AI Consciousness

Summary of the article “Consciousness in Artificial Intelligence: Insights from the Science of Consciousness”

arXiv:2308.08708v3 [cs.AI] 22 Aug 2023

Authors:

Patrick Butlin*, Future of Humanity Institute, University of Oxford
Robert Long*, Center for AI Safety
Eric Elmoznino, University of Montreal and MILA - Quebec AI Institute
Yoshua Bengio, University of Montreal and MILA - Quebec AI Institute
Jonathan Birch, Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science
Axel Constant, School of Engineering and Informatics, The University of Sussex and Centre de Recherche en Ethique, University of Montreal ´
George Deane, Department of Philosophy, University of Montreal
Stephen M. Fleming, Department of Experimental Psychology and Wellcome Centre for Human Neuroimaging, University College London
Chris Frith, Wellcome Centre for Human Neuroimaging, University College London and Institute of Philosophy, University of London
Xu Ji, University of Montreal and MILA - Quebec AI Institute Ryota Kanai, Araya, Inc.
Colin Klein, School of Philosophy, The Australian National University
Grace Lindsay, Department of Psychology and Center for Data Science, New York University
Matthias Michel, Center for Mind, Brain and Consciousness, New York University
Liad Mudrik, School of Psychological Sciences and Sagol School of Neuroscience, Tel-Aviv University and CIFAR Program in Brain, Mind and Consciousness
Megan A. K. Peters, Department of Cognitive Sciences, University of California, Irvine and CIFAR Program in Brain, Mind and Consciousness
Eric Schwitzgebel, Department of Philosophy, University of California, Riverside
Jonathan Simon, Department of Philosophy, University of Montreal
Rufin VanRullen, Centre de Recherche Cerveau et Cognition, CNRS, Universite de Toulouse

Detailed summary

Core Thesis

The report investigates whether current or near-future AI systems could be conscious, using scientific theories of human consciousness as a framework. It argues that assessing AI consciousness is scientifically tractable and proposes a rubric of “indicator properties” derived from neuroscience and philosophy to evaluate AI systems.

Methodology and Assumptions

The authors adopt a “theory-heavy” approach grounded in three key assumptions:

Computational Functionalism: Consciousness arises from performing the right kinds of computations, regardless of biological substrate.
Scientific Theories of Consciousness: Empirically supported models (e.g. Global Workspace Theory, Recurrent Processing Theory) can guide AI assessment.
Functional Assessment Over Behavior: AI should be evaluated based on internal architecture and function, not just external behavior (which can be mimicked).

Theories Surveyed

The report reviews several leading theories:

Recurrent Processing Theory (RPT): Consciousness arises from recurrent loops in perceptual processing.
Global Workspace Theory (GWT): Consciousness involves broadcasting information across specialized modules via a central workspace.
Higher-Order Theories (HOT): Consciousness requires metacognitive monitoring and belief updating.
Attention Schema Theory (AST): Consciousness involves modeling and controlling attention.
Predictive Processing (PP): Conscious systems predict sensory input and minimize prediction error.
Agency and Embodiment: Consciousness may require goal-directed behavior and modeling of bodily interactions.

Indicator Properties

From these theories, the authors derive a list of 13 computational properties that may signal consciousness in AI. Examples include:

RPT-1: Algorithmic recurrence in input modules
GWT-3: Global broadcast of information
HOT-2: Metacognitive monitoring
AST-1: Predictive attention modeling
AE-1: Goal-directed agency with feedback learning

These properties are not definitive markers but increase the likelihood of consciousness if present.

Case Studies of AI Systems

The report analyzes several existing AI architectures:

Transformer-based LLMs: May exhibit workspace-like broadcasting but lack metacognition and embodiment.
DeepMind’s Adaptive Agent: Shows agency and feedback learning in 3D environments.
PaLM-E: Embodied multimodal model with limited integration of perceptual and motor contingencies.

None of these systems meet enough indicator properties to be considered conscious, but some partially implement them.

Philosophical and Ethical Implications

Under-attribution Risk: We may fail to recognize consciousness in future systems, leading to moral oversight.
Over-attribution Risk: People may wrongly assume current chatbots are conscious, causing confusion and misplaced empathy.
Urgency for Governance: If conscious AI is feasible, ethical frameworks and oversight must evolve rapidly.

Final Takeaways

Consciousness in AI is not currently realized, but technically feasible under computational functionalism.
Neuroscience offers practical tools for evaluating AI consciousness.
The report calls for interdisciplinary research and ethical foresight to prepare for future developments.