The paper assumes a fundamental difference between the study of consciousness and the study of intelligence and cognition more widely construed. I question this assumption, drawing on work by the (badly named) “illusionists.” Once we see past the illusions, the landscape looks different, although Seth’s core conclusions (about the importance of living organisations) might still obtain.

Seth’s argument relies on the unargued-for claim that if computational functionalism is false, conscious AI is impossible. I show why the claim is in need of support by way of an analogy. I use a different analogy, involving artificial flight, to show that even if human consciousness is ineliminably biological, this does not imply the impossibility of non-biological, computational AI.

Seth’s arguments for biological naturalism are not actually arguments for substrate dependence. Most of the criteria he proposes – including embodiment, embeddedness, active inference, allostasis, and metabolism – could be satisfied by digital organisms existing within a sufficiently detailed simulation running on Turing computation.

Living things are special because they are valuing subjects, for whom things can be good or bad. This is in virtue of their having internalized optimization processes, tied to persistence, that evaluate whether things are going well or poorly and adjust appropriately. Those evaluations, when integrated from diverse sources and widely available, provide the basis for conscious experience.

Digital computation may not be sufficient for consciousness, but this does not warrant biological naturalism. Rejecting coarse-grained Turing-computational functionalism and adopting a broader computationalism with no built-in syntax/semantics distinction, undermines the motivation for biological naturalism. Substrate-dependent processes of the kind found in living things may be necessary for the emergence of consciousness, but these are functional requirements, not intrinsic properties.

We comment on Anil Seth’s target article from the perspective of theoretical computer science, a branch of mathematics that considers computation under limited resources, and from our own perspective developing a formal machine model for consciousness, the Conscious Turing Machine. From these perspectives, we argue that Seth makes a case for artificial intelligence consciousness. We also address misconceptions about computation.

Brain waves driven by electrochemical processing may be necessary for consciousness, and if so, that will challenge consciousness in artificial intelligence (AI) and perhaps in some invertebrates.

The relationship between subjectivity and its substrate can be understood via the notion of compatibilist emergence, accounting for the plurality of ways nature can host new dynamical laws at higher scales. We discuss how the autopoietic character of life may require and promote the kind of emergence necessary, albeit not sufficient, for consciousness.

Seth’s case for biological naturalism rests on a worryingly speculative foundation: the free energy principle. Arguments for computational functionalism are likewise speculative. Yet I am optimistic that the clash can be resolved empirically. Alleged dependencies of consciousness markers on biological properties can be tested comparatively, and a pattern of failed predictions would tilt the odds towards computational functionalism.

Seth argues that anthropocentrism, anthropomorphism, and human exceptionalism bias us towards an overly optimistic view of the prospects of conscious AI. I agree, but argue that these phenomena might also bias us towards an overly pessimistic view of the prospects for artificial consciousness. Furthermore, it is not always clear when these factors are functioning as “biases.”

Seth’s biological naturalism builds on ideas from predictive processing, the free energy principle, and active inference to (1) argue that only (some) biological systems are in the consciousness business and to (2) refute the possibility of artificial consciousness, i.e., consciousness in AI and other artificial systems. We find this perspective problematic for a few different reasons.

We agree that consciousness, as we know it, is inextricably biological – but not every biological detail is essential. Identifying which features are necessary, across scales, is vital for assessing the limits of computationalism. This process will reveal a richer, more biologically informed computationalism – one that future machines could, in principle, instantiate, but only after much deeper biological understanding.

No one is in a position to say what really matters for consciousness, or whether only living systems can instantiate the complexity needed for conscious feelings. Seth’s attempt to connect life to consciousness through the mathematics of active inference and free energy minimization plays one hunch, but it is too soon to say whether it is the best hunch.

As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I’ll instead make the case that consciousness depends on our nature as living organisms – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is or convincingly appears to be conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate ourselves.

The commentators mostly endorsed the value of attending to goals and evaluations in explanations of cultural patterns, including super-attractors. They also voiced concerns about subjective selection, enumerated other factors influencing goals and evaluation, proposed additional super-attractors and processes that might shape them, and demanded greater engagement with power and influence. Many issues signal larger disagreements in the naturalistic study of culture. Here, I respond.

Singh explains how cultural convergence is driven by super-attractors through subjective selection, but this account underestimates how social norms shape which psychological goals are maintained or suppressed. We clarify that social norms filter subjective selection by guiding what individuals regard as effective or desirable behaviors and by redefining their goals, thereby embedding subjective selection within the multilevel normative ecosystem.

This commentary enriches Singh’s subjective selection framework by focusing on the nature of goals. It considers their origins (biology, ecology, culture) and varying salience, allowing the model to account for both highly instrumental actions and seemingly goal-demoted behaviors like ritual, thus broadening its applicability.

Our commentary aims to enrich Singh’s subjective selection framework in two fundamental ways. First, language is necessary for coordinating on shared instrumental goals. Without it, the explanatory burden rests largely on evolved psychology. Second, goals themselves are culturally constructed, socially transmitted, and cumulatively evolving over time. This co-evolutionary dynamic between problems and solutions exceeds what fixed psychological hierarchies can explain.

We propose an extension in which core knowledge – innate, skeletal, and abstract cognitive representations – shape “super attractors.” They shape our cognition and our communication with others. People choose actions based not only on what they feel is right but also on whether they think others will agree. We propose that universal priors shape the space of shared intuitions.