The video explores intelligence as the efficient adaptation across environments, emphasizing the necessity of embodiment, multiscale causality, and biologically inspired principles like self-organization and delegation for true adaptability. It critiques current AI approaches, advocates for hybrid models combining approximation and precise reasoning, and proposes a novel perspective on consciousness as an emergent property of causal self-representation and valence patterns.
The discussion begins with an exploration of the nature and definitions of intelligence, referencing foundational thinkers like Legg and Hutter, and Pei Wang. Intelligence is framed as the efficiency of adaptation or adaptation with limited resources, emphasizing simplicity and fruitfulness in definitions. The conversation highlights the challenges in pinning down intelligence, noting that many definitions become overly complex and lose practical meaning. The guest, Michael Timothy Bennett, draws on these ideas to discuss intelligence as the ability to acquire skills and adapt across a range of environments, influenced by concepts like Solomonoff induction and Occam’s razor, but also points out the limitations of purely compression-based views of intelligence.
A significant portion of the dialogue focuses on the importance of embodiment and the role of abstraction layers in intelligence. Bennett critiques the notion of computational dualism—the idea that software intelligence can be considered independently of its hardware or environment—arguing that intelligence must be understood as situated within and interpreted through physical systems. He advocates for biologically inspired intelligence, emphasizing self-organization, delegation, and causal learning as key properties that artificial systems should emulate to achieve true adaptability and efficiency. This leads to a discussion on the multiscale, bidirectional causality present in biological systems, where control and information flow both top-down and bottom-up across layers of abstraction.
The conversation then delves into the concept of consciousness, with Bennett proposing a novel approach to the hard problem of consciousness. He suggests that consciousness arises necessarily from the causal representation of self and the tapestry of valence—patterns of attraction and repulsion experienced by an organism. He argues against the possibility of philosophical zombies (entities identical to humans but lacking consciousness), positing that consciousness is an inevitable consequence of certain physical and informational configurations. Bennett also discusses the implications of whether consciousness must be realized at a single point in time or can be “smeared” across time, touching on ideas related to liquid brains and distributed cognition.
Regarding the current state and future of artificial general intelligence (AGI), Bennett expresses skepticism about claims that scaling up current models like large language models (LLMs) alone will achieve human-level intelligence. He points out the inefficiencies and limitations of these systems, particularly their sample inefficiency and lack of true understanding or adaptability. Instead, he advocates for hybrid approaches that combine approximation methods (like those used in LLMs) with precise search and reasoning techniques, referencing systems like NARS and Hyperon that integrate diverse modules for more versatile intelligence. He also critiques the overreliance on benchmarks, emphasizing their role as measuring sticks rather than definitive indicators of progress.
Finally, the discussion touches on the nature of life, agency, and the design of adaptive systems. Bennett introduces the “law of the stack,” which states that higher-level adaptability depends on adaptability at lower abstraction layers, drawing parallels between biological systems and artificial ones. He warns against over-constraining systems, which can lead to breakdowns analogous to cancer in biological organisms or brittleness in AI. The conversation concludes with reflections on the interplay between simplicity and complexity in living systems, the importance of decentralized control and delegation, and the potential for future AI systems to embody these principles to achieve robustness, adaptability, and perhaps a form of artificial life.