At Google DeepMind, we've been applying AI across almost every branch of science, achieving breakthroughs in areas from mathematics to life sciences, including developing AlphaProof for solving Math Olympiad problems and AlphaFold for predicting molecular structures.

Growing up, I was fascinated by solving math problems and understanding how machines work, which laid the groundwork for my interest in AI and its practical applications.

My interest in computer science was influenced by the realization that AI could solve complex problems, leading me to shift from studying physics to focusing on AI and technology.

The breakthroughs in reinforcement learning at DeepMind, particularly in games like Capture the Flag and AlphaStar, have shown that we can achieve human-level performance in complex tasks.

The incredible work being done by DeepMind will continue to drive exploration in scientific and mathematical questions, even as AI helps us find answers.

Self-play mechanisms in AI, where systems play against themselves, have led to significant advancements in reinforcement learning, exemplified by systems like AlphaGo and AlphaZero.

After two decades in AI, I've witnessed the transition from AI winters to the rise of deep learning, which has opened up new possibilities in various applications.

The implications of AI advancements are hard to predict, especially regarding how they will transform our lives and the gaming industry in the next decade. AI could transform gaming by creating characters with complete backstories that respond to players based on their decisions, leading to endless bespoke content tailored to individual experiences.

DeepMind's work on reinforcement learning, particularly with AlphaGo and AlphaZero, has shown that algorithmic advancements can lead to substantial improvements in AI performance, often requiring far less training than traditional methods.