In recent interviews, Hinton has unabashedly spoken of AI being conscious, among many other claims, such as worrying that AI will fool humans into ceding power to AIs against our best interests, and that AIs ‘really understand’ us.

Here are some short videos of these claims in action.

What strikes me most about these conversations as a philosopher of mind is the complete absence of any explanation as to why Hinton thinks any of this.

Consider, for example, Hinton’s argument for why AI really understands. He states that anybody who regularly uses a chatbot knows that AI understands, using a kind of reductio ad absurdum to make his point. Anyone who denies AI lacks understanding is committed to believing an AI can give you the correct answer to a question without understanding the question. This is absurd, he says, so it’s not true.1

Something must be capable of understanding a question to answer a question. This argument alone might not move you, though the claim itself is not entirely unreasonable. In fact, I’d be willing to accept the claim, though I’d draw a different conclusion to Hinton. I wouldn’t concede AI consciousness on these grounds. Id respond that AI doesn’t actually answer questions. It produces outputs given inputs.

But this is beside the point, because Hinton’s views on AI are rooted in a lot more than the arguments offered in these videos, and that’s what’s frustrating.

What’s absent from the conversation is Hinton’s beliefs on the nature of understanding, how the human brain works, and how these views of his inform his views on AI consciousness and intelligence. That’s what we’ll get into here.

What Does Hinton Think the Brain is Like?

The Symbolic View and The Connectionist View

There are two big views on how the brain works: a symbolic view of the brain (it goes by other names too) and a connectionist view of it.

Hinton is a connectionist. He essentially believes that the human brain is one big neural network, and that, as such, it processes information very similarly to LLMs. Of course, Hinton and everyone else recognises that LLMs learn differently to humans, but he believes that fundamentally the human brain and LLMs are both non-symbolic distributed weight systems (these points are hinted at in the videos above, just not explained in any detail).

According to a connectionist model of the brain, we can explain human cognition in terms of neuronal connections, specifically, in terms of the patterns of activations that emerge across networks of neurons and the strengths of those patterns.

This view of the brain is sharply contrasted with a symbolic view in cognitive science, according to which the brain is like a digital computer. On this view, human cognition is understood in terms of the manipulation of symbols using formal rules.

Consider, then, what it means according to each view, to ‘understand’ something.

The Classic Symbolic View

To understand something according to a classic symbolic view of the brain, one needs to correctly manipulate a specific representation of whatever it is you’re trying to understand (a ‘representation’ of something is basically a fancy word for a physical symbol that carries information about that thing. e.g, that the concept bachelor contains information about males and marriage)

To understand what a bachelor is, you need to possess the appropriate rules to correctly use the term bachelor. Such a rule might be bachelor = unmarried adult male. Possessing an understanding of the concept bachelor on this view, just is the ability to check whether something is a male, is unmarried, and is an adult, and to return an appropriate output, namely, that ‘X is a bachelor’ or that ‘X is not a bachelor’.

The Connectionist View

According to a connectionist view, the brain has no rules and has no physical symbols in this sense. All it has are patterns of activations across neurons and varying strengths among those different patterns. In our last example, the concept bachelor does not consist of any information stored in symbols. And understanding what a bachelor is does not require possessing rules to manipulate that symbol.

It consists of the right neuronal patterns of activations, and those neuronal patterns of activations, further standing in the right relationships to other patterns of neuronal activations that are relevant to the concept.

E.g., to understand what a bachelor is requires the right patterns of neurons to activate to identify a bachelor (e.g., when you encounter one) and to have this neural pattern overlap with other appropriate patterns, for example the activations for ‘unmarried’, ‘male’ and ‘adult’, while diverging from unrelated ones, like ‘women’, ‘child’ and ‘married’.

Why Hinton Thinks AI’s ‘Understand’

For a brief overview of how LLMs work, see section 1 of this article before reading further

Now you’re in a better position to understand why Hinton thinks AIs ‘understand’. He’s a connectionist. He views the brain as one enormously vast and complex neural network, which, though not identical to an LLM, is built of the same basic principles.

LLMs, like the brain, is built on distributed representations and learned weights. So when we ask what it means for an LLM to understand something for Hinton, it more or less means exactly the same thing as what it means for a human to understand something. Once an LLM has been appropriately trained, and its ‘representations’ —that is, its patterns of activations across its MLP and attention layers —stand in the right relationships to the relevant concepts (i.e., other activation patterns) then an LLM understands these concepts. That is why Hinton thinks LLMs understand you when you ask it a question and it gives you the ‘right’ answer.

Now, I want to be clear, connectionism as a theory does not claim to explain consciousness. Moreover, I don’t mean to suggest that all connectionists believe that ‘understanding’ can necessarily be reduced to the picture I have described above (instead, I am interested in offering a possible way of understanding Hinton’s own claims about AI). So, Hinton’s view on consciousness are unexplained based on his connectionism beliefs alone. With that said, I do think we are in a better position to understand his views on consciousness if we consider his connectionist beliefs.

Hinton is well known for having built and designed foundational neural network models. He has even won the Nobel Prize “for foundational discoveries and inventions that enable machine learning with artificial neural networks”2

Now, if you remember that Hinton believes LLMs share the same basic foundational architecture as human brains, it is not too difficult to see how he might believe AIs are conscious. To be honest, if I were convinced the brain was a neural network —and I am really not convinced of this — I would at least consider it incumbent on me to seriously consider whether AI is conscious too.

Now, here comes the kicker. I really don’t see any reason to believe connectionism about the brain is true. But that’ll be a post for another time.

In the meantime, let me say that what is desperately missing in the media is any kind of educational content, or even laying out of background assumptions, to explain the views of people like Hinton. There is no denying Hinton is a man of genius, talent, and exceptional accomplishment. That is all the more reason to make very clear what his background beliefs are. Since, we are already conditioned to believe what he says.

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