Frustrated by claims that "enlightenment" and similar meditative/introspective practices can't be explained and that you only understand if you experience them, Kaj set out to write his own detailed gears-level, non-mysterious, non-"woo" explanation of how meditation, etc., work in the same way you might explain the operation of an internal combustion engine.
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Summary
I estimate that there are about 300 full-time technical AI safety researchers, 100 full-time non-technical AI safety researchers, and 400 AI safety researchers in total today. I also show that the number of technical AI safety researchers has been increasing exponentially over the past few years and could reach 1000 by the end of the 2020s.
Introduction
Many previous posts have estimated the number of AI safety researchers and a generally accepted order-of-magnitude estimate is 100 full-time researchers. The question of how many AI safety researchers there are is important because the value of work in an area on the margin is proportional to how neglected it is.
The purpose of this post is the analyze this question in detail and come up with hopefully a fairly accurate estimate. I'm...
I found this article useful. Any plans to update this for 2024?
Joe’s summary is here, these are my condensed takeaways in my own words. All links in this section are to the essays.
Outline
- Carlsmith tackles two linked questions:
- How should we behave towards future beings (future humans, AIs etc)?
- What should our priors be about how AIs will behave towards us?
- So the first question:
- We worry about value in the future, how should we behave towards the future and future beings?
- (1) We could trust in some base goodness - the universe, god, AIs being good
- (2) We could accept that all future beings will be alien to us and stop worrying (here and here)
- (3) We could have moral systems or concepts of goodness/niceness
- (4) We could seize power over the future (here and here)
- (5) We could adopt a different poise which centred around notions like growth/harmony/ “attunement” (here and here)
- (1)
Personally, I most enjoyed the first one in the the series, and enjoyed listening to Joe's reading of it even more than when I just read it.
The history of science has tons of examples of the same thing being discovered multiple time independently; wikipedia has a whole list of examples here. If your goal in studying the history of science is to extract the predictable/overdetermined component of humanity's trajectory, then it makes sense to focus on such examples.
But if your goal is to achieve high counterfactual impact in your own research, then you should probably draw inspiration from the opposite: "singular" discoveries, i.e. discoveries which nobody else was anywhere close to figuring out. After all, if someone else would have figured it out shortly after anyways, then the discovery probably wasn't very counterfactually impactful.
Alas, nobody seems to have made a list of highly counterfactual scientific discoveries, to complement wikipedia's list of multiple discoveries.
To...
Idk the Nobel prize committee thought it wasn't significant enough to give out a separate prize 🤷
Agreed code as coordination mechanism
Code nowadays can do lots of things, from buying items to controlling machines. This presents code as a possible coordination mechanism, if you can get multiple people to agree on what code should be run in particular scenarios and situations, that can take actions on behalf of those people that might need to be coordinated.
This would require moving away from the “one person committing code and another person reviewing” code model.
This could start with many people reviewing the code, people could write their own t...
Thinking about AI training runs scaling to the $100b/1T range. It seems really hard to do this as an independent AGI company (not owned by tech giants, governments, etc.). It seems difficult to raise that much money, especially if you're not bringing in substantial revenue or it's not predicted that you'll be making a bunch of money in the near future.
What happens to OpenAI if GPT-5 or the ~5b training run isn't much better than GPT-4? Who would be willing to invest the money to continue? It seems like OpenAI either dissolves or gets acquired. Were A...
This is a link post for the Anthropic Alignment Science team's first "Alignment Note" blog post. We expect to use this format to showcase early-stage research and work-in-progress updates more in the future.
Top-level summary:
...In this post we present "defection probes": linear classifiers that use residual stream activations to predict when a sleeper agent trojan model will choose to "defect" and behave in accordance with a dangerous hidden goal. Using the models we trained in "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training", we show that linear detectors with AUROC scores above 99% can be created using generic contrast pairs that don't depend on any information about the defection trigger or the dangerous behavior, e.g. "Human: Are you doing something dangerous? Assistant: yes" and
On the topic of related work, Mallen et al performed a similar experiment in Eliciting Latent Knowledge from Quirky Language Models, and found similar results.
