Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.
This is a special post for short-form writing by TurnTrout. Only they can create top-level comments. Comments here also appear on the Shortform Page and All Posts page.
Rationality exercise: Take a set of Wikipedia articles on topics which trainees are somewhat familiar with, and then randomly select a small number of claims to negate (negating the immediate context as well, so that you can't just syntactically discover which claims were negated).
For example:
Sometimes, trainees will be given a totally unmodified article. For brevity, the articles can be trimmed of irrelevant sections.
Benefits:
- Addressing key rationality skills. Noticing confusion; being more confused by fiction than fact; actually checking claims against your models of the world.
- If you fail, either the article wasn't negated skillfully ("5 people died in 2021" -> "4 people died in 2021" is not the right kind of modification), you don't have good models of the domain, or you didn't pay enough attention
... (read more)For the last two years, typing for 5+ minutes hurt my wrists. I tried a lot of things: shots, physical therapy, trigger-point therapy, acupuncture, massage tools, wrist and elbow braces at night, exercises, stretches. Sometimes it got better. Sometimes it got worse.
No Beat Saber, no lifting weights, and every time I read a damn book I would start translating the punctuation into Dragon NaturallySpeaking syntax.
Have you ever tried dictating a math paper in LaTeX? Or dictating code? Telling your computer "click" and waiting a few seconds while resisting the temptation to just grab the mouse? Dictating your way through a computer science PhD?
And then.... and then, a month ago, I got fed up. What if it was all just in my head, at this point? I'm only 25. This is ridiculous. How can it possibly take me this long to heal such a minor injury?
I wanted my hands back - I wanted it real bad. I wanted it so bad that I did something dirty: I made myself believe something. Well, actually, I pretended to be a person who really, really believed hi
... (read more)It was probably just regression to the mean because lots of things are, but I started feeling RSI-like symptoms a few months ago, read this, did this, and now they're gone, and in the possibilities where this did help, thank you! (And either way, this did make me feel less anxious about it 😀)
Still gone. I'm now sleeping without wrist braces and doing intense daily exercise, like bicep curls and pushups.
Totally 100% gone. Sometimes I go weeks forgetting that pain was ever part of my life.
This morning, I read about how close we came to total destruction during the Cuban missile crisis, where we randomly survived because some Russian planes were inaccurate and also separately several Russian nuclear sub commanders didn't launch their missiles even though they were being harassed by US destroyers. The men were in 130 DEGREE HEAT for hours and passing out due to carbon dioxide poisoning, and still somehow they had enough restraint to not hit back.
And and
I just started crying. I am so grateful to those people. And to Khrushchev, for ridiculing his party members for caring about Russia's honor over the deaths of 500 million people. and Kennedy for being fairly careful and averse to ending the world.
If they had done anything differently...
Shard theory suggests that goals are more natural to specify/inculcate in their shard-forms (e.g. if around trash and a trash can, put the trash away), and not in their (presumably) final form of globally activated optimization of a coherent utility function which is the reflective equilibrium of inter-shard value-handshakes (e.g. a utility function over the agent's internal plan-ontology such that, when optimized directly, leads to trash getting put away, among other utility-level reflections of initial shards).
I could (and did) hope that I could specify a utility function which is safe to maximize because it penalizes power-seeking. I may as well have hoped to jump off of a building and float to the ground. On my model, that's just not how goals work in intelligent minds. If we've had anything at all beaten into our heads by our alignment thought experiments, it's that goals are hard to specify in their final form of utility functions.
I think it's time to think in a different specification language.
Against CIRL as a special case of against quickly jumping into highly specific speculation while ignoring empirical embodiments-of-the-desired-properties.
Just because we write down English describing what we want the AI to do ("be helpful"), propose a formalism (CIRL), and show good toy results (POMDPs where the agent waits to act until updating on more observations), that doesn't mean that the formalism will lead to anything remotely relevant to the original English words we used to describe it. (It's easier to say "this logic enables nonmonotonic reasoning" and mess around with different logics and show how a logic solves toy examples, than it is to pin down probability theory with Cox's theorem)
And yes, this criticism applies extremely strongly to my own past work with attainable utility preservation and impact measures. (Unfortunately, I learned my lesson after, and not before, making certain mistakes.)
In the context of "how do we build AIs which help people?", asking "does CIRL solve corrigibility?" is hilariously unjustified. By what evidence have we located such a specific question? We have assumed there is an achievable "corrigibility"-like property; we ha... (read more)
My maternal grandfather was the scientist in my family. I was young enough that my brain hadn't decided to start doing its job yet, so my memories with him are scattered and inconsistent and hard to retrieve. But there's no way that I could forget all of the dumb jokes he made; how we'd play Scrabble and he'd (almost surely) pretend to lose to me; how, every time he got to see me, his eyes would light up with boyish joy.
My greatest regret took place in the summer of 2007. My family celebrated the first day of the school year at an all-you-can-eat buffet, delicious food stacked high as the eye could fathom under lights of green, red, and blue. After a particularly savory meal, we made to leave the surrounding mall. My grandfather asked me to walk with him.
I was a child who thought to avoid being seen too close to uncool adults. I wasn't thinking. I wasn't thinking about hearing the cracking sound of his skull against the ground. I wasn't thinking about turning to see his poorly congealed blood flowing from his forehead out onto the floor. I wasn't thinking I would nervously watch him bleed for long minutes while shielding my seven-year-old brother from the sight. I wasn't thinking t
... (read more)My mother told me my memory was indeed faulty. He never asked me to walk with him; instead, he asked me to hug him during dinner. I said I'd hug him "tomorrow".
But I did, apparently, want to see him in the hospital; it was my mother and grandmother who decided I shouldn't see him in that state.
One mood I have for handling "AGI ruin"-feelings. I like cultivating an updateless sense of courage/stoicism: Out of all humans and out of all times, I live here; before knowing where I'd open my eyes, I'd want people like us to work hard and faithfully in times like this; I imagine trillions of future eyes looking back at me as I look forward to them: Me implementing a policy which makes their existence possible, them implementing a policy which makes the future worth looking forward to.
A problem with adversarial training. One heuristic I like to use is: "What would happen if I initialized a human-aligned model and then trained it with my training process?"
So, let's consider such a model, which cares about people (i.e. reliably pulls itself into futures where the people around it are kept safe). Suppose we also have some great adversarial training technique, such that we have e.g. a generative model which produces situations where the AI would break out of the lab without permission from its overseers. Then we run this procedure, update the AI by applying gradients calculated from penalties applied to its actions in that adversarially-generated context, and... profit?
But what actually happens with the aligned AI? Possibly something like:
- The context makes the AI spuriously believe someone is dying outside the lab, and that if the AI asked for permission to leave, the person would die.
- Therefore, the AI leaves without permission.
- The update procedure penalizes these lines of computation, such that in similar situations in the future (i.e. the AI thinks someone nearby is dying) the AI is less likely to take those actions (i.e. leaving to help the person).
- We have
... (read more)Earlier today, I was preparing for an interview. I warmed up by replying stream-of-consciousness to imaginary questions I thought they might ask. Seemed worth putting here.
... (read more)For quite some time, I've disliked wearing glasses. However, my eyes are sensitive, so I dismissed the possibility of contacts.
Over break, I realized I could still learn to use contacts, it would just take me longer. Sure enough, it took me an hour and five minutes to put in my first contact, and I couldn't get it out on my own. An hour of practice later, I put in a contact on my first try, and took it out a few seconds later. I'm very happily wearing contacts right now, as a matter of fact.
I'd suffered glasses for over fifteen years because of a cached decision – because I didn't think to rethink something literally right in front of my face every single day.
What cached decisions have you not reconsidered?
