# Consciousness may be a learning algorithm for animal brains

Markdown copy of https://realalpha.app/s/consciousness-may-be-a-learning-algorithm-for-animal-brains/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Joscha Bach](https://realalpha.app/people/joscha-bach/) · Founder, California Institute for Machine Consciousness · Developed cognitive architectures and leads CIMC's research into machine consciousness and models of the mind.
- Host: [Tim Scarfe](https://realalpha.app/people/tim-scarfe/) · Host, Machine Learning Street Talk
- Show: Machine Learning Street Talk
- Episode: [Why Consciousness Might Not Be Hard At All (Joscha Bach)](https://realalpha.app/v/yW_ngvMFlYs/), Nov 2, 2026
- Moment: 31:51 to 39:38 of the episode (8 min), playing on https://realalpha.app/s/consciousness-may-be-a-learning-algorithm-for-animal-brains/; the episode from there: https://realalpha.app/v/yW_ngvMFlYs/?t=1911

## Quote

> “might be a **biological learning algorithm**, a training algorithm for animal brains”
> — [Joscha Bach](https://realalpha.app/people/joscha-bach/) at [33:17](https://realalpha.app/v/yW_ngvMFlYs/?t=1997) · Machine Learning Street Talk

## Takeaways

1. Consciousness may stabilize perception and learning in developing brains.
2. Self-organizing machines could potentially discover the same process.
3. Failure to recreate it in machines would not disprove the biological hypothesis.

## About this segment

Joscha Bach proposes that consciousness helps self-organizing brains learn, and that machines might discover it under similar conditions. He also explains why the biological and machine hypotheses could fail independently.

From “Why Consciousness Might Not Be Hard At All (Joscha Bach)”, Nov 2, 2026. [Watch the full episode](https://realalpha.app/v/yW_ngvMFlYs/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[32:04](https://realalpha.app/v/yW_ngvMFlYs/?t=1924) self-organizing system so to me the machine consciousness hypothesis has two parts one is a hypothesis about the role in nature and implementation of consciousness in organisms it starts out with this description of what we agree on what consciousness is and I hope that we can agree on it on the phenomenology of consciousness and it's basically this experience of a now and perception of perception and optionally the perspective of an observer that experiences itself in the act of observation this first person perspective there's also a minimal consciousness which is simply attention on attention itself.

[32:41](https://realalpha.app/v/yW_ngvMFlYs/?t=1961) You don't have an observer present. And when we can agree on what we mean by the phenomenology of consciousness, we can then speculate about why consciousness is there and why we need to be conscious in order to function as human beings, as human minds, as intellects, and to develop. And what we observe is that consciousness happens very early in our development. We are conscious as infants already.

[33:04](https://realalpha.app/v/yW_ngvMFlYs/?t=1984) And if infants don't wake up, if this ignition of consciousness doesn't happen, if the lights don't go on, then we will not observe directed attention and learning and the formation of a world model in the infant and learning of language at some point. Instead, the infant will remain vegetative. We will not turn into a human being. So consciousness seems to be crucial to train a brain like ours. And it leads me to the hypothesis that consciousness might be a biological learning algorithm, a training algorithm for animal brains, At least animal brains, I don't know if plants are conscious.

[33:39](https://realalpha.app/v/yW_ngvMFlYs/?t=2019) That's something that we might have to find out in simulations once we understand what consciousness is. But if consciousness is basically a pattern in the communication between cells that leads naturally to the emergence of an observer that stabilizes itself as an observer to make learning happen and perception happen and so on, then we have a hypothesis about what consciousness is in the biological organism.

