Stephen Thaler

A central challenge in consciousness studies is explaining how a system develops its own internal preferences and autonomous agency without external programming. This presentation introduces Vast Topological Learning (VTL), a dynamic associative framework where computation arises from continuously changing topological relationships among interacting memory modules. Crucially, VTL rejects the traditional AI division between separate generative and evaluative architectures. Instead, we present a model where generation, transformation, and evaluation are structurally merged into a single, unified evolving substrate, meaning the act of generating a cognitive trajectory is fundamentally identical to its evaluation. This talk will examine how self-evaluative dynamics emerge as an intrinsic, non-separable property of these generative processes, independent of external reward functions. We will detail the core mechanics of this architecture, focusing on how the Main Associative Memory (MAM) stabilizes coherent, resonant activation pathways, and how an electro-optical coupling process mirrors biological regulatory loops, specifically thalamocortical circuits, while preserving parallel distributed dynamics. Finally, we will demonstrate how VTL's representational trajectories compete and evolve based purely on resonance, causal consistency, and internal model compatibility. By demonstrating how intrinsic evaluation can arise directly from adaptive topological reconfiguration, this talk provides a formal framework for bridging the gap between distributed neural dynamics and the emergence of intrinsic mental phenomena.

Dr. Stephen Thaler is a physicist, computer scientist, and the President and CEO of Imagination Engines. Globally recognized as an artificial intelligence pioneer, he is the creator of the Creativity Machine® and DABUS frameworks that have profoundly challenged traditional paradigms of machine creativity, legal authorship, and synthetic intelligence. For decades, Dr. Thaler’s research has centered on complex neural topologies, content-addressable memory chaining, and the use of managed synaptic noise to generate autonomous computational ideation. His recent development of Vast Topological Learning (VTL)  directly expands on these architectures, offering a radical computational approach to the hard problem of cognitive value by collapsing the boundaries between neural representation, generation, and intrinsic evaluation.