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 from tiny worms to human brains and the quest to create more intelligent systems. The podcast explores the correlation between animal capabilities and AI, breakthroughs in associative learning, and the interplay of fast and slow thinking in decision-making. It also delves into the structural unity of the neocortex and the evolution of mentalizing in primates for complex social interactions.
Understanding the evolution of nervous systems from tiny worms to human-like intelligence is fascinating and complex.
The synergy between AI and neuroscience in understanding dopamine signals unlocks abilities for complex sequence learning and adaptation.
Curiosity as a trait evolved through reinforcement learning is essential for agents to tackle exploration-exploitation dilemmas and enhance problem-solving capabilities.
Deep dives
Evolution of Brain Functionality in Early Vertebrates
The podcast explores the evolution of brain functionality in early vertebrates, particularly focusing on the unique capabilities and breakthroughs observed in fish. It discusses how studying fish intelligence provides insights into the abilities of early vertebrates. Fish demonstrate remarkable cognitive skills, such as learning complex sequences, recognizing objects, and representing time and space, which shed light on the cognitive capabilities of early vertebrates.
Temporal Difference Learning and Reinforcement Systems
Temporal difference learning and reinforcement systems are highlighted as crucial mechanisms in early vertebrate brains. The discussion delves into how these systems enable learning through trial and error, addressing the temporal credit assignment problem. By explaining the synergy between AI and neuroscience in understanding dopamine signals, the podcast emphasizes how these systems unlock abilities for complex sequence learning and adaptation.
Algorithms Underlying Behavior in Early Bilaterians
The podcast delves into the underlying algorithms that drove behavior in early bilaterians, focusing on the concept of taxes navigation. It explains how the simple algorithm of taxes allowed organisms like nematodes to find food and avoid predators effectively, even with limited sensory perception. By illustrating the connection to associative learning and valence coding, the discussion highlights how these fundamental processes laid the groundwork for behavioral adaptation in primitive organisms.
Evolution of Curiosity and Reinforcement Learning
Curiosity has become a valuable trait due to evolutionary changes and reinforcement learning. When faced with sparse rewards, standard reinforcement learning agents struggle due to the exploration-exploitation dilemma. To tackle this, reinforcement learning systems need to be inherently curious, exploring new territories smartly rather than randomly. This curiosity enables agents to shift from intrinsic motivation to extrinsic rewards and enhances problem-solving capabilities.
Evolution of Mentalizing and Primate Brain Capabilities
Primates, through the evolution of mentalizing, gained the ability to think about thinking, leading to theory of mind and imitation learning. Mentalizing also allows primates to anticipate future needs better than non-primate mammals. These cognitive abilities emerged from mentalizing and have implications for building better AI models. However, caution is needed not to replicate certain human-like behaviors, such as status-seeking and territoriality, observed in non-human primate societies.
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?