If you want to read Euclid's Elements, look at this absolutely gorgeous online rendition:
Positive values seem more robust and lasting than prohibitions. Imagine we train an AI on realistic situations where it can kill people, and penalize it when it does so. Suppose that we successfully instill a strong and widely activated "If going to kill people, then don't" value shard.
Even assuming this much, the situation seems fragile. See, many value shards are self-chaining. In The shard theory of human values, I wrote about how:
The juice shard chains into itself, reinforcing itself across time and thought-steps.
But a "don't kill" shard seems like it should remain... stubby? Primitive?... (read more)
AI strategy consideration. We won't know which AI run will be The One. Therefore, the amount of care taken on the training run which produces the first AGI, will—on average—be less careful than intended.
No team is going to run a training run with more care than they would have used for the AGI Run, especially if they don't even think that the current run will produce AGI. So the average care taken on the real AGI Run will be strictly less than intended.
Teams which try to be more careful on each run will take longer to iterate on AI designs, thereby lowering the probability that they (the relatively careful team) will be the first to do an AGI Run.
Upshots:
- Th
... (read more)Very nice people don’t usually search for maximally-nice outcomes — they don’t consider plans like “killing my really mean neighbor so as to increase average niceness over time.” I think there are a range of reasons for this plan not being generated. Here’s one.
Consider a person with a niceness-shard. This might look like an aggregation of subshards/subroutines like “if
person nearbyandperson.state==sad, sample plan generator for ways to make them happy” and “bid upwards on plans which lead to people being happier and more respectful, according to my world model.” In mental contexts where this shard is very influential, it would have a large influence on the planning process.However, people are not just made up of a grader and a plan-generator/actor — they are not just “the plan-generating part” and “the plan-grading part.” The next sampled plan modification, the next internal-monologue-thought to have—these are influenced and steered by e.g. the nice-shard. If the next macrostep of reasoning is about e.g. hurting people, well — the niceness shard is activated, and will bid down on this.
The niceness shard isn’t just bidding over outcomes, it’s bidding on next thoughts (on m... (read more)
I'm pretty sure that LessWrong will never have profile pictures - at least, I hope not! But my partner Emma recently drew me something very special:
Comment #1000 on LessWrong :)
Back-of-the-envelope probability estimate of alignment-by-default via a certain shard-theoretic pathway. The following is what I said in a conversation discussing the plausibility of a proto-AGI picking up a "care about people" shard from the data, and retaining that value even through reflection. I was pushing back against a sentiment like "it's totally improbable, from our current uncertainty, for AIs to retain caring-about-people shards. This is only one story among billions."
Here's some of what I had to say:
[Let's reconsider the five-step mechanistic story I made up.] I'd give the following conditional probabilities (made up with about 5 seconds of thought each):
... (read more)Examples should include actual details. I often ask people to give a concrete example, and they often don't. I wish this happened less. For example:
This is not a concrete example.
This is a concrete example.
In Eliezer's mad investor chaos and the woman of asmodeus, the reader experiences (mild spoilers in the spoiler box, heavy spoilers if you click the text):
a character deriving and internalizing probability theory for the first time. They learned to introspectively experience belief updates, holding their brain's processes against the Lawful theoretical ideal prescribed by Bayes' Law.
I thought this part was beautiful. I spent four hours driving yesterday, and nearly all of that time re-listening to Rationality: AI->Zombies using this "probability sight frame. I practiced translating each essay into the frame.
When I think about the future, I feel a directed graph showing the causality, with branched updated beliefs running alongside the future nodes, with my mind enforcing the updates on the beliefs at each time step. In this frame, if I heard the pattering of a four-legged animal outside my door, and I consider opening the door, then I can feel the future observation forking my future beliefs depending on how reality turns out. But if I imagine being blind and deaf, there is no way to fuel my brain with reality-distinguishment/evidence, and my beliefs can't adapt acco... (read more)
Why do many people think RL will produce "agents", but maybe (self-)supervised learning ((S)SL) won't? Historically, the field of RL says that RL trains agents. That, of course, is no argument at all. Let's consider the technical differences between the training regimes.
In the modern era, both RL and (S)SL involve initializing one or more neural networks, and using the reward/loss function to provide cognitive updates to the network(s). Now we arrive at some differences.
Some of this isn't new (see Hidden Incentives for Auto-Induced Distributional Shift), but I think it's important and felt like writing up my own take on it. Maybe this becomes a post later.
[Exact gradients] RL's credit assignment problem is harder than (self-)supervised learning's. In RL, if an agent solves a maze in 10 steps, it gets (discounted) reward; this trajectory then provides a set of reward-modulated gradients to the agent. But if the agent could have solved the maze in 5 steps, the agent isn't directly updated to be more likely to do that in the future; RL's gradients are generally inexact, not pointing directly at intended behavior.
On the other hand, if a supervised-learning classifier outputs
dog... (read more)I think instrumental convergence also occurs in the model space for machine learning. For example, many different architectures likely learn edge detectors in order to minimize classification loss on MNIST. But wait - you'd also learn edge detectors to maximize classification loss on MNIST (loosely, getting 0% on a multiple-choice exam requires knowing all of the right answers). I bet you'd learn these features for a wide range of cost functions. I wonder if that's already been empirically investigated?
And, same for adversarial features. And perhaps, same for mesa optimizers (understanding how to stop mesa optimizers from being instrumentally convergent seems closely related to solving inner alignment).
What can we learn about this?
I regret each of the thousands of hours I spent on my power-seeking theorems, and sometimes fantasize about retracting one or both papers. I am pained every time someone cites "Optimal policies tend to seek power", and despair that it is included in the alignment 201 curriculum. I think this work makes readers actively worse at thinking about realistic trained systems.
I think a healthy alignment community would have rebuked me for that line of research, but sadly I only remember about two people objecting that "optimality" is a horrible way of understanding trained policies.
I think the basic idea of instrumental convergence is just really blindingly obvious, and I think it is very annoying that there are people who will cluck their tongues and stroke their beards and say "Hmm, instrumental convergence you say? I won't believe it unless it is in a very prestigious journal with academic affiliations at the top and Computer Modern font and an impressive-looking methods section."
I am happy that your papers exist to throw at such people.
Anyway, if optimal policies tend to seek power, then I desire to believe that optimal policies tend to seek power :) :) And if optimal policies aren't too relevant to the alignment problem, well neither are 99.99999% of papers, but it would be pretty silly to retract all of those :)
It seems like just 4 months ago you still endorsed your second power-seeking paper:
Why are you now "fantasizing" about retracting it?
A lot of people might have thought something like, "optimality is not a great way of understanding trained policies, but maybe it can be a starting point that leads to more realistic ways of understanding them" and therefore didn't object for that reason. (Just guessing as I apparently wasn't personally paying attention to this line of research back then.)
Which seems to have turned out to be true, at least as of 4 months ago, when you still endorsed your second paper as "actually has a shot of being applicable to... (read more)
You can use ChatGPT without helping train future models:
Outer/inner alignment decomposes a hard problem into two extremely hard problems.
I have a long post draft about this, but I keep delaying putting it out in order to better elaborate the prereqs which I seem to keep getting stuck on when elaborating the ideas. I figure I might as well put this out for now, maybe it will make some difference for someone.
I think that the inner/outer alignment framing[1] seems appealing but is actually a doomed problem decomposition and an unhelpful frame for alignment.
- The reward function is a tool which chisels cognition into agents through gradient updates, but the outer/inner decomposition assumes that that tool should also embody the goals we want to chisel into the agent. When chiseling a statue, the chisel doesn’t have to also look like the finished statue.
- I know of zero success stories for outer alignment to real-world goals.
- More precisely, stories where people decided “I want an AI which [helps humans / makes diamonds / plays Tic-Tac-Toe / grows strawberries]”, and then wrote down an outer objective only maximized in those worlds.
