All of Jay Bailey's Comments + Replies

Significant Digits is (or was, a few years ago) considered the best one, to my recollection.

Here you go: https://chatgpt.com/share/67b31788-32b0-8013-8bbf-a4100abf0457

https://chatgpt.com/share/67b1c1dc-e88c-8013-a6f0-88b25155a0d6

Here you are :)

I bought a month of Deep Research and am open to running queries if people have a few but don't want to spend 200 bucks for them. Will spend up to 25 queries in total.

A paragraph or two of detail is good - you can send me supporting documents via wnlonvyrlpf@tznvy.pbz (ROT13) if you want. Offer is open publicly or via PM.

Having reflected on this decision more, I have decided I no longer endorse those feelings in point B of my second-to-last paragraph. In fact, I've decided that "I donated roughly 1k to a website that provided way more expected value than that to me over my lifetime, and also if it shut down I think that would be a major blow to one of the most important causes in the world" is something to be proud of, not embarrassed by, and something worthy of being occasionally reminded of.

So if you're still sending them out I'd gladly take one after all :)

I've been procrastinating on this, but I heard it was the last day to do this, so here I am. I've utilised LessWrong for years, but am also a notoriously cheap bastard. I'm working on this. That said, I feel I should pay something back, for what I've gotten out of it.

When I was 20 or so, I was rather directionless, and didn't know what I wanted to do in life, bouncing between ideas, never finishing them. I was reading LessWrong at the time. At some point, a LessWrong-ism popped into my head - "Jay - this thing you're doing isn't working. Your interests cha... (read more)

Hi Giorgi,

Not an expert on this, but I believe the idea is that over time the agent will learn to assign negligible probabilities to actions that don't do anything. For instance, imagine a game where the agent can move in four directions, but if there's a wall in front of it, moving forward does nothing. The agent will eventually learn to stop moving forward in this circumstance. So you could probably just make it work, even if it's a bit less efficient, if you just had the environment do nothing if an invalid action was selected.

Thanks for this! I've changed the sentence to:

The target network gets to see one more step than the Q-network does, and thus is a better predictor.

Hopefully this prevents others from the same confusion :)

pandas is a good library for this - it takes CSV files and turns them into Python objects you can manipulate. plotly / matplotlib lets you visualise data, which is also useful. GPT-4 / Claude could help you with this. I would recommend starting by getting a language model to help you create plots of the data according to relevant subsets. Like if you think that the season matters for how much gold is collected, give the model a couple of examples of the data format and simply ask it to write a script to plot gold per season.

I don't know about the first one - I think you'll have to analyse each job and decide about that. I suspect the answer to your second question is "Basically nil". I think that unless you are working on state-of-the-art advances in:

A) Frontier models B) Agent scaffolds, maybe.

You are not speeding up the knowledge required to automate people.

I guess my way of thinking of it is - you can automate tasks, jobs, or people.

Automating tasks seems probably good. You're able to remove busywork from people, but their job is comprised of many more things than that task, so people aren't at risk of losing their jobs. (Unless you only need 10 units of productivity, and each person is now producing 1.25 units so you end up with 8 people instead of 10 - but a lot of teams could also quite use 12.5 units of productivity well)

Automating jobs is...contentious. It's basically the tradeoff I talked about above.

A... (read more)

It seems to me that either:

• RLHF can't train a system to approximate human intuition on fuzzy categories. This includes glitches, and this plan doesn't work.

• RLHF can train a system to approximate human intuition on fuzzy categories. This means you don't need the glitch hunter, just apply RLHF to the system you want to train directly. All the glitch hunter does is make it cheaper.

I was about 50/50 on it being AI-made, but then when I saw the title "Thought That Faster" was a song, I became much more sure, because that was a post that happened only a couple weeks ago I believe, and if it was human-made I assume it would take longer to go from post to full song. Then I read this post.

In Soviet Russia, there used to be something called a Coke party. You saved up money for days to buy a single can of contraband Coca-Cola. You got all of your friends together and poured each of them a single shot. It tasted like freedom.

