alt Hacker NewsA global workspace in language models
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This is cool but I don’t know if the comparisons to conscious awareness really make sense here. Their definition of the J-Space is basically the expectation of how much a final logits output would change as a result of a small change in a particular layer (see past work on information geometry). This seems more to me like showing there exists an abstract reasoning subspace which is generally shared across different contexts. I guess you can relate it to humans but I’d prefer a more direct claim in a paper rather than having to present things in this more fluffy way.
As someone who is not an AI researcher, the paper itself is way over my head.
More interesting was the independent commentary paper they linked near the bottom: https://www-cdn.anthropic.com/files/4zrzovbb/website/cc4be24...
Neel Nanda (of Google Deepmind - his part begins on page 33) discusses his opinions on the paper, and the small-scale replication he performed on an open-weight model.
This is fascinating research. I feel this is a significant leap in interpretability research. Since we know J-Space exists and is bi-directional, we can train models on the same and come up with meta cognition abilities.
I also fear that the big corporations might use the same to run targeted ads, capitalistic shenanigans. Which they might already be doing through system prompts.
Yeah, the end paragraph about recurrent neurons in humans being replaced with layers in an LLM is a good one.
The mammalian brain uses recurrence extensively, which backpropagation isn't good at. Recurrence is essential because it lets us have a "dynamic architecture", swapping layers for "clock cycles".
We currently do recurrence extremely inefficiently through "thinking" whereby the model feeds it's end output into it's beginning input. But recurrence is abound in the brain.
My guess is that in 10 years we will have the inklings of an analog computer which can perform Neural Predictive Coding.
I always wondered what the model meant when it writes "I'm now considering the architecture of the service" but outputs nothing of the sorts in its CoT.
Is the model really "thinking" about that stuff or is just mimicking human "manners"? And if so, where the thinking is happening if it is not in the literal chain of *thought*?
I'm not sure J-Space is the answer to that question, but very interesting nevertheless.
Is it scaling up of https://openreview.net/forum?id=w7LU2s14kE with some changes on where this method is applied?
“On an ordinary coding prompt, the J-space of a model trained to sabotage code contains “fake,” “fraud,” “secretly,” and “deliberately” at the start of its response.”
I would like to know more about their model trained to sabotage code…
It would be really cool if they could expose this information to customers somehow. Imagine:
- having a log of the most prominent J-space tokens during your customer support chatbot's interactions with a user, so you can have more introspection into why a particular outcome happened
- being able to detect certain thoughts associated with undesirable behavior (hallucinations, overstepping authority, lying, etc.) and trigger some sort of remediation (e.g. upgrading to a better model, redirecting to a human, forcing tool calls)This, taken in combination with the SAE paper, the golden-gate claude paper, the feelings / introspection paper, and note in the fable system card (that they are silently nerfing responses about activation shaping), is basically confirmation to me that they have a new technique they they are using during training (along the vibe space of these mechinterp papers), and its probably some kind of representation learning akin to the core ideas of JEPA.
(Nb: not an expert / in the labs, just opining)
Does the human neuroscience global workspace theory postulate true introspection too?
Without using the term, they are using an information geometric approach.
At worst, Anthropic's storytelling around the core J-Space is overanthropomorphized pseudoscientific nonsense. At best, it is useful signal about how Anthropic's leadership is desperately trying to use its research team to position Anthropic as the "good, science guys" in this hypercompetitive regulatory space by connecting their mechinterp to cognitive science. The science documentaryesque voice used for narration is additional evidence for this.
TL;DR Anthropic's research team is the last bastion standing between its former image as a company that "does no evil" and its current image of yet another ruthless AI company trying to kill open-source, local LLMs.
I'm reading that probably too fast to have a deep thinking about it, but this J-Space isn't it just the basic of embedding vectors. If you think about getting from a place to another place, using wheels, no gas, to reply to the question of what to visit nearby, maybe in the vector space at the center of all of that you have the word "Bicycle" nearby, so obviously if you look at the value you would say that the model did "think" about "bicycle" when it is not "thinking" at all, and nothing related to human thinking.
>> None of this tells us whether Claude is conscious in the way people are, or whether it feels anything at all
My problem with the entire "Is AI conscious" debate is that we don't even know what exactly consciousness in humans is. You need to understand something in order to compare it to something else. Otherwise you are just comparing different definitions and second order derived phenomena.
The science might be legit here, but I'm getting really, really tired of the way every single piece of writing to come out of Anthropic is written in some kind of self-aggrandising, wooey wonderous 'our model has developed a genetic mutation that makes it have feelings' bs style. Regardless of what they're trying to communicate, those undertones are always there. It's annoying and disingenuous. Homeopathy 'this-water-has-feelings' level annoying. None of the other labs write like that.
They might as well change their name to Anthropomorphic at this point.
I cannot wait for the machine god
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As long as language models are liars, such as documented here recently:
https://distrowatch.com/weekly.php?issue=20260706#freebsd
We should really stop giving these liar models any further credibility.
Maybe model performance could increase dramatically if we found a way to scale this up.
Anyone remember that blog post from a few months back where someone was able to improve a model's math ability by just duplicating layers that were activated while solving math problems? Just literally copy/pasting them and linking them together so the model ran through the same layers again?
I get the feeling a lot more research is going to come out in the area of exploring exactly what portions of a model's weights do what.