- This is pretty weird on any model where most of the
... (read more)Weak derivatives
In calculus, the product rule says ddx(f⋅g)=f′⋅g+f⋅g′. The fundamental theorem of calculus says that the Riemann integral acts as the anti-derivative.[1] Combining these two facts, we derive integration by parts:
ddx(F⋅G)=f⋅G+F⋅g∫ddx(F⋅G)dx=∫f⋅G+F⋅gdxF⋅G−∫F⋅gdx=∫f⋅Gdx.
It turns out that we can use these two properties to generalize the derivative to match some of our intuitions on edge cases. Let's think about the absolute value function:
The boring old normal derivative isn't defined at x=0, but it seems like it'd make sense to be able to say that the derivative is eg 0. Why might this make sense?
Taylor's theorem (and its generalizations) characterize first derivatives as tangent lines with slope L:=f′(x0) which provide good local approximations of f around x0: f(x)≈f(x0)+L(x−x0). You can prove that this is the best approximation you can get using only f(x0) and L! In the absolute value example, defining the "derivative" to be zero at x=0 would minimize approximation error on average in neighborhoods around the origin.
In multivariable calculus, the Jacobian is a tangent plane which again minimizes approximation error (with respect to the Eucli
... (read more)When I notice I feel frustrated, unproductive, lethargic, etc, I run down a simple checklist:
It's simple, but 80%+ of the time, it fixes the issue.
While reading Focusing today, I thought about the book and wondered how many exercises it would have. I felt a twinge of aversion. In keeping with my goal of increasing internal transparency, I said to myself: "I explicitly and consciously notice that I felt averse to some aspect of this book".
I then Focused on the aversion. Turns out, I felt a little bit disgusted, because a part of me reasoned thusly:
(Transcription of a deeper Focusing on this reasoning)
I'm afraid of being slow. Part of it is surely the psychological remnants of the RSI I developed in the summer of 2018. That is, slowing down is now emotionally associated with disability and frustration. There was a period of meteoric progress as I started reading textbooks and doing great research, and then there was pain. That pain struck even when I was just trying to take care of myself, sleep, open doors. That pain then left me on the floor of my apartment, staring at the ceiling, desperately willing my hands to just get better. They didn't (for a long while), so I
... (read more)An alternate mechanistic vision of how agents can be motivated to directly care about e.g. diamonds or working hard. In Don't design agents which exploit adversarial inputs, I wrote about two possible mind-designs:
I explained how evaluation-child is positively incentivized to dupe his model of his mom and thereby exploit adversarial inputs to her cognition. This shows that aligning an agent to evaluations of good behavior is not even close to aligning an agent to good behavior.
However, some commenters seemed maybe skeptical that value-child can exist, or uncertain how concretely that kind of mind works. I worry/suspect that many people have read shard theory posts without internalizing new ideas about how cognition can work, ... (read more)
Experiment: Train an agent in MineRL which robustly cares about chickens (e.g. would zero-shot generalize to saving chickens in a pen from oncoming lava, by opening the pen and chasing them out, or stopping the lava). Challenge mode: use a reward signal which is a direct function of the agent's sensory input.
This is a direct predecessor to the "Get an agent to care about real-world dogs" problem. I think solving the Minecraft version of this problem will tell us something about how outer reward schedules relate to inner learned values, in a way which directly tackles the key questions, the sensory observability/information inaccessibility issue, and which is testable today.
(Credit to Patrick Finley for the idea)
I passed a homeless man today. His face was wracked in pain, body rocking back and forth, eyes clenched shut. A dirty sign lay forgotten on the ground: "very hungry".
This man was once a child, with parents and friends and dreams and birthday parties and maybe siblings he'd get in arguments with and snow days he'd hope for.
And now he's just hurting.
And now I can't help him without abandoning others. So he's still hurting. Right now.
Reality is still allowed to make this happen. This is wrong. This has to change.
Suppose I actually cared about this man with the intensity he deserved - imagine that he were my brother, father, or best friend.
The obvious first thing to do before interacting further is to buy him a good meal and a healthy helping of groceries. Then, I need to figure out his deal. Is he hurting, or is he also suffering from mental illness?
If the former, I'd go the more straightforward route of befriending him, helping him purchase a sharp business professional outfit, teaching him to interview and present himself with confidence, secure an apartment, and find a job.
If the latter, this gets trickier. I'd still try and befriend him (consistently being a source of cheerful conversation and delicious food would probably help), but he might not be willing or able to get the help he needs, and I wouldn't have the legal right to force him. My best bet might be to enlist the help of a psychological professional for these interactions. If this doesn't work, my first thought would be to influence the local government to get the broader problem fixed (I'd spend at least an hour considering other plans before proceeding further, here). Realistically, there's ... (read more)
I work on technical AI alignment, so some of those I help (in expectation) don't even exist yet. I don't view this as what I'd do if my top priority were helping this man.
That's a good question. I think the answer is yes, at least for my close family. Recently, I've expended substantial energy persuading my family to sign up for cryonics with me, winning over my mother, brother, and (I anticipate) my aunt. My father has lingering concerns which I think he wouldn't have upon sufficient reflection, so I've designed a similar plan for ensuring he makes what I perceive to be the correct, option-preserving choice. For example, I made significant targeted donations to effective charities on his behalf to offset (what he perceives as) a considerable drawback of cryonics: his inability to also be an organ donor.
A universe in which humanity wins but my dad is gone would be quite sad t... (read more)
If you raised children in many different cultures, "how many" different reflectively stable moralities could they acquire? (What's the "VC dimension" of human morality, without cheating by e.g. directly reprogramming brains?)
(This is probably a Wrong Question, but I still find it interesting to ask.)
Listening to Eneasz Brodski's excellent reading of Crystal Society, I noticed how curious I am about how AGI will end up working. How are we actually going to do it? What are those insights? I want to understand quite badly, which I didn't realize until experiencing this (so far) intelligently written story.
Similarly, how do we actually "align" agents, and what are good frames for thinking about that?
Here's to hoping we don't sate the former curiosity too early.
The "maximize all the variables" tendency in reasoning about AGI.
Here are some lines of thought I perceive, which are probably straw to varying extents for some people and real to varying extents for other people. I give varying responses to each, but the point isn't the truth value of any given statement, but of a pattern across the statements:
- If an AGI has a concept around diamonds, and is motivated in some way to make diamonds, it will make diamonds which maximally activate its diamond-concept circuitry (possible example).
- My response.
- An AI will be trained to minimal loss on the training distribution.
- SGD does not reliably find minimum-loss configurations (modulo expressivity), in practice, in cases we care about. The existence of knowledge distillation is one large counterexample.

- Quintin: "In terms of results about model distillation, you could look at appendix G.2 of the Gopher paper. They compare training a 1.4 billion parameter model directly, versus distilling a 1.4 B model from a 7.1 B model."
- Predictive processing means that the goal of the human learning process is to minimize predictive loss.[1]
- In a process where local modifications are applied to reduce some
... (read more)I think this type of criticism is applicable in an even wider range of fields than even you immediately imagine (though in varying degrees, and with greater or lesser obviousness or direct correspondence to the SGD case). Some examples:
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... (read more)Despite the economists, the economy doesn't try to maximize welfare, or even net dollar-equivalent wealth. It rewards firms which are able to make a profit in proportion to how much they're able to make a profit, and dis-rewards firms which aren't able to make a profit. Firms which are technically profitable, but have no local profit incentive gradient pointing towards them (factoring in the existence of rich people and lenders, neither of which are perfect expected profit maximizers) generally will not happen.
Individual firms also don't (only) try to maximize profit. Some parts of them may maximize profit, but most are just structures of people built from local social capital and economic capital incentive gradients.
Politicians don't try to (only) maximize win-probability.
