This podcast explores the challenges and opportunities of using AI to solve real-world problems. Four key challenges are highlighted: insufficient high-quality data, an overwhelming number of potential solutions, unclear objectives, and the difficulty of defining "good" solutions in dynamic systems. The authors of the article propose strategies to overcome these hurdles, including generating synthetic data, using automated evaluators to verify solutions, defining clear measurable objectives, and employing reinforcement learning with human feedback. They advocate for focusing on "root node problems"—those with broad societal impact—and fostering interdisciplinary collaboration to maximize AI's potential. The podcast uses examples from Google DeepMind's projects, such as AlphaFold and Ithaca, to illustrate these concepts.