wassname

Karma: 246

wassname 8 Nov 2024 11:11 UTC
1 point
0
in reply to: wassname’s comment on: Model Organisms of Misalignment: The Case for a New Pillar of Alignment Research
To the people disagreeing, what part do you disagree with? My main point, or my example? Or something else

wassname 4 Nov 2024 8:09 UTC
3 points
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in reply to: MondSemmel’s comment on: johnswentworth’s Shortform
I think this is especially important for me/us to remember. On this site we often have a complex way of thinking, and a high computational budget (because we like exercising our brains to failure) and if we speak freely to the average person, they mat be annoyed at how hard it is to parse what we are saying.

We’ve all probably had this experience when genuinely trying to understand someone from a very different background. Perhaps they are trying to describe their inner experience when mediating, or Japanese poetry, or are simply from a different’t discipline. Or perhaps we were just very tired that day, meaning we had a low computational budget.

On the other hand, we are often a “tell” culture, which had a lower computational load compared to ask or guess culture. As long as we don’t tell too much.

wassname 4 Nov 2024 5:00 UTC
1 point
−2
on: Model Organisms of Misalignment: The Case for a New Pillar of Alignment Research
I would add:
- Must also generalise better than capabilities!
  - out of distribution
  - to smarter models
Currently, we do not know how to make sure machine learning generalises well out of sample. This is an open problem that is critical to alignment. I find that it’s left out of evals frustratingly often, probably because it’s hard, and most methods miserably fail to generalise OOD.

For example, you don’t want your ASI to become unaligned, have value drift, or extrapolate human values poorly when, for example, 1) it meets aliens, 2) 1000 years pass, or cultural drift happens. What if your descendants think it’s admirable and funny to take hostages as a form of artistic practical joke, you would hope that your AI’s would handle that in a principled and adaptable manner. At the very least, you want its capability to fail before its morality.
- An overlooked benchmark of text OOD: GENIES
- mention of how OOD is an open problem in “Towards out of distribution generalization for problems in mechanics”

wassname 28 Oct 2024 22:22 UTC
3 points
0
in reply to: johnswentworth’s comment on: johnswentworth’s Shortform
One blind spot we rationalists sometimes have is that charismatic people actually treat the game as:

“Can I think of an association that will make the other person feel good and/or further my goal?”. You need people to feel good, or they won’t participate. And if you want some complicated/favour/uncomftorble_truth then you better mix in some good feels to balance it out and keep the other person participating.

To put it another way: If you hurt people’s brain or ego, rush them, or make them feel unsure, or contradict them, then most untrained humans will feel a little bad. Why would they want to keep feeling bad? Do you like it when people don’t listen, contradict you, insult you, rush you, disagree with you? Probably not, probobly no one does.

But if someone listens to you, smiles at you, likes you, has a good opinion of you, agrees with you, make sense to you. Then it feels good!

This might sound dangerously sycophantic, and that’s because it is—if people overdo it! But if it’s mixed with some healthy understanding, learning, informing then It’s a great conversational lubricant, and you should apply as needed. It just ensures that everyone enjoys themselves and comes back for more, counteracting the normal frictions of socialising.

There are books about this. “How to Win Friends and Influence People” recommends talking about the other person’s interests (including themselves) and listening to them, which they will enjoy.

So I’d say, don’t just free associate. Make sure it’s fun for both parties, make room to listen to the other person, and to let them steer. (And ideally your conversational partner reciprocates, but that is not guaranteed).

wassname 16 Oct 2024 11:02 UTC
5 points
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on: wassname’s Shortform
Is machine learning in a period of multiple discovery?

Anecdotally, it feels as though we have entered a period of multiple discovery in machine learning, with numerous individuals coming up with very similar ideas.

Logically, this can be expected when more people pursue the same low-hanging fruit. Imagine orchards in full bloom with a crowd of hungry gatherers. Initially, everyone targets the nearest fruit. Exploring a new scientific frontier can feel somewhat similar. When reading the history books on the Enlightenment, I get a similar impression.

If we are indeed in a period of multiple discovery, we should not simply go after the nearest prize; it will soon be claimed. Your time is better spent looking further afield or exploring broader horizons.