Democracies don't try to (only) maximize voter approval.
Evolution doesn't try to maximize inclusive genetic fitness.
Memes don't try to maximize inclusive memetic
I was talking with Abram Demski today about a promising-seeming research direction. (Following is my own recollection)
One of my (TurnTrout's) reasons for alignment optimism is that I think:
- We can examine early-training cognition and behavior to some extent, since the system is presumably not yet superintelligent and planning against us,
- (Although this amount of information depends on how much interpretability and agent-internals theory we do now)
- All else equal, early-training values (decision-influences) are the most important to influence, since they steer future training.
- It's crucial to get early-training value shards of which a substantial fraction are "human-compatible values" (whatever that means)
- For example, if there are protect-human-shards which
- reliably bid against plans where people get hurt,
- steer deliberation away from such plan stubs, and
- these shards are "reflectively endorsed" by the overall shard economy (i.e. the decision-making isn't steering towards plans where the protect-human shards get removed)
- If we install influential human-compatible shards early in training, and they get retained, they will help us in mid- and late-training where we can't affect the ball
... (read more)"Globally activated consequentialist reasoning is convergent as agents get smarter" is dealt an evidential blow by von Neumann:
Good, original thinking feels present to me - as if mental resources are well-allocated.
The thought which prompted this:
Reacting to a bit of HPMOR here, I noticed something felt off about Harry's reply to the Fred/George-tried-for-two-seconds thing. Having a bit of experience noticing confusing, I did not think "I notice I am confused" (although this can be useful). I did not think "Eliezer probably put thought into this", or "Harry is kinda dumb in certain ways - so what if he's a bit unfair here?". Without resurfacing, or distraction, or wondering if this train of thought is more fun than just reading further, I just thought about the object-level exchange.
People need to allocate mental energy wisely; this goes far beyond focusing on important tasks. Your existing mental skillsets already optimize and auto-pilot certain mental motions for you, so you should allocate less deliberation to them. In this case, the confusion-noticing module was honed; by not worrying about how w
... (read more)Yesterday, I put the finishing touches on my chef d'œuvre, a series of important safety-relevant proofs I've been striving for since early June. Strangely, I felt a great exhaustion come over me. These proofs had been my obsession for so long, and now - now, I'm done.
I've had this feeling before; three years ago, I studied fervently for a Google interview. The literal moment the interview concluded, a fever overtook me. I was sick for days. All the stress and expectation and readiness-to-fight which had been pent up, released.
I don't know why this happens. But right now, I'm still a little tired, even after getting a good night's sleep.
Be kind to yourself for sample efficiency reasons. Reinforcing good behavior provides an exact "policy gradient" towards desired outputs. Whipping yourself provides an "inexact gradient" away from undesired decisions, which is much harder to learn. :)
If you want to argue an alignment proposal "breaks after enough optimization pressure", you should give a concrete example in which the breaking happens (or at least internally check to make sure you can give one). I perceive people as saying "breaks under optimization pressure" in scenarios where it doesn't even make sense.
For example, if I get smarter, would I stop loving my family because I applied too much optimization pressure to my own values? I think not.
If you're tempted to write "clearly" in a mathematical proof, the word quite likely glosses over a key detail you're confused about. Use that temptation as a clue for where to dig in deeper.
At least, that's how it is for me.
I went to the doctor's yesterday. This was embarrassing for them on several fronts.
First, I had to come in to do an appointment which could be done over telemedicine, but apparently there are regulations against this.
Second, while they did temp checks and required masks (yay!), none of the nurses or doctors actually wore anything stronger than a surgical mask. I'm coming in here with a KN95 + goggles + face shield because why not take cheap precautions to reduce the risk, and my own doctor is just wearing a surgical? I bought 20 KN95s for, like, 15 bucks on Amazon.
Third, and worst of all, my own doctor spouted absolute nonsense. The mildest insinuation was that surgical facemasks only prevent transmission, but I seem to recall that many kinds of surgical masks halve your chances of infection as well.
Then, as I understood it, he first claimed that coronavirus and the flu have comparable case fatality rates. I wasn't sure if I'd heard him correctly - this was an expert talking about his area of expertise, so I felt like I had surely misunderstood him. I was taken aback. But, looking back, that's what he meant.
He went on to suggest that we can't expect COVID immunity to last (wrong) b... (read more)
Judgment in Managerial Decision Making says that (subconscious) misapplication of e.g. the representativeness heuristic causes insensitivity to base rates and to sample size, failure to reason about probabilities correctly, failure to consider regression to the mean, and the conjunction fallacy. My model of this is that representativeness / availability / confirmation bias work off of a mechanism somewhat similar to attention in neural networks: due to how the brain performs time-limited search, more salient/recent memories get prioritized for recall.
The availability heuristic goes wrong when our saliency-weighted perceptions of the frequency of events is a biased estimator of the real frequency, or maybe when we just happen to be extrapolating off of a very small sample size. Concepts get inappropriately activated in our mind, and we therefore reason incorrectly. Attention also explains anchoring: you can more readily bring to mind things related to your anchor due to salience.
The case for confirmation bias seems to be a little more involved: first, we had evolutionary pressure to win arguments, which means our search is meant to find supportive arguments and avoid even subconscio
... (read more)From my Facebook
My life has gotten a lot more insane over the last two years. However, it's also gotten a lot more wonderful, and I want to take time to share how thankful I am for that.
Before, life felt like... a thing that you experience, where you score points and accolades and check boxes. It felt kinda fake, but parts of it were nice. I had this nice cozy little box that I lived in, a mental cage circumscribing my entire life. Today, I feel (much more) free.
I love how curious I've become, even about "unsophisticated" things. Near dusk, I walked the winter wonderland of Ogden, Utah with my aunt and uncle. I spotted this gorgeous red ornament hanging from a tree, with a hunk of snow stuck to it at north-east orientation. This snow had apparently decided to defy gravity. I just stopped and stared. I was so confused. I'd kinda guessed that the dry snow must induce a huge coefficient of static friction, hence the winter wonderland. But that didn't suffice to explain this. I bounded over and saw the smooth surface was iced, so maybe part of the snow melted in the midday sun, froze as evening advanced, and then the part-ice part-snow chunk stuck much more solidly to the ornament.
Mayb
... (read more)With respect to the integers, 2 is prime. But with respect to the Gaussian integers, it's not: it has factorization 2=(1−i)(1+i). Here's what's happening.
You can view complex multiplication as scaling and rotating the complex plane. So, when we take our unit vector 1 and multiply by (1+i), we're scaling it by |1+i|=√2 and rotating it counterclockwise by 45∘:
This gets us to the purple vector. Now, we multiply by (1−i), scaling it up by √2 again (in green), and rotating it clockwise again by the same amount. You can even deal with the scaling and rotations separately (scale twice by √2, with zero net rotation).
Thomas Kwa suggested that consequentialist agents seem to have less superficial (observation, belief state) -> action mappings. EG a shard agent might have:
But a consequentialist would just reason about what happens, and not mess with those heuristics. (OFC, consequentialism would be a matter of degree)
In this way, changing a small set of decision-relevant features (e.g. "Brown dog treat" -> "brown ball of chocolate") changes the consequentialist's action logits a lot, way more than it changes the shard agent's logits. In a squinty, informal way, the (belief state -> logits) function has a higher Lipschitz constant/is more smooth for the shard agent than for the consequentialist agent.
So maybe one (pre-deception) test for consequentialist reasoning is to test sensitivity of decision-making to small perturbations in observation-space (e.g. dog treat -> tiny chocolate) but large perturbations in action-consequence space (e.g. happy dog -> sick dog). You could spin up two copies of the model to compare.
