I know a large organization who's built their AI framework completely around this concept, and I feel that it's not really meaningful concept with the capabilities of current models.
Do we? I just learned from a speaker[1] that we literally need words to recognize emotions. People who have a poor vocabulary have lower emotional intelligence because without being able to attach a word to an emotion, the brain is unable to recognize & process it.
[1] Dude seemed to be knowledgeable about the subject. He's a specialized trainer, should be educated in this exact field. So hopefully I'm not lying to anyone here :)
Someone expresses an emotion but doesn't know how to react to it, their inner group all have an opinion about it, and the consensus is selected as the "appropiate" reaction to it. The individuals who react this way will claim this consensus is the same as emotional intelligence.
Just as there are also people who react in one way, and completely disregard any external opinion about it. They simply have firm opinions and don't need the consensus.
I will not comment on who can belong to each group, that's an exercise for the reader.
'slow' means making one or several action-dependent forecasts, evaluating the expected value of the outcomes, and making a decision based on that.
Neither map exactly to the situation with LLMs, but very roughly, the first is analogous to trained classifiers and the second to reasoning models.
The analogy breaks down, since each instance of token being produced is an example of a policy execution (system 1), and reasoning is just stringing lots of these together. But there are those who argued, before LLMs, that system 2 is just "policy composition" anyway...
That seems to fit the fast vs slow model of human thought reasonably well.
That’s still several orders of magnitudes too slow to fit fast vs slow. Think of 30ms vs 3-4 seconds to get an idea of what we’re talking about here
In a human 30ms vs 3-4 seconds is a 1:100 ratio. Single vs multiple passes with an LLM varies but a 1:100 ratio isn’t unrealistic. So with enough compute and the right workload single vs multiple pass LLM could sit in that exact same 30ms vs 3-4 second timeframe.
So to talk about system 2 in AI we need to talk about consciousness. As long as AI is not officially conscious there is no System 2 thinking implemented
The process of internal refinement without external action however fits.
The systems apply for humans and describe conscious and subconscious processing. Without that the entire reference to thinking fast and slow is bullshit.
Using conscious vs subconscious processing is not however a meaningful definition. Subconscious processing isn’t necessarily fast. Visual processing and sensory integration can be quite slow without any conscious input.
But for example the amygdala is super fast at tagging dangerious situations with emotions.
And humans socialize subconsciously, so all the small talk conversations are all subconsciously generated.
The definition from the book was:
System 1 is subconscious, fast, automatic and relies on heuristics
System 2 is conscious, slow, deliberate and effortful
If he’s defining System 3+ then that kind of specificity could still work, but from what I understand he doesn’t thus making subconscious a poor fit for the systems described.
Saying that danger classification is slow is very clearly false because animals must react fast to danger or die.
You bringing up something bogus like system 3 makes me think this conversation is pointless and Im just chatting with a system 1 that does not have good shortcuts to have a good conversation about this so it's making new up contexts.
These systems exist, if the author you’re referring to is unaware or unwilling to discuss major issues with their classification then that’s a hit to their credibility.
Danger isn’t some universal thing, people are adapted to notice fact moving objects heading towards them quickly very fast. A very dangerous snake isn’t in that category of danger and can take a significant chunk of time to notice after you’ve stopped looking in that direction. Noticing social danger is extremely critical and glacial by any of the metrics previously discussed.
Emotions cover a huge range of complex interactions that depend on many different parts of the brain but hormones are inherently slow to diffuse through the brain. Pair bonding between a mother and child needs to happen, it doesn’t need to happen in 50ms.
If a tiger attacks you, you better react fast, but one strictly human example is noticing and reacting to angry faces.
An angry person comes at you with a bloody hammer you react before you understand what's even happening
I can only assume you have some sort of philosophical objection here, but this is information processing.
Neurons are reacting to a range of chemical signals not just a single neurotransmitter because they don’t map to what CS style neural networks. Understanding the brain requires understanding the nuances not simplified models.
When are you measuring?
Systems 1 thinking is closer to precomputed tables in some ways. That is by evolution or massive amounts of training your neural network has a narrow fast path it can execute with as little compute at execution as needed.
LLM’s operate strictly feed forward neural networks.
There’s a reason reflexes for the feet are handled by the spine that’s got nothing to do with total processing power.
Of course the shapes of what an AI can do in fast vs slow are quite different.
So the entire debate is fubar.
Except, we do.
This choice doesn't really constrain what the organization can do, either. Pop-sci books have plenty of wiggle room in interpretation, and afford a lot of "you're holding it wrong" dismissals of criticism, that with a bit of clever copywriting, the organization can do absolutely anything and still claim it's embodying the framework/theory of the book.