DeepMind's core breakthrough was treating AI like a child, not a machine. Instead of programming complex strategies, they taught it to master tasks through simple games like Pong, giving it only one rule ('score go up is good') and allowing it to learn for itself through trial and error.

A Rice PhD showed that training a vision model on a game like Snake, while prompting it to see the game as a math problem (a Cartesian grid), improved its math abilities more than training on math data directly. This highlights how abstract, game-based training can foster more generalizable reasoning.

AlphaGo's architecture mimicked human cognition by pairing a 'fast thinking' neural network for intuition with a 'slow thinking' search algorithm for explicit planning. This hybrid model, combining pattern recognition with calculation, proved more powerful for tackling complex problems than either approach alone.

Languages like Lean allow mathematical proofs to be automatically verified. This provides a perfect, binary reward signal (correct/incorrect) for a reinforcement learning agent. It transforms the abstract art of mathematics into a well-defined environment, much like a game of Go, that an AI can be trained to master.

Google's Project Genie can generate playable game worlds from text prompts, a feat that would have seemed like AGI recently. However, users' expectations immediately shift to the next challenge: demanding AI-generated game mechanics like timers, scoring, and complex interactions.

In domains like coding and math where correctness is automatically verifiable, AI can move beyond imitating humans (RLHF). Using pure reinforcement learning, or "experiential learning," models learn via self-play and can discover novel, superhuman strategies similar to AlphaGo's Move 37.

AlphaGo's infamous 'Move 37' was a play no human expert would have made, initially dismissed as an error. Its eventual success demonstrated that AI can discover novel, superior strategies beyond the existing corpus of human knowledge, fundamentally expanding a field of study rather than just mastering it.

Google DeepMind CEO Demis Hassabis argues that today's large models are insufficient for AGI. He believes progress requires reintroducing algorithmic techniques from systems like AlphaGo, specifically planning and search, to enable more robust reasoning and problem-solving capabilities beyond simple pattern matching.

Harmonic, co-founded by Vlad Tenev to build mathematical superintelligence, has seen its model 'Aristotle' advance faster than anticipated. Initially targeting competition-level math, Aristotle is already assisting with or solving previously unsolved 'Erdős problems,' accelerating the timeline towards tackling foundational scientific challenges.