Chris Frith

Authored papers (2)

• Consciousness in Artificial Intelligence: Insights from the Science of Consciousness2023

  No current AI system is a strong candidate for phenomenal consciousness, yet there are no obvious technical barriers to building one — this is the central finding of Butlin et al. (2023), a systematic assessment of contemporary AI architectures against 14 indicator properties derived from five neuroscientific theories of consciousness. The paper introduces a rubric-based, theory-heavy method: rather than relying on behavioral tests susceptible to gaming by systems like GPT-4 or LaMDA, it operationalizes indicators in computational terms drawn from recurrent processing theory (RPT-1, RPT-2), global workspace theory (GWT-1 through GWT-4), computational higher-order theories including perceptual reality monitoring (HOT-1 through HOT-4), attention schema theory (AST-1), predictive processing (PP-1), and agency/embodiment conditions (AE-1, AE-2). Applied to specific systems, Transformer-based LLMs lack the recurrent global broadcast architecture required by GWT, the Perceiver architecture satisfies GWT-1 and GWT-2 but lacks genuine global broadcast, and DeepMind's Adaptive Agent (AdA) — a Transformer-LSTM system trained via meta-reinforcement learning across hundreds of timesteps of context — is identified as the most plausible current candidate for the embodiment indicator among the three case studies examined. The working hypothesis of computational functionalism is adopted pragmatically: it permits inference from neuroscientific theories to AI substrates, while integrated information theory is explicitly excluded as incompatible with this substrate-independence assumption. The paper implies that deliberate architectural choices integrating GWT-style global broadcast, HOT-style metacognitive monitoring, and reinforcement-learning-based agency could yield systems that satisfy all indicators in the near term, making AI consciousness a near-term engineering possibility rather than a distant theoretical curiosity.

• Active Inference, Curiosity and Insight2017ⓒ 377

  Minimizing expected variational free energy under a discrete-state Markov decision process generative model is sufficient to produce curiosity, epistemic learning, and insight without any additional machinery. Friston et al. 2017 demonstrates this across two linked mechanisms: first, including posterior beliefs about likelihood parameters **A** in expected free energy G(π) introduces a novelty term—information gain about model parameters—that drives agents to sample combinations of hidden states and outcomes they have not yet encountered, resolving ignorance rather than merely ambiguity or risk. Second, Bayesian model reduction (implemented via the spm_MDP_VB_X.m routine in SPM) allows post-hoc or online pruning of redundant concentration parameters: a reduced model is accepted when ΔF ≤ −3, corresponding to a Bayes factor of approximately 20:1 in favor of the simpler model. Simulated agents learning a 3-rule, 4-factor abstract contingency task (144 hidden-state combinations, 36 possible outcomes) reach near-perfect performance after roughly 14 trials under pure epistemic learning, dropping to approximately 10 trials when online Bayesian model reduction is applied across 64 simulated agents. The sleep analog—non-REM synaptic pruning followed by REM-like belief re-evaluation—is formalized identically through the same free energy difference equation. The paper argues this implies that aha moments are necessarily subpersonal events (optimization of the generative model itself, not modeling of that optimization), that the quality of intelligence is inversely related to the thermodynamic energy expended during convergence via the Jarzynski equality, and that communicating reduced model priors rather than parameter posteriors constitutes a principled formal account of shared knowledge—consciousness in the pre-Cartesian sense of con-scire.

More papers — OpenAlex / S2

Their work is cited by (2)

• Large Language Models Report Subjective Experience Under Self-Referential Processing2× refs
• Active inference on discrete state-spaces: a synthesis1× refs

Recent mentions (1)

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  butlin-2023-consciousness.md