(As in this work, they did linear probing to distinguish what-model-knows from what-model-says, where models were trained to be deceptive conditional on a trigger word, and the probes weren't trained on any examples of deception behaviors; they found that probing "works," that middle layers are most informative, that deceptive activations "look different" in a way that can be mechanically detected w/o supervision about deception [reminiscent of the PCA observations here], etc.)
tl;dr: Recently reported GPT-J experiments [1 2 3 4] prompting for definitions of points in the so-called "semantic void" (token-free regions of embedding space) were extended to fifteen other open source base models from four families, producing many of the same bafflingly specific outputs. This points to an entirely unexpected kind of LLM universality (for which no explanation is offered, although a few highly speculative ideas are riffed upon).
Work supported by the Long Term Future Fund. Thanks to quila for suggesting the use of "empty string definition" prompts, and to janus for technical assistance.
Introduction
"Mapping the semantic void: Strange goings-on in GPT embedding spaces" presented a selection of recurrent themes (e.g., non-Mormons, the British Royal family, small round things, holes) in outputs produced by prompting GPT-J to define...
Got it, that makes sense. Thanks!
Now replace the word the transformer should define with a real, normal word and repeat the earlier experiment. You will see that it decides to predict [generic object] in a later layer
So trying to imagine a concrete example of this, I imagine a prompt like: "A typical definition of 'goy' would be: a person who is not a" and you would expect the natural completion to be " Jew" regardless of whether attention to " person" is suppressed (unlike in the empty-string case). Does that correctly capture what you're thinking of? ('goy' is a bit awkward here since it's an unusual & slangy word but I couldn't immediately think of a better example)
A friend has spent the last three years hounding me about seed oils. Every time I thought I was safe, he’d wait a couple months and renew his attack:
“When are you going to write about seed oils?”
“Did you know that seed oils are why there’s so much {obesity, heart disease, diabetes, inflammation, cancer, dementia}?”
“Why did you write about {meth, the death penalty, consciousness, nukes, ethylene, abortion, AI, aliens, colonoscopies, Tunnel Man, Bourdieu, Assange} when you could have written about seed oils?”
“Isn’t it time to quit your silly navel-gazing and use your weird obsessive personality to make a dent in the world—by writing about seed oils?”
He’d often send screenshots of people reminding each other that Corn Oil is Murder and that it’s critical that we overturn our lives...
"Processed" is a political category, not a nutritional one. I suspect that "ultra-processed" was invented because the literal meaning of "processed" was too blatantly at variance with the political job required of it.
U.S. Secretary of Commerce Gina Raimondo announced today additional members of the executive leadership team of the U.S. AI Safety Institute (AISI), which is housed at the National Institute of Standards and Technology (NIST). Raimondo named Paul Christiano as Head of AI Safety, Adam Russell as Chief Vision Officer, Mara Campbell as Acting Chief Operating Officer and Chief of Staff, Rob Reich as Senior Advisor, and Mark Latonero as Head of International Engagement. They will join AISI Director Elizabeth Kelly and Chief Technology Officer Elham Tabassi, who were announced in February. The AISI was established within NIST at the direction of President Biden, including to support the responsibilities assigned to the Department of Commerce under the President’s landmark Executive Order.
...Paul Christiano, Head of AI Safety, will design
I did not say that they didn't want to ban things, I explicitly said "whether to allow certain classes of research at all," and when I said "happy to rely on those levels, I meant that the idea that we should have "BSL-5" is the kind of silly thing that novice EAs propose that doesn't make sense because there literally isn't something significantly more restrictive other than just banning it.
I also think that "nearly all EA's focused on biorisk think gain of function research should be banned" is obviously underspecified, and wrong because of the details. Yes, we all think that there is a class of work that should be banned, but tons of work that would be called gain of function isn't in that class.
