Hi, I am a Physicist, an Effective Altruist and AI Safety student/researcher.
I timed how long it took me to fill in the survey. It took 30 min. I could probably have done it in 15 min if I skipped the optional text questions. This is to be expected however. Every time I've seen someone someone guesses how long it will take to respond to their survey, it's off by a factor of 2-5.
Current Interpretability results suggest that roughly the first half of the layers in an LLM correspond to understanding the context at increasingly abstract levels, and the second half to figuring out what to say and turning that back from abstractions into concrete tokens. It's further been observed that in the second half, figuring out what to say generally seems to occur in stages: first working out the baseline relevant facts, then figuring out how to appropriately slant/color those in the current context, then converting these into the correct language, and last getting the nitty-gritty details of tokenization right.
How do we know this? This claim seems plausible, but also I did not know that mech-interp was advanced enough to verify something like this. Where can I read more?
It looks like this to me:
Where's the colourful text?Is it broken or am I doing something wrong?
Potentially we might be ok with it if the expected timescale is long enough (or the probability of it happening in a given timescale is low enough).
Agreed. I'd love for someone to investigate the possibility of slowing down substrate-convergence enough to be basically solved.
If that's true then that is a super important finding! And also an important thing to communicate to people! I hear a lot of people who say the opposite and that we need lots of competing AIs.
Hm, to me this conclusion seem fairly obvious. I don't know how to communicate it though, since I don't know what the crux is. I'd be up for participating in a public debate about this, if you can find me an opponent. Although, not until after AISC research lead applications are over, and I got some time to recover. So maybe late November at the earliest.
An approach could be to say under what conditions natural selection will and will not sneak in.
Natural selection requires variation. Information theory tells us that all information is subject to noise and therefore variation across time. However, we can reduce error rates to arbitrarily low probabilities using coding schemes. Essentially this means that it is possible to propagate information across finite timescales with arbitrary precision. If there is no variation then there is no natural selection.
Yes! The big question to me is if we can reduced error rates enough. And "error rates" here is not just hardware signal error, but also randomness that comes from interacting with the environment.
In abstract terms, evolutionary dynamics require either a smooth adaptive landscape such that incremental changes drive organisms towards adaptive peaks and/or unlikely leaps away from local optima into attraction basins of other optima. In principle AI systems could exist that stay in safe local optima and/or have very low probabilities of jumps to unsafe attraction basins.
It has to be smooth relative to the jumps the jumps that can be achieved what ever is generating the variation. Natural mutation don't typically do large jumps. But if you have a smal change in motivation for an intelligent system, this may cause a large shift in behaviour.
I believe that natural selection requires a population of "agents" competing for resources. If we only had a single AI system then there is no competition and no immediate adaptive pressure.
I though so too to start with. I still don't know what is the right conclusion, but I think that substrate-needs convergence it at least still a risk even with a singleton. Something that is smart enough to be a general intelligence, is probably complex enough to have internal parts that operate semi independently, and therefore these parts can compete with each other.
I think the singleton scenario is the most interesting, since I think that if we have several competing AI's, then we are just super doomed.
And by singleton I don't necessarily mean a single entity. It could also be a single alliance. The boundaries between group and individual is might not be as clear with AIs as with humans.
Other dynamics will be at play which may drown out natural selection. There may be dynamics that occur at much faster timescales that this kind of natural selection, such that adaptive pressure towards resource accumulation cannot get a foothold.
This will probably be correct for a time. But will it be true forever? One of the possible end goals for Alignment research is to build the aligned super intelligence that saves us all. If substrate convergence is true, then this end goal is of the table. Because even if we reach this goal, it will inevitable start to either value drift towards self replication, or get eaten from the inside by parts that has mutated towards self replication (AI cancer), or something like that.
Other dynamics may be at play that can act against natural selection. We see existence-proofs of this in immune responses against tumours and cancers. Although these don't work perfectly in the biological world, perhaps an advanced AI could build a type of immune system that effectively prevents individual parts from undergoing runaway self-replication.
Cancer is an excellent analogy. Humans defeat it in a few ways that works together
Point 4 is very important. If there is only one agent, this agent needs perfect cancer fighting ability to avoid being eaten by natural selection. The big question to me is: Is this possible?If you on the other hand have several agents, they you defiantly don't escape natural selection, because these entities will compete with each other.
We don't know why the +2000 vector works but the +100 vector doesn't.
My guess is it's because in the +100 case the vectors are very similar, causing their difference to be something un-natural."I talk about weddings constantly " and "I do not talk about weddings constantly" are technically opposites. But if you imagine someone saying this, you notice that their neural language meaning is almost identical. What sort of person says "I do not talk about weddings constantly"? That sounds to me like someone who talks about weddings almost constantly. Why else would they feel the need to say that?
To steer a forward pass with the "wedding" vector, we start running an ordinary GPT-2-XL forward pass on the prompt "I love dogs" until layer 6. Right before layer 6 begins, we now add in the cached residual stream vectors from before:
I have a question about the image above this text.Why do you add the embedding from the [<endofotext> -> "The"] stream? This part has no information about wedding.
If you think it would be helpful, you are welcome to suggest a meta philpsophy topic for AI Safety Camp.
More info at aisafety.camp. (I'm typing on a phone, I'll add actuall link later if I remember too)
But I think orgs are more likely to be well-known to grant-makers on average given that they tend to have a higher research output,
I think your getting the causality backwards. You need money first, before there is an org. Unless you count informal multi people collaborations as orgs. I think people how are more well-known to grant-makers are more likely to start orgs. Where as people who are less known are more likely to get funding at all, if they aim for a smaller garant, i.e. as an independent researcher.
Counter point. After the FTX collapse, OpenPhil said publicly (some EA Forum post) that they where raising their bar for funding. I.e. there are things that would have been funded before that would now not be funded. The stated reason for this is that there are generally less money around, in total. To me this sounds like the thing you would do if money is the limitation. I don't know why OpenPhil don't spend more. Maybe they have long timelines and also don't expect any more big donors any time soon? And this is why they want to spend carefully?