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Found 5 papers, 1 papers with code
Amortizing intractable inference in large language models
Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions.
Discrete, compositional, and symbolic representations through attractor dynamics
Compositionality is an important feature of discrete symbolic systems, such as language and programs, as it enables them to have infinite capacity despite a finite symbol set.
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties.
Sources of Richness and Ineffability for Phenomenally Conscious States
Conscious states (states that there is something it is like to be in) seem both rich or full of detail, and ineffable or hard to fully describe or recall.
Visual representations derived from multiplicative interactions
Biological sensory systems appear to rely on canonical nonlinear computations that can be readily adapted to a broad range of representational objectives.