Are there convergently-ordered developmental milestones for AI? I suspect there may be convergent orderings in which AI capabilities emerge. For example, it seems that LMs develop syntax before semantics, but maybe there's an even more detailed ordering relative to a fixed dataset. And in embodied tasks with spatial navigation and recurrent memory, there may be an order in which enduring spatial awareness emerges (i.e. "object permanence").
In A shot at the diamond-alignment problem, I wrote:
... (read more)Quick summary of a major takeaway from Reward is not the optimization target:
Stop thinking about whether the reward is "representing what we want", or focusing overmuch on whether agents will "optimize the reward function." Instead, just consider how the reward and loss signals affect the AI via the gradient updates. How do the updates affect the AI's internal computations and decision-making?
I remarked to my brother, Josh, that when most people find themselves hopefully saying "here's how X can still happen!", it's a lost cause and they should stop grasping for straws and move on with their lives. Josh grinned, pulled out his cryonics necklace, and said "here's how I can still not die!"
Suppose you could choose how much time to spend at your local library, during which:
Suppose you don't go crazy from solitary confinement, etc. Remember that value drift is a potential thing.
How long would you ask for?
I feel very excited by the AI alignment discussion group I'm running at Oregon State University. Three weeks ago, most attendees didn't know much about "AI security mindset"-ish considerations. This week, I asked the question "what, if anything, could go wrong with a superhuman reward maximizer which is rewarded for pictures of smiling people? Don't just fit a bad story to the reward function. Think carefully."
There was some discussion and initial optimism, after which someone said "wait, those optimistic solutions are just the ones you'd prioritize! What's that called, again?" (It's called anthropomorphic optimism)
I'm so proud.
Hindsight bias and illusion of transparency seem like special cases of a failure to fully uncondition variables in your world model (e.g. who won the basketball game), or to model an ignorant other person. Such that your attempts to reason from your prior state of ignorance (e.g. about who won) either are advantaged by the residual information or reactivate your memories of that information.
Partial alignment successes seem possible.
People care about lots of things, from family to sex to aesthetics. My values don't collapse down to any one of these.
I think AIs will learn lots of values by default. I don't think we need all of these values to be aligned with human values. I think this is quite important.
- I think the more of the AI's values we align to care about us and make decisions in the way we want, the better. (This is vague because I haven't yet sketched out AI internal motivations which I think would actually produce good outcomes. On my list!)
- I think there are strong gains from trade possible among an agent's values. If I care about bananas and apples, I don't need to split my resources between the two values, I don't need to make one successor agent for each value. I can drive to the store and buy both bananas and apples, and only pay for fuel once.
- This makes it lower-cost for internal values handshakes to compromise; it's less than 50% costly for a power-seeking value to give human-compatible values 50% weight in the reflective utility function.
- I think there are thresholds at which the AI doesn't care about us sufficiently strongly, and
... (read more)If another person mentions an "outer objective/base objective" (in terms of e.g. a reward function) to which we should align an AI, that indicates to me that their view on alignment is very different. The type error is akin to the type error of saying "My physics professor should be an understanding of physical law." The function of a physics professor is to supply cognitive updates such that you end up understanding physical law. They are not, themselves, that understanding.
Similarly, "The reward function should be a human-aligned objective" -- The function of the reward function is to supply cognitive updates such that the agent ends up with human-aligned objectives. The reward function is not, itself, a human aligned objective.
Against "Evolution did it."
"Why do worms regenerate without higher cancer incidence? Hm, perhaps because they were selected to do that!"
"Evolution did it" explains why a trait was brought into existence, but not how the trait is implemented. You should still feel confused about the above question, even after saying "Evolution did it!".
I thought I learned not to make this mistake a few months ago, but I made it again today in a discussion with Andrew Critch. Evolution did it is not a mechanistic explanation.
I often have thunk thoughts like "Consider an AI with a utility function that is just barely incorrect, such that it doesn't place any value on boredom. Then the AI optimizes the universe in a bad way."
One problem with this thought is that it's not clear that I'm really thinking about anything in particular, anything which actually exists. What am I actually considering in the above quotation? With respect to what, exactly, is the AI's utility function "incorrect"? Is there a utility function for which its optimal policies are aligned?
For sufficiently expressive utility functions, the answer has to be "yes." For example, if the utility function is over the AI's action histories, you can just hardcode a safe, benevolent policy into the AI: utility 0 if the AI has ever taken a bad action, 1 otherwise. Since there presumably exists at least some sequence of AI outputs which leads to wonderful outcomes, this action-history utility function works.
But this is trivial and not what we mean by a "correct" utility function. So, now I'm left with a puzzle. What does it mean for the AI to have a correct utility function? I do not think this is a quibble. The quoted thought seems ungrounded from the substance of the alignment problem.
An AGI's early learned values will steer its future training and play a huge part in determining its eventual stable values. I think most of the ball game is in ensuring the agent has good values by the time it's smart, because that's when it'll start being reflectively stable. Therefore, we can iterate on important parts of alignment, because the most important parts come relatively early in the training run, and early corresponds to "parts of the AI value formation process which we can test before we hit AGI, without training one all the way out."
I think this, in theory, cuts away a substantial amount of the "But we only get one shot" problem. In practice, maybe OpenMind just YOLOs ahead anyways and we only get a few years in the appropriate and informative regime. But this suggests several kinds of experiments to start running now, like "get a Minecraft agent which robustly cares about chickens", because that tells us about how to map outer signals into inner values.
When proving theorems for my research, I often take time to consider the weakest conditions under which the desired result holds - even if it's just a relatively unimportant and narrow lemma. By understanding the weakest conditions, you isolate the load-bearing requirements for the phenomenon of interest. I find this helps me build better gears-level models of the mathematical object I'm studying. Furthermore, understanding the result in generality allows me to recognize analogies and cross-over opportunities in the future. Lastly, I just find this plain satisfying.
Does Venting Anger Feed or Extinguish the Flame? Catharsis, Rumination, Distraction, Anger, and Aggressive Responding
Interesting. A cursory !scholar search indicates these results have replicated, but I haven't done an in-depth review.
I never thought I'd be seriously testing the reasoning abilities of an AI in 2020.
Looking back, history feels easy to predict; hindsight + the hard work of historians makes it (feel) easy to pinpoint the key portents. Given what we think about AI risk, in hindsight, might this have been the most disturbing development of 2020 thus far?
I personally lean towards "no", because this scaling seemed somewhat predictable from GPT-2 (flag - possible hindsight bias), and because 2020 has been so awful so far. But it seems possible, at least. I don't really know what update GPT-3 is to my AI risk estimates & timelines.
DL so far has been easy to predict - if you bought into a specific theory of connectionism & scaling espoused by Schmidhuber, Moravec, Sutskever, and a few others, as I point out in https://www.gwern.net/newsletter/2019/13#what-progress & https://www.gwern.net/newsletter/2020/05#gpt-3 . Even the dates are more or less correct! The really surprising thing is that that particular extreme fringe lunatic theory turned out to be correct. So the question is, was everyone else wrong for the right reasons (similar to the Greeks dismissing heliocentrism for excellent reasons yet still being wrong), or wrong for the wrong reasons, and why, and how can we prevent that from happening again and spending the next decade being surprised in potentially very bad ways?
An exercise in the companion workbook to the Feynman Lectures on Physics asked me to compute a rather arduous numerical simulation. At first, this seemed like a "pass" in favor of an exercise more amenable to analytic and conceptual analysis; arithmetic really bores me. Then, I realized I was being dumb - I'm a computer scientist.
Suddenly, this exercise became very cool, as I quickly figured out the equations and code, crunched the numbers in an instant, and churned out a nice scatterplot. This seems like a case where cross-domain competence is unusually helpful (although it's not like I had to bust out any esoteric theoretical CS knowledge). I'm wondering whether this kind of thing will compound as I learn more and more areas; whether previously arduous or difficult exercises become easy when attacked with well-honed tools and frames from other disciplines.
