🔬 Google's AI Scientist Started as an Attempt to Automate Kaggle — John Platt, Google Fellow

Shows how to turn a research problem into a score an agent can optimize, using LLMs with tree search and UCB, and why choosing the score is the hard part.

• Platt credits ERA with helping crack a climate-modeling problem that had stalled for two years, and explains that contrails trap heat and that small changes in flight altitude can reduce that warming.
• Platt still recommends starting with a simple baseline such as linear regression or an SVM, and cautions that predictive accuracy does not automatically mean scientific understanding.
• The episode uses a half-pixel labeling error that helped win a Kaggle competition to illustrate reward hacking and Goodhart's law, a risk for any agent optimizing a chosen score.
• Google's FireSat effort aims to detect wildfires from space before they grow out of control, one of the climate-resilience applications Platt discusses.

• AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.

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