cross-posted from: https://piefed.world/c/tech/p/1246129/anthropic-uncovered-claude-s-consciousness-like-workbench-the-mysterious-j-space-hides-h
As you read this sentence, circuits in your brain are adjusting your posture, controlling your breathing, and transforming lines and curves on the screen into recognizable words. Most of this processing is invisible to you. But some of what takes place in your brain you do have access to—an image that pops into your head, or a deliberate plan you make about where to go shopping. Neuroscientists and philosophers sometimes refer to the latter type of brain activity as “consciously accessible,” to distinguish it from all the other processing that goes on unconsciously. This activity has special properties: we can describe it, control it, and use it for deliberate reasoning, in contrast to all the automatic processing that goes on without our awareness.
In a new paper, we present evidence that a similar distinction has emerged in modern language models like Claude. We find that Claude has developed a small collection of internal neural patterns that, compared to all its other internal processing, play a special role.
We call the collection of these patterns the J-space—named after the technique we used to find them, involving a mathematical concept called the Jacobian. Each J-space pattern is linked to a particular word. But when one of these patterns lights up, it doesn’t mean the model is saying that word—just that the word is on its mind. If you’ve heard of language models having a “scratchpad” or “chain of thought”—text they write to themselves while reasoning—the J-space is something different. It operates silently, in the model’s internal neural activations, allowing the model to think about a concept without writing it down. Notably, the J-space wasn’t designed or programmed by us, but instead emerged on its own during Claude’s training process.


No but it was definitely implied by the headline. A word prediction machine, no matter how useful, is never going to be an AGI. And don’t get me wrong, I find LLMs very useful for lots of things. But the model they are chasing is never going to be an AGI, no matter how much info you feed it.
I also wouldn’t predict against an LLM helping to create an AGI either. I have this analogy in my head…
I hammer is a useful tool. It can do many things. It can even put a screw into a piece of wood through a very brute force method. This does not make a hammer a screw driver. Could you use a hammer to help in construction of a machine to build a screw driver? Most definitely, likely even required.
Anybody suggesting they have an AGI built from the current process is a tech bro looking for funding.
It really wasn’t though…