What’s the most promising path to creating Artificial General Intelligence (AGI)? This paper makes the bold claim that a learning agent maximizing its reward in a sufficiently complex environment will necessarily develop intelligence as a by-product, and that Reward Maximization is the best way to move the creation of AGI forward. The paper is a mix of philosophy, engineering, and futurism, and raises many points of discussion.
0:00 - Intro & Outline
4:10 - Reward Maximization
10:10 - The Reward-is-Enough Hypothesis
13:15 - Abilities associated with intelligence
16:40 - My Criticism
26:15 - Reward Maximization through Reinforcement Learning
31:30 - Discussion, Conclusion & My Comments