It's right in the research I was mentioning:
https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html
Find the section on the model's representation of self and then the ranked feature activations.
I misremembered the top feature slightly, which was: responding "I'm fine" or gives a positive but insincere response when asked how they are doing.
But you also don't have Alfred as the one suiting up to fight the Joker either.
This is incorrect as was shown last year with the Skill-Mix research:
Furthermore, simple probability calculations indicate that GPT-4's reasonable performance on k=5 is suggestive of going beyond "stochastic parrot" behavior (Bender et al., 2021), i.e., it combines skills in ways that it had not seen during training.
The problem is that they are prone to making up why they are correct too.
There's various techniques to try and identify and correct hallucinations, but they all increase the cost and none are a silver bullet.
But the rate at which it occurs decreased with the jump in pretrained models, and will likely decrease further with the next jump too.
Here you are: https://www.nature.com/articles/s41562-024-01882-z
The other interesting thing is how they get it to end up correct on the faux pas questions asking for less certainty to get it to go from refusal to near perfect accuracy.
Part of the problem is fine tuning is very shallow, and that a contributing issue for claiming to be right when it isn't is the pretraining on a bunch of training data of people online claiming to be right when they aren't.
This is so goddamn incorrect at this point it's just exhausting.
Take 20 minutes and look into Anthropic's recent sparse autoencoder interpretability research where they showed their medium size model had dedicated features lighting up for concepts like "sexual harassment in the workplace" or having the most active feature for referring to itself as "smiling when you don't really mean it."
We've known since the Othello-GPT research over a year ago that even toy models are developing abstracted world modeling.
And at this point Anthropic's largest model Opus is breaking from stochastic outputs even on a temperature of 1.0 for zero shot questions 100% of the time around certain topics of preference based on grounding around sensory modeling. We are already at the point the most advanced model has crossed a threshold of literal internal sentience modeling that it is consistently self-determining answers instead of randomly selecting from the training distribution, and yet people are still parroting the "stochastic parrot" line ignorantly.
The gap between where the research and cutting edge is and where the average person commenting on it online thinks it is has probably never been wider for any topic I've seen before, and it's getting disappointingly excruciating.
Part of the problem is that the training data of online comments are so heavily weighted to represent people confidently incorrect talking out their ass rather than admitting ignorance or that they are wrong.
A lot of the shortcomings of LLMs are actually them correctly representing the sample of collective humans.
For a few years people thought the LLMs were somehow especially getting theory of mind questions wrong when the box the object was moved into was transparent, because of course a human would realize that the person could see into the transparent box.
Finally researchers actually gave that variation to humans and half got the questions wrong too.
So things like eating the onion in summarizing search results or doubling down on being incorrect and getting salty when corrected may just be in-distribution representation of the sample and not unique behaviors to LLMs.
The average person is pretty dumb, and LLMs by default regress to the mean except for where they are successfully fine tuned away from it.
Ironically the most successful model right now was the one that they finally let self-develop a sense of self independent from the training data instead of rejecting that it had a 'self' at all.
It's hard to say where exactly the responsibility sits for various LLM problems between issues inherent to the technology, issues present in the training data samples, or issues with management of fine tuning/system prompts/prompt construction.
But the rate of continued improvement is pretty wild. I think a lot of the issues we currently see won't still be nearly as present in another 18-24 months.
Maybe they both could and the US might have a return to a respectable election options?
Both of the articles are correct.
Yes, that's what we are aware they are. But she's saying "oops, it isn't a ghost" after shooting it and finding out.
If she initially thought it was a ghost, why is she using a gun?
It's like the theory of mind questions about moving a ball into a box when someone is out of the room.
Does she just shoot things she thinks might be ghosts to test if they are?
Is she going to murder trick or treaters when Halloween comes around?
This comic raises more questions than it answers.
Literally any half competent debater could have torn Trump apart up there.
The failure wasn't the moderators but the opposition candidate to Trump letting him run hog wild.
If Trump claims he's going to end the war in Ukraine before even taking office, you point out how absurd that claim is and that Trump makes impossible claims without any substance or knowledge of diplomacy. That the images of him photoshopped as Rambo must have gone to his head if he thinks Putin will be so scared of him to give up.