Is any of this backed by empirical evidence? No! I have simply plotted Wikipedia’s list of multiple discoveries. It shows multiple discoveries increasing with population, I don’t see any distinct periods, so it’s inconclusive.

wassname 12 Oct 2024 5:19 UTC
0 points
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on: Overview of strong human intelligence amplification methods
I made up the made-up numbers in this table of made-up numbers; therefore, the numbers in this table of made-up numbers are made-up numbe
These hallucinated outputs are really getting out of hand

wassname 12 Oct 2024 0:13 UTC
1 point
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in reply to: Kaninchen’s comment on: What are your contrarian views?
Some reports of people who have tried it https://old.reddit.com/r/financialindependence/comments/a9h20a/has_anyone_fired_to_a_boat_full_time_how_did_it/

wassname 11 Oct 2024 6:03 UTC
1 point
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in reply to: wassname’s comment on: wassname’s Shortform
In particular, I’d be keen to know what @Stag and @technicalities think, as this was in large part inspired by the desire to further simplify and categorise the “one sentence summaries” from their excellent Shallow review of live agendas in alignment & safety

wassname 11 Oct 2024 5:31 UTC
2 points
−1
in reply to: wassname’s comment on: wassname’s Shortform
If anyone finds this useful, please let me know. I’ve abandoned it because none of my test audience found it interesting or useful. That’s OK, it just means it’s better to focus on other things.

wassname 11 Oct 2024 5:31 UTC
5 points
0
on: wassname’s Shortform
Draft: A cartoonishly simplified overview of some technical AI alignment efforts

Epistemic status: excessive lossy compression applied

How are people actually trying to make friendly AI? Here are few simplified examples

LessWrong has some great technical and critical overviews of alignment agendas, but for many readers they take too long to read.

Here’s my attempt at cartoonishly simplified explanations of technical alignment efforts:
- Let’s make AI solve it
  - Just make a friendly dumb AI and that will make a smarter friendly AI, and so on—Iterated Amplification
  - Just make AI’s debate each other, so that the truth comes out when we look at both sides of the argument
  - Just have the AI follow a constitution
  - Just make a friendly automatic alignment researcher—Superalignment
- Let’s have lots of AI’s interacting:
  - Just have multiple focused AIs competing in various roles, kind of like a corporation—Drexler’s The Open Agency Model
  - Just use dumb AI’s to build an understandable world simulation, then train a smart AI in that simulation so that we can verify that it’s aligned—DavidAD’s plan
  - Just have the AI learn and respect other beings’ boundaries—Boundaries/Membranes
- Let’s make sure the goal is good
  - Just design the AI to want to help humans but be maximally uncertain about how to do that, so it constantly seeks guidance—CIRL
  - Just make a lazy AI that is satisfied after doing “enough”—Mild Optimization
- Let’s build tools that will let us control smarter AI’s
  - Just read their minds—ELK
  - Just edit their minds—Activation Steering
- Let’s understand more
  - What it means to be an agent—agent foundations
Some historic proposals sounded promising but seem to have been abandoned fow now, I include this to show how hard the problem is:
- Just keep the AI in a sandbox environment where it can’t cause any real-world harm while we continue to align and train it—AI Boxing
- Just make an oracle AI that only talks, not acts
- simulate a human thinking for a very long time about the alignment problem, and use whatever solution they write down—TODO Another proposal by Christiano (2012):
- Do what we would wish for if we knew more, thought faster, were more the people we wished we were—coherent extrapolated volition (CEV)
I’ve left out the many debates over the proposals. I’m afraid that you need to dig much deeper to judge which methods will work. If you want to know more, just follow the links below.

If you dislike this: please help me make it better by contributing better summaries, and I’ll be pleased to include them.

If you would like to know more, I recommend these overviews:
- 2023 - Shallow review of live agendas in alignment & safety—I’ve taken heavily from this post which has one sentence summaries as well as much, much more
- 2022 A newcomer’s guide to the technical AI safety field
- 2023 - A Brief Overview of AI Safety/Alignment Orgs, Fields, Researchers, and Resources for ML Researchers
- 2023 - The Genie in the Bottle: An Introduction to AI Alignment and Risk
- 2022 - On how various plans miss the hard bits of the alignment challenge
- 2022 - (My understanding of) What Everyone in Technical Alignment is Doing and Why

wassname 11 Oct 2024 5:22 UTC
3 points
0
on: wassname’s Shortform
If we regularise human values, what would happen?

We want AI to take up human values, in a way that generalizes out of distribution better than our capabilities. In other words, if our AI meets friendly aliens, we want its gun skills to fail before its morals.

Human values—as expressed by actions—seem messy, inconsistent, and hypocritical. If we forced an AI to regularise them, would it be wiser, more consistent, and simply good? Or would it oversimplify and make a brittle set of values.

I would predict that a simplified and more consistent form of human values would extrapolate better to a new situation. But if they are too simplified, they will be brittle and fail to generalize or perform how we want. I expect there to be quite a lot of simplification that can be performed initially, and I expect it to be interesting and helpful.

Perhaps we could even remove the parts of human values that are to an individual’s advantage. Removing self-preservation, self advantage, then ensuring consistency would lead to an interesting set of values.

