Topic: symbolic reasoning

Diffusion and energy-based models can support reasoning by searching during inference and combining separate learned components. This talk develops that compositional perspective and examines how smaller models can jointly solve more complex questions. Empirical examples cover symbolic reasoning, decisions and planning.

In this talk I will present an account of how an agent designed or evolved to be intelligent may come to enjoy subjective experiences. First, the agent is stipulated to be capable of (meta)representing subjective ‘qualitative’ sensory information, in the sense that it can easily assess how exactly similar a sensory signal is to all other possible sensory signals. This information is subjective in the sense that it concerns how the different stimuli can be distinguished by the agent itself, rather than how physically similar they are. For this to happen, sensory coding needs to satisfy sparsity