I know this isn't the point of the piece, but this got to me. However much I appreciate my existence, it never quite seems to be enough to be calibrated to things like this. I suddenly feel both a deep appreciation and vague guilt. Though it does give me a new gratitude exercise - imagine the item I am about to enjoy is forbidden in my country and I have acquired a small sample at great expense.

I notice that this is a standard pattern I use and had forgotten how non-obvious it is, since you do have to imagine yourself in someone else's perspective. If you're a man dating women on dating apps, you also have to imagine a very different perspective than your own - women tend to have many more options of significantly lower average quality. It's unlikely you'd imagine yourself giving up on a conversation because it required mild effort to continue, since you have less of them in the first place and invest more effort in each one.

The level above that ... (read more)

What I'm curious about is how you balance this with the art of examining your assumptions.

Puzzle games are a good way of examining how my own mind works, and I often find that I go through an algorithm like:

• Do I see the obvious answer?
• What are a few straightforward things I could try?

Then Step 3 I see as similar to your maze-solving method:

• What are the required steps to solve this? What elements constrain the search space?

But I often find that for difficult puzzles, a fourth step is required:

• What assumptions am I making, that would lead me to ov

Concrete feedback signals I've received:

• I don't find myself excited about the work. I've never been properly nerd-sniped by a mechanistic interpretability problem, and I find the day-to-day work to be more drudgery than exciting, even though the overall goal of the field seems like a good one.

• When left to do largely independent work, after doing the obvious first thing or two ("obvious" at the level of "These techniques are in Neel's demos") I find it hard to figure out what to do next, and hard to motivate myself to do more things if I do think of t

Anecdotally I have also noticed this - when I tell people what I do, the thing they are frequently surprised by is that we don't know how these things work.

As you implied, if you don't understand how NN's work, your natural closest analogue to ChatGPT is conventional software, which is at least understood by its programmers. This isn't even people being dumb about it, it's just a lack of knowledge about a specific piece of technology, and a lack of knowledge that there is something to know - that NN's are in fact qualitatively different from other programs.

Yes, this is an argument people have made. Longtermists tend to reject it. First off, applying a discount rate on the moral value of lives in order to account for the uncertainty of the future is...not a good idea. These two things are totally different, and shouldn't be conflated like that imo. If you want to apply a discount rate to account for the uncertainty of the future, just do that directly. So, for the rest of the post I'll assume a discount rate on moral value actually applies to moral value.

So, that leaves us with the moral argument.

A fairly goo... (read more)

For the Astra Fellowship, what considerations do you think people should be thinking about when deciding to apply for SERI MATS, Astra Fellowship, or both? Why would someone prefer one over the other, given they're both happening at similar times?

The agent's context includes the reward-to-go, state (i.e, an observation of the agent's view of the world) and action taken for nine timesteps. So, R1, S1, A1, .... R9, S9, A9. (Figure 2 explains this a bit more) If the agent hasn't made nine steps yet, some of the S's are blank. So S5 is the state at the fifth timestep. Why is this important?

If the agent has made four steps so far, S5 is the initial state, which lets it see the instruction. Four is the number of steps it takes to reach the corridor where the agent has to make the decision to go left or r... (read more)

I think there’s an aesthetic clash here somewhere. I have an intuition or like... an aesthetic impulse, telling me basically… “advocacy is dumb”. Whenever I see anybody Doing An Activism, they're usually… saying a bunch of... obviously false things? They're holding a sign with a slogan that's too simple to possibly be the truth, and yelling this obviously oversimplified thing as loudly as they possibly can? It feels like the archetype of overconfidence.

This is exactly the same thing that I have felt in the past. Extremely well said. It is worth pointing ou... (read more)

I find this interesting but confusing. Do you have an idea for what mechanism allowed this? E.g: Are you getting more done per hour now than your best hours working full-time? Did the full-time hours fall off fast at a certain point? Was there only 15 hours a week of useful work for you to do and the rest was mostly padding?

I think this makes a lot of sense. While I think you can make the case for "fertility crisis purely as a means of preventing economic slowdown and increasing innovation" I think your arguments are good that people don't actually often make this argument, and a lot of it does stem from "more people = good".

But I think if you start from "more people = good", you don't actually have motivated reasoning as much as you suspect re: innovation argument. I think it's more that the innovation argument actually does just work if you accept that more people = good. B... (read more)

Okay, I think I see several of the cruxes here.