It would be an interesting experimental direction. Perhaps an early experiment could make LLM answers conditional on a latent space which encodes values. It would be much like the image generators that are conditional on style. As a bonus, you would have this nice regular space of values where you can examine and play with.

wassname 30 Sep 2024 22:27 UTC
1 point
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in reply to: Connor Kissane’s comment on: Base LLMs refuse too
Interesting, that changes my mind somewhat.

I wonder why this happens?! I can’t find any examples of this in the pile, and their filtering doesn’t seem to add it. It’s hard to imagine humans generating this particular refusal response. Perhaps it’s a result of filtering, s

wassname 29 Sep 2024 23:51 UTC
LW: 6 AF: 3
2
AF
on: Base LLMs refuse too
Are these really pure base models? I’ve also noticed this kind of behaviour in so-called base models. My conclusion is that they are not base models in the sense that they have been trained to predict the next word on the internet, but they have undergone some safety fine-tuning before release. We don’t actually know how they were trained, and I am suspicious. It might be best to test on Pythia or some model where we actually know.

wassname 22 Aug 2024 8:19 UTC
3 points
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in reply to: gwern’s comment on: gwern’s Shortform
I wonder where the best places to write are. I’d say Reddit and GitHub are good bets, but you would have to get through their filtering, for karma, stars, language, subreddit etc.

wassname 25 Jul 2024 0:42 UTC
1 point
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on: Using an LLM perplexity filter to detect weight exfiltration
CMIIW, you are looking at information content according to the LLM, but that’s not enough. It has to be learnable information content to avoid the noisy TV problem. E.g. a random sequence of tokens will be unpredictable and high perplexity. But if it’s learnable, then it has potential.

I had a go at a few different approaches here https://github.com/wassname/detect_bs_text

wassname 28 Jun 2024 21:57 UTC
7 points
0
on: I’m a bit skeptical of AlphaFold 3
But if it’s a poor split, wouldn’t it also favour the baseline (your software). But they did beat the baseline. So if your concern is correct, they did outperform the baseline, but just didn’t realistically measure generalisation to radically different structures.

So it’s not fair to say ‘it’s only memorisation’. It seems fairer to say ‘it doesn’t generalise enough to be docking software, and this is not obvious at first due to a poor choice of train test split’.

wassname 15 Jun 2024 6:28 UTC
7 points
2
in reply to: kromem’s comment on: OpenAI appoints Retired U.S. Army General Paul M. Nakasone to Board of Directors
I thought this too, until someone in finance told me to google “Theranos Board of Directors”, so I did, and it looked a lot like OpenAI’s new board.

This provides an alternate hypothesis: That signals nothing substantial. Perhaps it’s empty credentialism, or empty PR, or a cheap attempt to win military contacts.

wassname 14 Jun 2024 0:35 UTC
18 points
15
on: OpenAI appoints Retired U.S. Army General Paul M. Nakasone to Board of Directors
It could indicate the importance of security, which is safe. Or of escalation in a military arms race, which is unsafe.

wassname 9 Jun 2024 6:34 UTC
1 point
0
in reply to: wassname’s comment on: ChatGPT can learn indirect control
Also DEIR needs to implicitly distinguish between things it caused, and things it didn’t https://arxiv.org/abs/2304.10770

wassname 9 Jun 2024 6:24 UTC
1 point
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in reply to: RHollerith’s comment on: wassname’s Shortform

Your “given a lab environment where we strongly expect an AI to be roughly human level” seems to assume the thing to be proven.

But if we are being Bayesians here, then it seems to become clearer. Let’s assume that the law says we can safely and effectively evaluate an AGI that’s truly 0-200% as smart as the smartest human alive (revisit this as our alignment tools improve). Now, as you say, in the real world there is dangerous uncertainty about this. So how much probability do we put on, say, GPT-5 exceeding that limit? That’s our risk, and it either meets or exceeds our risk budget. If there was a regulatory body overseeing that, you would need to submit the evidence to back this up, and they would hire smart people or AI to make the judgments (the FDA does this now, to mixed effects). The applicant can put in work to derisk the prospect.

With a brand-new architecture, you are right, we shouldn’t scale it up and test the big version; that would be too risky! We evaluate small versions first and use that to establish priors about how capabilities scale. This is generally how architecture improvements works, anyway, so it wouldn’t require extra work.

Does that make sense? To be, this seems like a more practical approach than once-and-done alignment, or pause everything. And most importantly of all, it seems to set up the incentives correctly—commercial success is dependent on convincing us you have narrower AI, better alignment tools, or have increased human intelligence.

If you have an approach I think is promising, I’ll drop everything and lobby funders to give you a big salary to work on your approach.

I wish I did, mate ;p Even better than a big salary, it would give me and my kids a bigger chance to survive.

wassname

Draft: A cartoonishly simplified overview of some technical AI alignment efforts