AI & ML Research 8 Hours

Last updated: June 24, 2026, 5:30 PM ET

AI & ML Research

Google Deep Mind has developed a "Three-Phase Factual Recall Circuit" within its Gemma models, demonstrating a structured approach to how these large language models store and retrieve information. This circuit, observed in Gemma-2B and Gemma-12B-IT, reveals that the residual stream plays a significant role in routing and reading out facts, suggesting a more organized internal knowledge representation than previously assumed. Further research from Google AI explores how this "thinking to recall" capability, or reasoning, unlocks parametric knowledge within LLMs, enhancing their ability to access and utilize stored information. Google Deep Mind has also integrated computer use into its latest Gemini 3.5 Flash model, expanding its capabilities for complex tasks that require interaction with external tools and environments.

In data engineering, establishing testable ETL pipelines is presented as a crucial first task for new hires. This involves setting up environments, implementing automated testing protocols, and leveraging AI-assisted development to ensure data integrity and pipeline reliability. The practical application of machine learning in finance is also detailed, with a guide on how to transform logistic regression coefficients into a 0-1000 credit scoring grid. This method includes risk class segmentation and stability checks, offering a systematic way to translate model outputs into actionable credit assessments. Meanwhile, Europe faces significant strain on its power grids due to record-breaking heat waves, forcing some power plants offline and threatening energy stability as demand for cooling surges.