Here's my understanding of your viewpoint:

"It's utterly bizarre to worry about fertility. Lack of fertility is not going to be an x-risk anytime soon. We already have too many people and if anything a voluntary population reduction is a good thing in the relative near-term. (i.e, a few decades or so) We've had explosive growth over the last century in terms of population, it's already unstable, why do we want to keep going?"

In a synchronous discussion I would now pause to see if I had your view right. Because ... (read more)

I would suggest responding with your points (Top 3-5, if you have too many to easily list) on why this is incredibly obviously not a problem, seeing where you get pushback if anywhere, and iterating from there. Don't be afraid to point out "incredibly obvious" things - it might not be incredibly obvious to other people. And if you're genuinely unsure why anyone could think this is a problem, the responses to your incredibly obvious points should give you a better idea.

I think Tristan is totally right, and it puts an intuition I've had into words. I'm not vegan - I am sympathetic to the idea of having this deep emotional dislike of eating animals, I feel like the version of me who has this is a better person, and I don't have it. From a utilitarian perspective I could easily justify just donating a few bucks to animal charities...but veganism isn't about being optimally utilitarian. I see it as more of a virtue ethics thing. It's not even so much that I want to be vegan, but I want to be the kind of person who chooses it... (read more)

One of the core problems of AI alignment is that we don't know how to reliably get goals into the AI - there are many possible goals that are sufficiently correlated with doing well on training data that the AI could wind up optimising for a whole bunch of different things.

Instrumental convergence claims that a wide variety of goals will lead to convergent subgoals such that the agent will end up wanting to seek power, acquire resources, avoid death, etc.

These claims do seem a bit...contradictory. If goals are really that inscrutable, why do we strongly ex... (read more)

I found an error in the application - when removing the last item from the blacklist, every page not whitelisted is claimed to be blacklisted. Adding an item back to the blacklist fixes this. Other than that, it looks good!

Interesting. That does give me an idea for a potentially useful experiment! We could finetune GPT-4 (or RLHF an open source LLM that isn't finetuned, if there's one capable enough and not a huge infra pain to get running, but this seems a lot harder) on a "helpful, harmless, honest" directive, but change the data so that one particular topic or area contains clearly false information. For instance, Canada is located in Asia.

Does the model then:

• Deeply internalise this new information? (I suspect not, but if it does, this would be a good sign for scalable

In current user-facing LLMs like ChatGPT or Claude, the closest approximation to goals may be being helpful, harmless, and honest.

According to my understanding of RLHF, the goal-approximation it trains for is "Write a prompt that is likely to be rated as positive". In ChatGPT / Claude, this is indeed highly correlated with being helpful, harmless, and honest, since the model's best strategy for getting high ratings is to be those things. If models are smarter than us, this may cease to be the case, as being maximally honest may begin to conflict with the r... (read more)

I don't really understand how your central point applies here. The idea of "money saves lives" is not supposed to be a general rule of society, but rather a local point about Alice and Bob - namely, donating ~5k will save a life. That doesn't need to be always true under all circumstances, there just needs to be some repeatable action that Alice and Bob can take (e.g, donating to the AMF) that costs 5k for them that reliably results in a life being saved. (Your point about prolonging life is true, but since the people dying of malaria are generally under 5... (read more)

Not quite, in my opinion. In practice, humans tend to be wrong in predictable ways (what we call a "bias") and so picking the best option isn't easy.

What we call "rationality" tends to be the techniques / thought patterns that make us more likely to pick the best option when comparing alternatives.

How about "AI-assisted post"? Shouldn't clash with anything else, and should be clear what it means on seeing the tag.

"Reward" in the context of reinforcement learning is the "goal" we're training the program to maximise, rather than a literal dopamine hit. For instance, AlphaGo's reward is winning games of Go. When it wins a game, it adjusts itself to do more of what won it the game, and the other way when it loses. It's less like the reward a human gets from eating ice-cream, and more like the feedback a coach might give you on your tennis swing that lets you adjust and make better shots. We have no reason to suspect there's any human analogue to feeling good.

