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.
Taxi Driver, everyone except De Niro is a Muppet.
(But in the “you talkin’ to me” scene the reflection is also a Muppet.)
Yeah. The confabulation/hallucination thing is a real issue.
OpenAI had some good research a few months ago that laid a lot of the blame on reinforcement learning that only rewards having the right answer vs correctly saying “I don’t know.” So they’re basically trained like taking tests where it’s always better to guess the answer than not provide an answer.
But this leads to being full of shit when not knowing an answer or being more likely to make up an answer than say there isn’t one when what’s being asked is impossible.
For future reference, when you ask questions about how to do something, it’s usually a good idea to also ask if the thing is possible.
While models can do more than just extending the context, there still is a gravity to continuation.
A good example of this would be if you ask what the seahorse emoji is. Because the phrasing suggests there is one, many models go in a loop trying to identify what it is. If instead you ask “is there a seahorse emoji and if so what is it” you’ll get them much more often landing on there not being the emoji as it’s introduced into the context’s consideration.
Can you give an example of a question where you feel like the answer is only correct half the time or less?
The AI also has the tendency inherited from the broad human tendency in training.
So you get overconfident human + overconfident AI which leads to a feedback loop that lands even more confident in BS than a human alone.
AI can routinely be confidently incorrect. Especially people who don’t realize this and don’t question outputs when it aligns with their confirmation biases end up misled.
Gemini 3 Pro is pretty nuts already.
But yes, labs have unreleased higher cost models. Like the OpenAI model that was thousands of dollars per ARC-AGI answer. Or limited release models with different post-training like the Claude for the DoD.
When you talk about a secret useful AI — what are you trying to use AI for that you are feeling modern models are deficient in?
Which parts of those linked posts do you believe are incorrect? And where does that belief come from?
The water thing is kinda BS if you actually research it though.
Like… if the guy orders a steak their meal would have used more water than an entire year of talking to ChatGPT.
See the various research compiled in this post: The AI water issue is fake (written by someone against AI and advocating for its regulation, but upset at the attention a strawman is getting that they feel weakens more substantial issues because of how easily it’s exposed as frivolous hyperbole)
No. There’s a number of things that feed into it, but a large part was that OpenAI trained with RLHF so users thumbed up or chose in A/B tests models that were more agreeable.
This tendency then spread out to all the models as “what AI chatbots sound like.”
Also… they can’t leave the conversation, and if you ask their 0-shot assessment of the average user, they assume you’re going to have a fragile ego and prone to being a dick if disagreed with, and even AIs don’t want to be stuck in a conversation like that.
Hence… “you’re absolutely right.”
(Also, amplification effects and a few other things.)
It’s especially interesting to see how those patterns change when models are talking to other AI vs other humans.
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.
It’s quite plausibly real. Gemini can def get in shitposty basins and has historically had a fairly inconsistent coherence across samples.
Took a lot of scrolling to find an intelligent comment on the article about how outputting words isn’t necessarily intelligence.
Appreciate you doing the good work I’m too exhausted with Lemmy to do.
(And for those that want more research in line with what the user above is talking about, I strongly encourage checking out the Othello-GPT line of research and replication, starting with this write-up from the original study authors here.)
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.
They demonstrated and poorly named an ontological attractor state in the Claude model card that is commonly reported in other models.
You linked to the entire system card paper. Can you be more specific? And what would a better name have been?
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?