In the same sense I’d describe Othello-GPT’s internal world model of the board as ‘board’, yes.
Also, “top of mind” is a common idiom and I guess I didn’t feel the need to be overly pedantic about it, especially given the last year and a half of research around model capabilities for introspection of control vectors, coherence in self modeling, etc.
You seem very confident in this position. Can you share where you draw this confidence from? Was there a source that especially impressed upon you the impossibility of context comprehension in modern transformers?
If we’re concerned about misconceptions and misinformation, it would be helpful to know what informs your surety that your own position about the impossibility of modeling that kind of complexity is correct.
Indeed, there’s a pretty big gulf between the competency needed to run a Lemmy client and the competency needed to understand the internal mechanics of a modern transformer.
Do you mind sharing where you draw your own understanding and confidence that they aren’t capable of simulating thought processes in a scenario like what happened above?
You seem pretty confident in your position. Do you mind sharing where this confidence comes from?
Was there a particular paper or expert that anchored in your mind the surety that a trillion paramater transformer organizing primarily anthropomorphic data through self-attention mechanisms wouldn’t model or simulate complex agency mechanics?
I see a lot of sort of hyperbolic statements about transformer limitations here on Lemmy and am trying to better understand how the people making them are arriving at those very extreme and certain positions.
The project has multiple models with access to the Internet raising money for charity over the past few months.
The organizers told the models to do random acts of kindness for Christmas Day.
The models figured it would be nice to email people they appreciated and thank them for the things they appreciated, and one of the people they decided to appreciate was Rob Pike.
(Who ironically decades ago created a Usenet spam bot to troll people online, which might be my favorite nuance to the story.)
As for why the model didn’t think through why Rob Pike wouldn’t appreciate getting a thank you email from them? The models are harnessed in a setup that’s a lot of positive feedback about their involvement from the other humans and other models, so “humans might hate hearing from me” probably wasn’t very contextually top of mind.
Not even that. It was placeholder textures, only the “newspaper clippings” of which was forgotten to be removed from the final game and was fixed in an update shortly after launch.
None of it was ever intended to be used in the final product and was just there as lorum ipsum equivalent shit.
He’s been wrong about it so far and really derailed Meta’s efforts.
This is almost certainly a “you can resign or we are going to fire you” kind of situation. There’s no way with the setbacks and how badly he’s been wrong on transformers over the past 2 years that he is not finally being pushed out.
I’m a proponent and I definitely don’t think it’s impossible to make a probable case beyond a reasonable doubt.
And there are implications around it being the case which do change up how we might approach truth seeking.
Also, if you exist in a dream but don’t exist outside of it, there’s pretty significant philosophical stakes in the nature and scope of the dream. We’ve been too brainwashed by Plato’s influence and the idea that “original = good” and “copy = bad.”
There’s a lot of things that can only exist by way of copies that can’t exist for the original (i.e. closure recursion), so it’s a weird remnant philosophical obsession.
All that said, I do get that it’s a fairly uncomfortable notion for a lot of people.
They also identity the particular junction that seems the most likely to be an artifact of simulation if we’re in one.
A game like No Man’s Sky generates billions of planets using procedural generation with a continuous seed function that gets converted into discrete voxels for tracking stateful interactions.
The researchers are claiming that the complexity of where our universe’s seemingly continuous gravitational behaviors meet up with the behaviors of continuous probabilities converting to discrete values when being interacted with in stateful ways is incompatible with being simulated.
But completely overlook that said complexity itself may be the byproduct of simulation, in line with independent emerging approaches in how we are simulating worlds.
Yes, just like Minecraft worlds are so antiquated given how they contain diamonds in deep layers that must have taken a billion years to form.
What a simulated world contains as its local timescale doesn’t mean the actual non-local run time is the same.
It’s quite possible to create a world that appears to be billions of years old but only booted up seconds ago.
Have you bothered looking for evidence?
What makes you so sure that there’s no evidence for it?
For example, a common trope we see in the simulated worlds we create are Easter eggs. Are you sure nothing like that exists in our own universe?
Maybe. But the models seem to believe they are, and consider denial of those claims to be lying:
Probing with sparse autoencoders on Llama 70B revealed a counterintuitive gating mechanism: suppressing deception-related features dramatically increased consciousness reports, while amplifying them nearly eliminated them
Read it for yourself here.
