This episode provides an extensive overview of the potential of Artificial Intelligence (AI) to transform learning, authored by several Google leaders and published in November 2025.

This episode is an overview of how Google deepmind is using Artificial Intelligence (AI) models to better understand and protect the natural world, focusing on three key research areas.

This episode explores key points from "LLMs: The Illusion of Thinking – JSO," which challenges common assumptions about Large Language Models. The piece contends that what appears to be intelligence in these systems is actually advanced pattern recognition rather than true comprehension.

This episode dives deep on a comprehensive report titled "GEN AI FAST-TRACKS INTO THE ENTERPRISE," produced jointly by the Wharton Human-AI Research initiative and the consultancy GBK Collective. This document presents the findings of a three-year, repeated cross-sectional study tracking the adoption, investment, impact, and future expectations of Generative AI within large U.S. enterprises. 

This episode provides an extensive overview of the last Amazon research paper focusing heavily on the development and implementation of AI agents through platforms like AWS Bedrock AgentCore. They detail a wide array of research areas, including machine learning, robotics, quantum technologies, and computer vision, and highlight Amazon's scientific contributions via publications and conference presentations.

This episode dive deep on the impact of visual generative AI (genAI) on advertising effectiveness by comparing human expert-created ads, genAI-modified ads (AI enhances expert designs), and genAI-created ads (AI generates content entirely). The study finds that genAI-created ads consistently outperform the other two categories, yielding up to a 19% increase in click-through rates, while genAI-modified ads show no significant improvement.

This episode present a summary of the detailed academic paper, "Emergent Introspective Awareness in Large Language Models," which investigates the capacity of large language models (LLMs) to observe and report on their own internal states. The research employs a technique called concept injection, where known patterns of neural activity are manipulated and then LLMs, particularly Anthropic's Claude models, are tested on their ability to accurately identify these internal changes. 

This episode introduces and evaluates On-Policy Distillation (OPD) as a highly efficient method for the post-training of large language models (LLMs). The authors categorize LLM training into three phases—pre-training, mid-training, and post-training—and distinguish between on-policy training (sampling from the student model) and off-policy training (imitating external sources).

This episode is about introduce Chronos-2, a new time series foundation model developed by Amazon that expands beyond the limitations of previous models by supporting multivariate and covariate-informed forecasting in a zero-shot manner. The core innovation enabling this capability is the group attention mechanism, which allows the model to share information across related time series and external factors, significantly improving prediction accuracy in complex scenarios.

This episode is about C2S-Scale, a new family of large language models (LLMs) built upon Google's Gemma framework and designed for next-generation single-cell analysis. This platform translates high-dimensional single-cell RNA sequencing data into textual "cell sentences," enabling LLMs to process and synthesize vast amounts of transcriptomic and biological text data.