[34:04](https://realalpha.app/v/yW_ngvMFlYs/?t=2044) it's basically the simplest way to train a self-organizing system like us and the way in which it trains it up automatically leads to this phenomenal experience this experience of what it's like this would be the hypothesis about biological consciousness and this hypothesis might be wrong maybe it's something else altogether maybe it's something much bigger but I don't see a reason to think that it's much more complicated than this and if this hypothesis is true then the second part, the machine consciousness hypothesis might also be true and this is that we might be able to reconstruct the conditions for self-organization in today's machines so if we basically limit today's machines in such a way that they can only learn in self-organizing ways and we give them tasks that are suitable in the large enough search space

[34:55](https://realalpha.app/v/yW_ngvMFlYs/?t=2095) then it might be possible that these machines discover consciousness in the same way as the brain of the infant discovers it and we will see its presence by the fact that suddenly the system undergoes some phase transition that enables it to learn and at some point it will train itself up into something that will also naturally report on phenomenology and so on so in this way we won't have a turing test for consciousness because the turing test is about performance where turing's test does not care about how you produce the dialogue for turing an llm is sufficient as long as the LLM is good enough at writing a poem and revising it and at solving mathematical problems and playing chess and so on and so on.

[35:36](https://realalpha.app/v/yW_ngvMFlYs/?t=2136) Turing doesn't care about whether the machine is also conscious, whether it has the same internal mechanisms. But for consciousness, we do care whether there is an actual experience of what it's like in the system. And so this is more not a test, but it's an interpretation. And the interpretation here is, we interpret the biological consciousness as something that forms under these and these conditions for that and that purpose.

[36:02](https://realalpha.app/v/yW_ngvMFlYs/?t=2162) And if that is correct, then we might be able to recreate this into a causal system that has an equivalent structure. And these two hypotheses, the machine consciousness hypothesis and the biological consciousness hypothesis, can be wrong independently of each other, of course. It might be that my idea of what biological consciousness is, is correct, but we're still not able to recreate it in the artificial substrate.

[36:26](https://realalpha.app/v/yW_ngvMFlYs/?t=2186) And this could have multiple reasons. For instance, maybe our computers are not fast enough. Maybe we require too many of the moving parts of the individual cells to make consciousness happen. Maybe just having a pattern across neurons and so on is not good enough. But why should I expect this to happen? Because the current AIs are fast enough to produce, for instance, game engines that look very much like our own internal experience.

[36:53](https://realalpha.app/v/yW_ngvMFlYs/?t=2213) And they can produce output that happens at roughly the same rate as human brains can make it. And they can also process inputs at roughly the rate at which human brains can produce input, give or take. So we are now with our computers in this ballpark and with our algorithms in the ballpark of what human brains can be doing. And so is this dream within a dream, this dream of actually dreaming a dream, much, much more difficult to achieve?

[37:19](https://realalpha.app/v/yW_ngvMFlYs/?t=2239) Does it require much more compute than producing the contents of the dream? And I doubt that this is the case. If it's not the case that it's more complicated to dream of dreaming than just to have the contents of the dream that the LM can already produce, or the frontier model, then we should expect that today's computers are fast enough. Another way in which the project can fail is that the search space is too large, that the ways in which a biological system can train itself, there are too many possibilities to discover them independently.

[37:52](https://realalpha.app/v/yW_ngvMFlYs/?t=2272) And maybe we don't know how long nature took to discover it. Maybe consciousness is indeed discovered in every individual brain. But maybe it's not. Maybe the switch from single-cellular life to multicellular life took so long because morphogenesis this algorithm that allows organisms to become coherent at scale is so difficult to discover and mike levin has this hypothesis that this algorithm for morphogenesis the algorithm that makes an organism coherent with itself is exactly the same one as the one that happens in the mind when it becomes coherent with itself so this coherence maximizing pattern that we observe in our own mind to play out when we are conscious is maybe the same pattern at a different time scale that happens in the organism and so consciousness and life multicellular life

[38:40](https://realalpha.app/v/yW_ngvMFlYs/?t=2320) at least could be the same and if that is the case maybe it took a very very long time to discover and you don't discover it independently in your consciousness but you get infected by your mother in utero or you basically vibe with your biological environment with all the biological organisms around you and they basically nudge your mind to discover consciousness So it's much, much easier. And without this nudge, maybe the search space is too large for our computers to become conscious.

[39:07](https://realalpha.app/v/yW_ngvMFlYs/?t=2347) And maybe we would need to surround our GPUs with lots of antennas that can pick up the vibe from the biological environment in the right way so it gets inspired by it and discovers the same thing. It's a very poetic idea. But again, why would this be the case? It's not necessarily the case, right? It would not be my natural expectation that this is the way.