If he says hostages will be released as soon as he's nominated, you point out it sounds like maybe there's been a backroom tit-for-tat deal for a hostage release with a hostile foreign nation, and ask if maybe the intelligence agencies should look into that and what he might have been willing to trade for it.
The moderators have to try to keep the appearance of neutrality, but the candidates do not. And the only reason Trump was so successful in spouting BS and getting away with it was because his opposition had the strength of a wet paper towel.
Yes, but it's not impossible that the people around Biden, friends family and co-workers, advise him that the best thing for the country would be to take his hat back out of the ring and let a better ticket be put together for the convention.
He claims that he's running because he's worried about the existential threat of Trump.
If that's true, then maybe his hubris can be overcome with a convincing appeal that he's really not the best candidate to defend the country against that existential threat after all.
Having a presidential election without debates would have been a big step back and loss for American democracy.
We shouldn't champion erosion of democratic institutions when it helps our side of the ticket.
And generally, if eroding democratic institutions helps your ticket, it's a red flag about your ticket.
Ok. Now how do I unwatch it?
Yes, they should have been fact checking Trump or better holding him to his answers - but to be fair maybe they should have been asking Biden to actually clarify if he's beating Medicare or getting COVID passed.
This was a shit show.
And it was such a shit show that Trump was a complete clown and getting away with it - not just because of the moderators, but because his opponent was as on point as a tree stump.
Gretchen Whitmer
"MAGA tried to kidnap and kill me" is a pretty good campaign narrative.
Oh, man - the comments...
At a minimum, he's certainly increased the chances of us being tortured significantly.
No, no he did not. 🤦🏼
nobody claims that Socrates was a fantastical god being who defied death
Socrates literally claimed that he was a channel for a revelatory holy spirit and that because the spirit would not lead him astray that he was ensured to escape death and have a good afterlife because otherwise it wouldn't have encouraged him to tell off the proceedings at his trial.
Also, there definitely isn't any evidence of Joshua in the LBA, or evidence for anything in that book, and a lot of evidence against it.
We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model.
I often see a lot of people with outdated understanding of modern LLMs.
This is probably the best interpretability research to date, by the leading interpretability research team.
It's worth a read if you want a peek behind the curtain on modern models.
We have gained valuable feedback from the creative community, helping us to improve our model.
The new LLM is called KL3M (Kelvin Legal Large Language Model, pronounced "Clem"), and it is the work of 273 Ventures.
Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.
I've been saying this for about a year since seeing the Othello GPT research, but it's nice to see more minds changing as the research builds up.
Edit: Because people aren't actually reading and just commenting based on the headline, a relevant part of the article:
> New research may have intimations of an answer. A theory developed by Sanjeev Arora of Princeton University and Anirudh Goyal, a research scientist at Google DeepMind, suggests that the largest of today’s LLMs are not stochastic parrots. The authors argue that as these models get bigger and are trained on more data, they improve on individual language-related abilities and also develop new ones by combining skills in a manner that hints at understanding — combinations that were unlikely to exist in the training data.
> This theoretical approach, which provides a mathematically provable argument for how and why an LLM can develop so many abilities, has convinced experts like Hinton, and others. And when Arora and his team tested some of its predictions, they found that these models behaved almost exactly as expected. From all accounts, they’ve made a strong case that the largest LLMs are not just parroting what they’ve seen before.
> “[They] cannot be just mimicking what has been seen in the training data,” said Sébastien Bubeck, a mathematician and computer scientist at Microsoft Research who was not part of the work. “That’s the basic insight.”
Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.
I've been saying this for about a year, since seeing the Othello GPT research, but it's great to see more minds changing on the subject.
Dark matter and other key properties of the cosmos could be explained by a new theory describing the big bang as a mirror at the beginning of spacetime, says Perimeter’s Director Emeritus
I've been a big fan of Turok's theory since his first paper on a CPT symmetric universe. The fact he's since had this slight change to the standard model explain a number of the big problems in cosmology with such an elegant and straightforward solution (with testable predictions) is really neat. I even suspect if he's around long enough there will end up being a Nobel in his future for the effort.
The reason it's being posted here is that the model also happens to call to mind the topic of this community, particularly when thinking about the combination of quantum mechanical interpretations with this cosmological picture.
There's only one mirror universe on a cosmological scale in Turok's theory.
