The early days of evolutionary biology offer paradigms for the field that are still relevant today, but it can be challenging to develop an intuition about evolutionary biology, which has often been reduced to memorization of legible mathematics.
The process of hill climbing in machine learning can lead to the system becoming very capable and generalizing far outside the training distribution, but it does not necessarily represent or optimize for the loss function it was trained on in the first place.
The consequences of super intelligent AI are unpredictable, and the vast majority of randomly specified utility functions do not have optima with humans in them, making it difficult to ensure that AI remains aligned with human values.
The discussion revolves around the difficulty of predicting the behavior of AI systems that are designed to imitate humans and how to achieve perfect alignment with human values and principles.
This podcast explores the potential threat of super intelligence and how even a deep respect for intelligence may not be enough to prevent its dangers.
The transcript discusses the importance of studying evolutionary biology and game theory in understanding basic paradigms of the field, and touches upon various arguments that biologists made in the early days of the field, including how organisms tend to restrain their own reproduction when faced with scarce resources.