AI in Scientific Discovery & Medicine

AI is significantly accelerating scientific discovery and the development of new medicines. Google is committing $40