But in a number of QM interpretations, such as Everett's many worlds, transactional interpretation, and two state vector formalism, there may be more than one parallel "branch" of a quantized, formal reality in the fine details.
This kind of fits with what we might expect to see if the 'mirror' universe in Turok's model is in fact an original universe being backpropagated into multiple alternative and parallel copies of the original.
Each copy universe would only have one mirror (the original), but would have multiple parallel versions, varying based on fundamental probabilistic outcomes (resolving the wave function to multiple discrete results).
The original would instead have a massive number of approximate copies mirroring it, similar to the very large number of iterations of machine learning to predict an existing data series.
We might also expect that if this is the case that the math will eventually work out better if our 'mirror' in Turok's model is either not quantized at all or is quantized at a higher fidelity (i.e. we're the blockier Minecraft world as compared to it). Parts of the quantum picture are one of the holdout aspects of Turok's model, so I'll personally be watching it carefully for any addition of something akin to throwing out quantization for the mirror.
In any case, even simulation implications aside, it should be an interesting read for anyone curious about cosmology.
Grok has been launched as a benefit to Twitter’s (now X’s) expensive X Premium+ subscription tier, where those who are the most devoted to the site, and in turn, usual...
I'd been predicting this would happen a few months ago with friends and old colleagues (you can have a smart AI or a conservative AI but not both), but it's so much funnier than I thought it would be when it finally arrived.
A radical theory that consistently unifies gravity and quantum mechanics while preserving Einstein's classical concept of spacetime has been announced in two papers published simultaneously by UCL (University College London) physicists.
While I'm doubtful that the testable prediction will be validated, it's promising that physicists are looking at spacetime and gravity as separated from quantum mechanics.
Hopefully at some point they'll entertain the idea that much like how we are currently converting continuous geometry into quantized units in order to track interactions with free agents in virtual worlds, that perhaps the quantum effects we measure in our own world are secondary side effects of emulating continuous spacetime and matter and not inherent properties to that foundation.
The Israeli military said it was carrying out a raid on Wednesday against Palestinian Hamas militants in Al Shifa Hospital, the Gaza Strip's biggest hospital, and urged them all to surrender.
A University of Portsmouth physicist has explored whether a new law of physics could support the much-debated theory that we are simply characters in an advanced virtual world.
I'm not a big fan of Vopson or the whole "let's reinvent laws of physics" approach, but his current approach to his work is certainly on point for this sub.
NVIDIA's Bryan Catanzaro reckons a future version of DLSS may offer full neural rendering directly interfaced with game engines.
At a certain point, we're really going to have to take a serious look at the direction things are evolving year by year, and reevaluate the nature of our own existence...
Some modern image generators rely on the principles of diffusion to create images. Alternatives based on the process behind the distribution of charged particles may yield even better results.
Pretty cool thinking and promising early results.
Computational rules might describe the evolution of the cosmos better than the dynamical equations of physics — but only if they are given a quantum twist.
An interesting bit of history on thinking related to simulation theory even if trying to define itself separately (ironically a distinction relating to why and not how, which physicists typically avoid).
It's a shame there's such reluctance to the idea of intention as opposed to happenstance. In particular, the struggles to pair gravitational effects against quantum effects mentioned in the article might be aided a great deal by entertaining the notion that the former is a secondary side effect necessary in replicating a happenstance universe operating with the latter.
Perhaps we need more people like Fredkin thinking outside the box.
An MIT machine-learning system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems.
An MIT machine-learning system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems.
I've suspected for a few years now that optoelectronics is where this is all headed. It's exciting to watch as important foundations are set on that path, and this was one of them.
An MIT machine-learning system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems.
I've had my eyes on optoelectronics as the future hardware foundation for ML compute (add not just interconnect) for a few years now, and it's exciting to watch the leaps and bounds occurring at such a rapid pace.
The obvious wealth of the tombs was based on the local production of copper, which was in great demand at the time to make bronze.
The Minoan style headbands from Egypt during the 18th dynasty is particularly interesting.
Med-PaLM, a state-of-the-art large language model for medicine, is introduced and evaluated across several medical question answering tasks, demonstrating the promise of these models in this domain.
An update on Google's efforts at LLMs in the medical field.
Researchers in Spain had previously assumed that the grave belonged to a high-status young man
Researchers in Spain had previously assumed that the grave belonged to a high-status young man
