Gary Marcus on the False Dichotomy of Nature vs. Nurture and Biomimicry for AI

The discussion fundamentally challenges the common 'nature versus nurture' dichotomy, asserting that it is a false and misleading framework. Instead, the conversation emphasizes that innate knowledge and learned experiences are inextricably linked and work synergistically. Gary Marcus argues that learning cannot occur without some foundational innate structures, and conversely, innate abilities do not preclude further learning. This perspective critiques the prevalent mindset in fields like machine learning, where acknowledging innate components is sometimes seen as 'cheating,' highlighting the absurdity of such a separation.

The podcast delves into the nature of innate knowledge, exploring how genes build these foundational structures. Marcus references his book, "The Birth of the Mind," to explain the biological underpinnings of innate abilities. Examples are drawn from both the animal kingdom, such as a baby ibex instinctively climbing a mountain hours after birth, and human development, where children are born with inherent notions of space, time, other agents, places, mental algebra, and causation. These innate frameworks are presented not as complete knowledge, but as essential scaffolding for subsequent learning and reasoning, particularly in understanding complex relationships like causality.

Evolution plays a significant role in shaping these innate capacities. While acknowledged as 'terribly inefficient' in its initial stages, evolution is described as a cumulative process that, once it develops a 'good idea' (like the vertebrate or primate brain plan), reuses and adapts these 'libraries' or 'subroutines' across species. This reuse of genetic components, such as those for building multiple digits, allows for accelerated development over time. The implication for AI is that starting with minimal or 'empty' libraries in evolutionary computation hinders progress, suggesting that richer, biologically inspired innate structures could significantly speed up AI development.

Finally, the conversation pivots to the practical implications for Artificial Intelligence. Marcus advocates for a biomimicry approach, urging AI engineers to look more closely at biology and the cognitive sciences (psychology, neuroscience, linguistics) for inspiration. Rather than attempting to build intelligence from scratch, the recommendation is to borrow from how biological systems have already solved problems, such as how dogs reason about people ('dognition'). This interdisciplinary approach, mimicking biological solutions, is presented as a crucial shortcut to accelerate the development of truly intelligent systems, avoiding the vast timescales evolution required by leveraging its successful designs.

"so many people dichotomize it and they think it's nature versus nurture when it is obviously has to be nature and nurture they have to work together"

"you can't learn the stuff along the way unless you have some I need stuff but just because you have the innate stuff doesn't mean you don't learn anything"

"people think if I work in machine learning the learning side I must not be allowed to work on the innate side what is Cheney cheating exactly people who said that to me and this is just absurd"

"one of my earlier books was actually trying to understand the biology of this the book was called the birth of the mind like how is it the genes even build a neat knowledge"

"children are born with a notion of space time other agents places and also this kind of mental algebra that I was describing before no certain of causation"

"evolution had a significant role to play in that development this whole Cluj right how efficient do you think is evolution oh it's terribly inefficient except that once it gets a good idea it runs with it"

"once you have a library for building something with multiple digits you can use it for a hand but you can also use it for a foot you just kind of reuse the library with slightly different parameters evolution does a lot of that which means that the speed over time picks up"

"if we had richer libraries to begin with if you were evolving from systems that had in a rich innate structure to begin with then things might speed up"

"part of what I'm suggesting is we should look at biology a lot more we should look at the biology of thought and understanding"

"I mean there's a field called biomimicry and people do that for like material science all the time we should be doing the analog of that for AI"