I recently reached out to my two PhD advisors to discuss Hinton stepping down from Google. An excerpt from one of my emails:
... (read more)Speculation: RL rearranges and reweights latent model abilities, which SL created. (I think this mostly isn't novel, just pulling together a few important threads)
Suppose I supervised-train a LM on an English corpus, and I want it to speak Spanish. RL is inappropriate for the task, because its on-policy exploration won't output interestingly better or worse Spanish completions. So there's not obvious content for me to grade.
More generally, RL can provide inexact gradients away from undesired behavior (e.g. negative reinforcement event -> downweigh... (read more)
When I think about takeoffs, I notice that I'm less interested in GDP or how fast the AI's cognition improves, and more on how AI will affect me, and how quickly. More plainly, how fast will shit go crazy for me, and how does that change my ability to steer events?
For example, assume unipolarity. Let architecture Z be the architecture which happens to be used to train the AGI.
- How long is the duration between "architecture Z is published / seriously considered" and "the AGI kills me, assuming alignment fails"?
- How long do I have, in theory,
... (read more)Being proven wrong is an awesome, exciting shortcut which lets me reach the truth even faster.
Research-guiding heuristic: "What would future-TurnTrout predictably want me to get done now?"
The Pfizer phase 3 study's last endpoint is 7 days after the second shot. Does anyone know why the CDC recommends waiting 2 weeks for full protection? Are they just being the CDC again?
The framing effect & aversion to losses generally cause us to execute more cautious plans. I’m realizing this is another reason to reframe my x-risk motivation from “I won’t let the world be destroyed” to “there’s so much fun we could have, and I want to make sure that happens”. I think we need more exploratory thinking in alignment research right now.
(Also, the former motivation style led to me crashing and burning a bit when my hands were injured and I was no longer able to do much.)
ETA: actually, i’m realizing I had the effect backwards. Framing via
... (read more)Three recent downward updates for me on alignment getting solved in time:
- Thinking for hours about AI strategy made me internalize that communication difficulties are real serious.
... (read more)I'm not just solving technical problems—I'm also solving interpersonal problems, communication problems, incentive problems. Even if my current hot takes around shard theory / outer/inner alignment are right, and even if I put up a LW post which finally successfully communicates some of my key points, reality totally allows OpenAI to just train an AGI the next month without incorp
I often get the impression that people weigh off e.g. doing shard theory alignment strategies under the shard theory alignment picture, versus inner/outer research under the inner/outer alignment picture, versus...
And insofar as this impression is correct, this is a mistake. There is only one way alignment is.
If inner/outer is altogether a more faithful picture of those dynamics:
- relatively coherent singular mesa-objectives form in agents, albeit not necessarily always search-based
- more fragility of value and difficulty in getting the mesa object
... (read more)I plan to mentor several people to work on shard theory and agent foundations this winter through SERI MATS. Apply here if you're interested in working with me and Quintin.
Amazing how much I can get done if I chant to myself "I'm just writing two pages of garbage abstract/introduction/related work, it's garbage, it's just garbage, don't fix it rn, keep typing"
What kind of reasoning would have allowed me to see MySpace in 2004, and then hypothesize the current craziness as a plausible endpoint of social media? Is this problem easier or harder than the problem of 15-20 year AI forecasting?
Over the last 2.5 years, I've read a lot of math textbooks. Not using Anki / spaced repetition systems over that time has been an enormous mistake. My factual recall seems worse-than-average among my peers, but when supplemented with Anki, it's far better than average (hence, I was able to learn 2000+ Japanese characters in 90 days, in college).
I considered using Anki for math in early 2018, but I dismissed it quickly because I hadn't had good experience using that application for things which weren't languages. I should have at least tried to see if... (read more)
This might be the best figure I've ever seen in a textbook. Talk about making a point!
An additional consideration for early work on interpretability: it slightly increases the chance we actually get an early warning shot. If a system misbehaves, we can inspect its cognition and (hopefully) find hints of intentional deception. Could motivate thousands of additional researcher-hours being put into alignment.
Today, let's read about GPT-3's obsession with Shrek:
... (read more)My power-seeking theorems seem a bit like Vingean reflection. In Vingean reflection, you reason about an agent which is significantly smarter than you: if I'm playing chess against an opponent who plays the optimal policy for the chess objective function, then I predict that I'll lose the game. I predict that I'll lose, even though I can't predict my opponent's (optimal) moves - otherwise I'd probably be that good myself.
My power-seeking theorems show that most objectives have optimal policies which e.g. avoid shutdown and survive into the far future, even... (read more)
If Hogwarts spits back an error if you try to add a non-integer number of house points, and if you can explain the busy beaver function to Hogwarts, you now have an oracle which answers n∣BB(k) for arbitrary k,n∈Z+: just state "BB(k)n points to Ravenclaw!". You can do this for other problems which reduce to divisibility tests (so, any decision problem f which you can somehow get Hogwarts to compute; if f(x)=1, 2∤f(x)).
Homework: find a way to safely take over the world using this power, and no other magic.
When I imagine configuring an imaginary pile of blocks, I can feel the blocks in front of me in this fake imaginary plane of existence. I feel aware of their spatial relationships to me, in the same way that it feels different to have your eyes closed in a closet vs in an empty auditorium.
But what is this mental workspace? Is it disjoint and separated from my normal spatial awareness, or does my brain copy/paste->modify my real-life spatial awareness. Like, if my brother is five feet in front of me, and then I imagine a blade flying five feet in f... (read more)
The new "Broader Impact" NeurIPS statement is a good step, but incentives are misaligned. Admitting fatally negative impact would set a researcher back in their career, as the paper would be rejected.
Idea: Consider a dangerous paper which would otherwise have been published. What if that paper were published title-only on the NeurIPS website, so that the researchers can still get career capital?
Problem: How do you ensure resubmission doesn't occur elsewhere?
Cool Math Concept You Never Realized You Wanted: Fréchet distance.
... (read more)Earlier today, I became curious why extrinsic motivation tends to preclude or decrease intrinsic motivation. This phenomenon is known as overjustification. There's likely agreed-upon theories for this, but here's some stream-of-consciousness as I reason and read through summarized experimental results. (ETA: Looks like there isn't consensus on why this happens)
My first hypothesis was that recognizing external rewards somehow precludes activation of curiosity-circuits in our brain. I'm imagining a kid engrossed in a puzzle. Then, they're told that they'll b
... (read more)Virtue ethics seems like model-free consequentialism to me.
Going through an intro chem textbook, it immediately strikes me how this should be as appealing and mysterious as the alchemical magic system of Fullmetal Alchemist. "The law of equivalent exchange" ≈ "conservation of energy/elements/mass (the last two holding only for normal chemical reactions)", etc. If only it were natural to take joy in the merely real...
Argument that you can't use a boundedly intelligent ELK solution to search over plans to find one which keeps the diamond in the vault. That is, the ELK solution probably would have to be at least as smart (or smarter) than the plan-generator.
Consider any situation where it's hard to keep the diamond in the vault. Then any successful plan will have relatively few degrees of freedom. Like, a bunch of really smart thieves will execute a cunning plot to extract the diamond. You can't just sit by or deploy some simple traps in this situation.
Therefore, any pla... (read more)
80% credence: It's very hard to train an inner agent which reflectively equilibrates to an EU maximizer only over commonly-postulated motivating quantities (like
# of diamondsor# of happy peopleorreward-signal) and not quantities like (# of times I have to look at a cube in a blue roomor-1 * subjective micromorts accrued).Intuitions:
- I expect contextually activated heuristics to be the default, and that agents will learn lots of such contextual values which don't cash out to being strictly about diamonds or people, even if the overall agent is mostly
... (read more)Notes on behaviorism: After reading a few minutes about it, behaviorism seems obviously false. It views the "important part" of reward to be the external behavior which led to the reward. If I put my hand on a stove, and get punished, then I'm less likely to do that again in the future. Or so the theory goes.
