Max Bennett, "A Brief History of Intelligence: Evolution, Ai, and the Five Breakthroughs That Made Our Brains" (Mariner Books, 2023)
Apr 1, 2024
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Max Bennett, an AI entrepreneur and neuroscientist, discusses the evolution of nervous systems and the quest for intelligent systems. The podcast explores how AI mimics neural networks, the influence of human brains on AI architecture, and the reciprocal relationship between AI and neurobiology. Five breakthroughs framework compares evolution to technological innovation, highlighting the emergence of new skills based on brain capabilities. The discussion spans from navigation strategies in bilateral animals to the neocortex's intricate workings and the evolution of mentalizing in early primates.
Evolution of intelligence from worms to humans, AI's attempt to mimic neural networks.
Comparison of breakthroughs in brain capabilities to technological innovations in AI development.
Studying fish intelligence for insights into early vertebrate cognitive abilities and reinforcement learning challenges.
Deep dives
Emergence of Artificial Intelligence Systems in Comparison to Human Brain Capabilities
The discussion in the podcast highlights the origins of the author's interest in neuroscience and artificial intelligence (AI) systems. Max Bennett, an AI entrepreneur, delves into the gap between the abilities of AI systems and the human brain. He reflects on the limitations of functional decomposition in understanding brain function and presents an alternative approach that looks at the evolutionary history of the brain.
Evolutionary Breakthroughs Model and the Connection to Technological Innovations
The podcast explores the author's five breakthroughs model in understanding the evolution of intelligence. By tracing milestones in brain development, such as the emergence of the neocortex, the model identifies underlying algorithms that gave rise to key intellectual capacities. The link between breakthroughs and technological advancements sheds light on shared principles in artificial and biological intelligence.
Insights from Fish Intelligence Research and Vertebrate Learning Mechanisms
The discussion shifts to insights from studying fish intelligence as a window into early vertebrate capabilities. Contrary to common assumptions, fish exhibit complex cognitive abilities like maze navigation, object recognition, and spatial awareness. By analyzing fish learning behaviors, researchers uncover fundamental principles of reinforcement learning and pattern recognition crucial for vertebrate cognitive development.
The Importance of Curiosity in Reinforcement Learning Systems
Sparse rewards pose a challenge to standard reinforcement learning systems due to the exploration-exploitation dilemma. To tackle this issue, researchers have made reinforcement learning systems inherently curious. These systems explore new actions intelligently and expand their knowledge without repeating actions, aiding in discovering sparse rewards.
Evolution of Mentalizing in Primates and Its Impact on Cognitive Abilities
The ability to mentalize, or think about thinking, emerged in early primates, leading to theory of mind and imitation learning. This cognitive advancement enabled primates to anticipate future needs, enhance observational learning, and understand others' intentions. By studying these capabilities in primates, researchers gain insights into improving AI models while being cautious about replicating negative human behaviors like status-seeking and territoriality.
A Brief History of Intelligence: Evolution, Ai, and the Five Breakthroughs That Made Our Brains(Mariner Books, 2023) tells two fascinating stories. One is the evolution of nervous systems. It started 600 million years ago, when the first brains evolved in tiny worms. The other one is humans' quest to create more and more intelligent systems. This story begins in 1951 with the first reinforcement learning algorithm trying to mimic neural networks.
Max Bennett is an AI entrepreneur and neuroscience researcher. His work combines insights from evolutionary neuroscience, comparative psychology, and AI. As each chapter describes how a skill evolved, it also explains whether(!) and how an AI system has managed to implement something similar. A recurring theme is how human brains and neural circuits have influenced AI architecture. The other side of this bi-directional connection is also intriguing. AI has often served as a litmus test, giving a clue how a not well understood neurobiological phenomenon might work, how plausible a hypothesis is.
The organzining principle of this book is a framework of five breakthroughs, which compares evolution to technological innovation. Like a new technology enables several innovative products, a new brain capability enables several new skills. For example, mammals show several new intelligent behaviors compared to their ancestors: vicarious trial and error, episodic memory, and planning. The foundation of all these novelties, is probably the same capability: simulation.
The five breakthroughs are:
steering in bilaterians
learning from trial and error in vertebrates
simulating in mammals
mentalizing in primates
speaking in humans
This framework guides the readers through a time travel of 600 million years. We learn about the environment in which these capabilities evolved: Who were the first mammals and why did planning benefit them? We see what contemporary animals can and can't do: Fish aren't as dumb as folklore suggests. And we take a look at AI's baffling achievements and limitations: Why can AI write decent essays but not load a dishwasher?