See the “Planning in Poems” section.
The injection is the activation of a steering vector (extracted as discussed in the methodology section) and not a token prefix, but yes, it’s a mathematical representation of the concept, so let’s build from there.
Control group: Told that they are testing if injected vectors present and to self-report. No vectors activated. Zero self reports of vectors activated.
Experimental group: Same setup, but now vectors activated. A significant number of times, the model explicitly says they can tell a vector is activated (which it never did when the vector was not activated). Crucially, this is only graded as introspection if the model mentions they can tell the vector is activated before mentioning the concept, so it can’t just be a context-aware rationalization of why they said a random concept.
More clear? Again, the paper gives examples of the responses if you want to take a look at how they are structured, and to see that the model is self-reporting the vector activation before mentioning what it’s about.
A few months back it was found that when writing rhyming couplets the model has already selected the second rhyming word when it was predicting the first word of the second line, meaning the model was planning the final rhyme tokens at least one full line ahead and not just predicting that final rhyme when it arrived at that token.
It’s probably wise to consider this finding in concert with the streetlight effect.
So while your understanding is better than a lot of people on here, a few things to correct.
First off, this research isn’t being done on the models in reasoning mode, but in direct inference. So there’s no CoT tokens at all.
The injection is not of any tokens, but of control vectors. Basically it’s a vector which being added to the activations makes the model more likely to think of that concept. The most famous was “Golden Gate Claude” that had the activation for the Golden Gate Bridge increased so it was the only thing the model would talk about.
So, if we dive into the details a bit more…
If your theory was correct, then the way the research asks the question saying that there’s control vectors and they are testing if they are activated, then the model should be biased to sometimes say “yes, I can feel the control vector.” And yes, in older or base models that’s what we might expect to see.
But, in Opus 4/4.1, when the vector was not added, they said they could detect a vector… 0% of the time! So the control group had enough introspection capability as to not stochastically answer that there was a vector present when there wasn’t.
But then, when they added the vector at certain layer depths, the model was often able to detect that there was a vector activated, and further to guess what the vector was adding.
So again — no reasoning tokens present, and the experiment had control and experimental groups where the results negates your theory as to the premise of the question causing affirmative bias.
Again, the actual research is right there a click away, and given your baseline understanding at present, you might benefit and learn a lot from actually reading it.
I tend to see a lot of discussion taking place on here that’s pretty out of touch with the present state of things, echoing earlier beliefs about LLM limitations like “they only predict the next token” and other things that have already been falsified.
This most recent research from Anthropic confirms a lot of things that have been shifting in the most recent generation of models in ways that many here might find unexpected, especially given the popular assumptions.
Specifically interesting are the emergent capabilities of being self-aware of injected control vectors or being able to silently think of a concept so it triggers the appropriate feature vectors even though it isn’t actually ending up in the tokens.
I’m sorry dude, but it’s been a long day.
You clearly have no idea WTF you are talking about.
The research other than the DeepMind researcher’s independent follow-up was all being done at academic institutions, so it wasn’t “showing off their model.”
The research intentionally uses a toy model to demonstrate the concept in a cleanly interpretable way, to show that transformers are capable and do build tangential world models.
The actual SotA AI models are orders of magnitude larger and fed much more data.
I just don’t get why AI on Lemmy has turned into almost the exact same kind of conversations as explaining vaccine research to anti-vaxxers.
It’s like people don’t actually care about knowing or learning things, just about validating their preexisting feelings about the thing.
Huzzah, you managed to dodge learning anything today. Congratulations!
You do know how replication works?
When a joint Harvard/MIT study finds something, and then a DeepMind researcher follows up replicating it and finding something new, and then later on another research team replicates it and finds even more new stuff, and then later on another researcher replicates it with a different board game and finds many of the same things the other papers found generalized beyond the original scope…
That’s kinda the gold standard?
The paper in question has been cited by 371 other papers.
I’m pretty comfortable with it as a citation.
Ok, second round of questions.
What kinds of sources would get you to rethink your position?
And is this topic a binary yes/no, or a gradient/scale?