But this seems, in fullest generality, wildly false. The above argument black-boxes the inner structure of human cognition which produces the externally observed behavior.
What actually happens, on my model, is that the stove makes your hand hot, which ... (read more)
Argument sketch for why boxing is doomed if the agent is perfectly misaligned:
Consider a perfectly misaligned agent which has -1 times your utility function—it's zero-sum. Then suppose you got useful output of the agent. This means you're able to increase your EU. This means the AI decreased its EU by saying anything. Therefore, it should have shut up instead. But since we assume it's smarter than you, it realized this possibility, and so the fact that it's saying something means that it expects to gain by hurting your interests via its output. Therefore, the output can't be useful.
The costs of (not-so-trivial) inconveniences
I like exercising daily. Some days, I want to exercise more than others—let's suppose that I actually benefit more from exercise on that day. Therefore, I have a higher willingness to pay the price of working out.
Consider the population of TurnTrouts over time, one for each day. This is a population of consumers with different willingnesses to pay, and so we can plot the corresponding exercise demand curve (with a fixed price). In this idealized model, I exercise whenever my willingness to pay exceeds the price.
B... (read more)
The discussion of the HPMOR epilogue in this recent April Fool's thread was essentially online improv, where no one could acknowledge that without ruining the pretense. Maybe I should do more improv in real life, because I enjoyed it!
If you measure death-badness from behind the veil of ignorance, you’d naively prioritize well-liked, famous people with large families.
AIDungeon's subscriber-only GPT-3 can do some complex arithmetic, but it's very spotty. Bold text is me.
... (read more)Idea: learn by making conjectures (math, physical, etc) and then testing them / proving them, based on what I've already learned from a textbook.
Learning seems easier and faster when I'm curious about one of my own ideas.
Sentences spoken aloud are a latent space embedding of our thoughts; when trying to move a thought from our mind to another's, our thoughts are encoded with the aim of minimizing the other person's decoder error.
Broca’s area handles syntax, while Wernicke’s area handles the semantic side of language processing. Subjects with damage to the latter can speak in syntactically fluent jargon-filled sentences (fluent aphasia) – and they can’t even tell their utterances don’t make sense, because they can’t even make sense of the words leaving their own mouth!
It seems like GPT2 : Broca’s area :: ??? : Wernicke’s area. Are there any cog psych/AI theories on this?
We can think about how consumers respond to changes in price by considering the elasticity of the quantity demanded at a given price - how quickly does demand decrease as we raise prices? Price elasticity of demand is defined as % change in quantity% change in price; in other words, for price p and quantity q, this is pΔqqΔp (this looks kinda weird, and it wasn't immediately obvious what's happening here...). Revenue is the total amount of cash changing hands: pq.
What's happening here is that raising prices is a good idea when the revenue gained (the "pric
... (read more)How does representation interact with consciousness? Suppose you're reasoning about the universe via a partially observable Markov decision process, and that your model is incredibly detailed and accurate. Further suppose you represent states as numbers, as their numeric labels.
To get a handle on what I mean, consider the game of Pac-Man, which can be represented as a finite, deterministic, fully-observable MDP. Think about all possible game screens you can observe, and number them. Now get rid of the game screens. From the perspective of reinforcement lea
... (read more)I seem to differently discount different parts of what I want. For example, I'm somewhat willing to postpone fun to low-probability high-fun futures, whereas I'm not willing to do the same with romance.
Wikipedia has an unfortunate and incorrect-in-generality description of reinforcement learning (emphasis added)
Later in the article, talking about basic optimal-control inspired approaches:
... (read more)Idea for getting weak-in-expectation evidence about deception:
Be cautious with sequences-style "words don't matter, only anticipations matter." (At least, this is an impression I got from the sequences, and could probably back this up.) Words do matter insofar as they affect how your internal values bind. If you decide that... (searches for non-political example) monkeys count as "people", that will substantially affect your future decisions via e.g. changing your internal "person" predicate, which in turn will change how different downstream shards activate (like "if person harmed, be less likely to execute plan", a... (read more)
Why don't people reinforcement-learn to delude themselves? It would be very rewarding for me to believe that alignment is solved, everyone loves me, I've won at life as hard as possible. I think I do reinforcement learning over my own thought processes. So why don't I delude myself?
On my model of people, rewards provide ~"policy gradients" which update everything, but most importantly shards. I think eg the world model will have a ton more data from self-supervised learning, and so on net most of its bits won't come from reward gradients.
For example, if I ... (read more)
Basilisks are a great example of plans which are "trying" to get your plan evaluation procedure to clock in a huge upwards error. Sensible beings avoid considering such plans, and everything's fine. I am somewhat worried about an early-training AI learning about basilisks before the AI is reflectively wise enough to reject the basilisks.
For example:
- Pretraining on a corpus in which people worry about basilisks could elevate reasoning about basilisks to the AI's consideration,
- at which point the AI reasons in more detail because it's not... (read more)
How the power-seeking theorems relate to the selection theorem agenda.
- Power-seeking theorems. P(agent behavior | agent decision-making procedure, agent objective, other agent internals, environment).
- The power-seeking theorems also allow some discussion of P(agent behavior | agent training process, trai
... (read more)I've mostly studied the likelihood function for power-seeking behavior: what decision-making procedures, objectives, and environments produce what behavioral tendencies. I've discovered some gears for what situations cause what kinds of behaviors.
Idea: Expert prediction markets on predictions made by theories in the field, with $ for being a good predictor and lots of $ for designing and running a later-replicated experiment whose result the expert community strongly anti-predicted. Lots of problems with the plan, but surprisal-based compensation seems interesting and I haven't heard about it before.
What is "real"? I think about myself as a computation embedded in some other computation (i.e. a universe-history). I think "real" describes hypotheses about the environment where my computation lives. What should I think is real? That which an "ideal embedded reasoner" would assign high credence. However that works.
This sensibly suggests that Gimli-in-actual-Ea (LOTR) should believe he lives in Ea, and that Ea is real, even though it isn't our universe's Earth. Also, the notion accounts for indexical uncertainty by punting it to how embedded reasoning sho... (read more)
ordinal preferences just tell you which outcomes you like more than others: apples more than oranges.
Interval scale preferences assign numbers to outcomes, which communicates how close outcomes are in value: kiwi 1, orange 5, apple 6. You can say that apples have 5 times the advantage over kiwis that they do over oranges, but you can't say that apples are six times as good as kiwis. Fahrenheit and Celsius are also like this.
Ratio scale ("rational"? 😉) preferences do let you say that apples are six times as good as kiwis, and you need this property to maxi
... (read more)My autodidacting has given me a mental reflex which attempts to construct a gears-level explanation of almost any claim I hear. For example, when listening to “Listen to Your Heart” by Roxette:
I understood what she obviously meant and simultaneously found myself subvocalizing “she means all other reasonable plans are worse than listening to your heart - not that that’s literally all you can do”.
This reflex is really silly and annoying in the wrong context - I’ll fix it soon. But it’s pretty amusing
... (read more)One of the reasons I think corrigibility might have a simple core principle is: it seems possible to imagine a kind of AI which would make a lot of different possible designers happy. That is, if you imagine the same AI design deployed by counterfactually different agents with different values and somewhat-reasonable rationalities, it ends up doing a good job by almost all of them. It ends up acting to further the designers' interests in each counterfactual. This has been a useful informal way for me to think about corrigibility, when considering different
... (read more)I had an intuition that attainable utility preservation (RL but you maintain your ability to achieve other goals) points at a broader template for regularization. AUP regularizes the agent's optimal policy to be more palatable towards a bunch of different goals we may wish we had specified. I hinted at the end of Towards a New Impact Measure that the thing-behind-AUP might produce interesting ML regularization techniques.