I think intelligence is a lot easier than morality, here. There are agreed upon moral principles like not lying, not stealing, and not hurting others, sure...but even those aren't always stable across time. For instance, standard Western morality held that it was acceptable to hit your children a couple of generations ago, now standard Western morality says it's not. If an AI trained to be moral said that actually, hitting children in some circumstances is a worthwhile tradeoff, that could mean that the AI is more moral than we are and we overcorrected, or... (read more)

If NAMSI achieved a superhuman level of expertise in morality, how would we know? I consider our society to be morally superior to the one we had in 1960. People in 1960 would not agree with this assessment upon looking. If NAMSI agrees with us about everything, it's not superhuman. So how do we determine whether its possibly-superhuman morality is superior or inferior?

That may explain why these scenarios have never been all that appealing to me, because I do think about the future in these hypothetical scenarios. I ask myself "Okay, what would the plan be in five years, when the scavenged food has long since run out?" and that feels scary and overwhelming. (Admittedly, rollercoaster scary, since it's a fantasy, but I find myself spending just as much time asking how the hell I'd learn to recreate agriculture and how miserable day-to-day farming would be as I do imagining myself as a badass hero who saves someone from zombies - and that's assuming I survive at all, which is a pretty big if!)

""AI alignment" has the application, the agenda, less charitably the activism, right in the name."

This seems like a feature, not a bug. "AI alignment" is not a neutral idea. We're not just researching how these models behave or how minds might be built neutrally out of pure scientific curiosity. It has a specific purpose in mind - to align AI's. Why would we not want this agenda to be part of the name?

What are the best ones you've got?

I don't think this is a good metric. It is very plausible that porn is net bad, but living under the type of govermnment that would outlaw it is worse. In which case your best bet would be to support its legality but avoid it yourself.

I'm not saying that IS the case, but it certainly could be. I definitely think there are plenty of things that are net-negative to society but nowhere near bad enough to outlaw.

An AGI that can answer questions accurately, such as "What would this agentic AGI do in this situation" will, if powerful enough, learn what agency is by default since this is useful to predict such things. So you can't just train an AGI with little agency. You would need to do one of:

• Train the AGI with the capabilities of agency, and train it not to use them for anything other than answering questions.
• Train the AGI such that it did not develop agency despite being pushed by gradient descent to do so, and accept the loss in performance.

Both of these s... (read more)

Late response but I figure people will continue to read these posts over time: Wedding-cake multiplication is the way they teach multiplication in elementary school. i.e, to multiply 706 x 265, you do 706 x 5, then 706 x 60, then 706 x 200 and add all the results together. I imagine it is called that because the result is tiered like a wedding cake.

One of the easiest ways to automate this is to have some sort of setup where you are not allowed to let things grow past a certain threshold, a threshold which is immediately obvious and ideally has some physical or digital prevention mechanism attached.

Examples:

Set up a Chrome extension that doesn't let you have more than 10 tabs at a time. (I did this)

Have some number of drawers / closet space. If your clothes cannot fit into this space, you're not allowed to keep them. If you buy something new, something else has to come out.

I know this is two years later, but I just wanted to say thank you for this comment. It is clear, correct, and well-written, and if I had seen this comment when it was written, it could have saved me a lot of problems at the time.

I've now resolved this issue to my satisfaction, but once bitten twice shy, so I'll try to remember this if it happens again!

Sorry it took me a while to get to this.

Intuitively, as a human, you get MUCH better results on a thing X if your goal is to do thing X, rather than Thing X being applied as a condition for you to do what you actually want. For example, if your goal is to understand the importance of security mindset in order to avoid your company suffering security breaches, you will learn much more than being forced to go through mandatory security training. In the latter, you are probably putting in the bare minimum of effort to pass the course and go back to whatever y... (read more)

I think what the OP was saying was that in, say, 2013, there's no way we could have predicted the type of agent that LLM's are and that they would be the most powerful AI's available. So, nobody was saying "What if we get to the 2020s and it turns out all the powerful AI are LLM's?" back then. Therefore, that raises a question on the value of the alignment work done before then.

If we extend that to the future, we would expect most good alignment research to happen within a few years of AGI, when it becomes clear what type of agent we're going to get. Align... (read more)