This hunch was roughly correct; Model-Agnostic Meta-Learning tunes the network parameters such that they can be quickly adapted to achiev
... (read more)From the ELK report:
... (read more)I think that the training goal of "the AI never makes a catastrophic decision" is unrealistic and unachievable and unnecessary. I think this is not a natural shape for values to take. Consider a highly altruistic man with anger problems, strongly triggered by e.g. a specifc vacation home. If he is present with his wife at this home, he beats her. As long as he starts off away from the home, and knows about his anger problems, he will be motivated to resolve his anger problems, or at least avoid the triggering contexts / take other precautions to ensure her... (read more)
"Goodhart" is no longer part of my native ontology for considering alignment failures. When I hear "The AI goodharts on some proxy of human happiness", I start trying to fill in a concrete example mind design which fits that description and which is plausibly trainable. My mental events are something like:
Condition on: AI with primary value shards oriented around spurious correlate of human happiness; AI exhibited deceptive alignment during training, breaking perceived behavioral invariants during its sharp-capabilities-gainWarning: No history... (read more)Excalidraw is now quite good and works almost seamlessly on my iPad. It's also nice to use on the computer. I recommend it to people who want to make fast diagrams for their posts.
Reading EY's dath ilan glowfics, I can't help but think of how poor English is as a language to think in. I wonder if I could train myself to think without subvocalizing (presumably it would be too much work to come up with a well-optimized encoding of thoughts, all on my own, so no new language for me). No subvocalizing might let me think important thoughts more quickly and precisely.
How might we align AGI without relying on interpretability?
I'm currently pessimistic about the prospect. But it seems worth thinking about, because wouldn't it be such an amazing work-around?
My first idea straddles the border between contrived and intriguing. Consider some AGI-capable ML architecture, and imagine its Rn parameter space being 3-colored as follows:
- Gray if the
- Red if the parameter vector+... leads to a misaligned or dece
... (read more)parameter vector+training process+other initial conditionsleads to a nothingburger (a non-functional model)I'd like to see research exploring the relevance of intragenomic conflict to AI alignment research. Intragenomic conflict constitutes an in-the-wild example of misalignment, where conflict arises "within an agent" even though the agent's genes have strong instrumental incentives to work together (they share the same body).
In an interesting parallel to John Wentworth's Fixing the Good Regulator Theorem, I have an MDP result that says:
Roughly: being able to complete linearly many tasks in the state space means you ha... (read more)
I read someone saying that ~half of the universes in a neighborhood of ours went to Trump. But... this doesn't seem right. Assuming Biden wins in the world we live in, consider the possible perturbations to the mental states of each voter. (Big assumption! We aren't thinking about all possible modifications to the world state. Whatever that means.)
Assume all 2020 voters would be equally affected by a perturbation (which you can just think of as a decision-flip for simplicity, perhaps). Since we're talking about a neighborhood ("worlds pretty close to ours"... (read more)
Epistemic status: not an expert
Understanding Newton's third law, F=ddt(mv).
Consider the vector-valued velocity as a function of time, v:t↦R3. Scale this by the object's mass and you get the momentum function over time. Imagine this momentum function wiggling around over time, the vector from the origin rotating and growing and shrinking.
The third law says that force is the derivative of this rescaled vector function - if an object is more massive, then the same displacement of this rescaled arrow is a proportionally smaller velocity modification, because o... (read more)
Tricking AIDungeon's GPT-3 model into writing HPMOR:
You start reading Harry Potter and the Methods of Rationality by Eliezer Yudkowsky:
" "It said to me," said Professor Quirrell, "that it knew me, and that it would hunt me down someday, wherever I tried to hide." His face was rigid, showing no fright.
"Ah," Harry said. "I wouldn't worry about that, Professor Quirrell." It's not like Dementors can actually talk, or think; the structure they have is borrowed from your own mind and expectations...
Now
ARCHES distinguishes between single-agent / single-user and single-agent/multi-user alignment scenarios. Given assumptions like "everyone in society is VNM-rational" and "societal preferences should also follow VNM rationality", and "if everyone wants a thing, society also wants the thing", Harsanyi's utilitarian theorem shows that the societal utility function is a linear non-negative weighted combination of everyone's utilities. So, in a very narrow (and unrealistic) setting, Harsanyi's theorem tells you how the single-multi solution is built from the si
... (read more)From FLI's AI Alignment Podcast: Inverse Reinforcement Learning and Inferring Human Preferences with Dylan Hadfield-Menell:
... (read more)On page 22 of Probabilistic reasoning in intelligent systems, Pearl writes:
An agent observes a sequence of images displaying either a red or a blue ball. The balls are drawn according to some deterministic rule of the time step. Reasoning directly from the experiential data leads to ~Solomonoff induction. What mig
... (read more)We can imagine aliens building a superintelligent agent which helps them get what they want. This is a special case of aliens inventing tools. What kind of general process should these aliens use – how should they go about designing such an agent?
Assume that these aliens want things in the colloquial sense (not that they’re eg nontrivially VNM EU maximizers) and that a reasonable observer would say they’re closer to being rational than antirational. Then it seems[1] like these aliens eventually steer towards reflectively coherent rationality (provided they
... (read more)It seems to me that Zeno's paradoxes leverage incorrect, naïve notions of time and computation. We exist in the world, and we might suppose that that the world is being computed in some way. If time is continuous, then the computer might need to do some pretty weird things to determine our location at an infinite number of intermediate times. However, even if that were the case, we would never notice it – we exist within time and we would not observe the external behavior of the system which is computing us, nor its runtime.
Very rough idea
In 2018, I started thinking about corrigibility as "being the kind of agent lots of agents would be happy to have activated". This seems really close to a more ambitious version of what AUP tries to do (not be catastrophic for most agents).
I wonder if you could build an agent that rewrites itself / makes an agent which would tailor the AU landscape towards its creators' interests, under a wide distribution of creator agent goals/rationalities/capabilities. And maybe you then get a kind of generalization, where most simple algorithms which solve this solve ambitious AI alignment in full generality.
AFAICT, the deadweight loss triangle from eg price ceilings is just a lower bound on lost surplus. inefficient allocation to consumers means that people who value good less than market equilibrium price can buy it, while dwl triangle optimistically assumes consumers with highest willingness to buy will eat up the limited supply.
I was having a bit of trouble holding the point of quadratic residues in my mind. I could effortfully recite the definition, give an example, and walk through the broad-strokes steps of proving quadratic reciprocity. But it felt fake and stale and memorized.
Alex Mennen suggested a great way of thinking about it. For some odd prime p, consider the multiplicative group (Z/pZ)×. This group is abelian and has even order p−1. Now, consider a primitive root / generator g. By definition, every element of the group can be expressed as ge. The quadratic residues ar
... (read more)I noticed I was confused and liable to forget my grasp on what the hell is so "normal" about normal subgroups. You know what that means - colorful picture time!
First, the classic definition. A subgroup H is normal when, for all group elements g, gH=Hg (this is trivially true for all subgroups of abelian groups).
ETA: I drew the bounds a bit incorrectly; g is most certainly within the left coset (ge=g).
Notice that nontrivial cosets aren't subgroups, because they don't have the identity e.
This "normal" thing matters because sometimes we want to highlight regu
... (read more)The existence of the human genome yields at least two classes of evidence which I'm strongly interested in.
- Humans provide many highly correlated datapoints on general intelligence (human minds), as developed within one kind of learning process (best guess: massively parallel circuitry, locally randomly initialized, self-supervised learning + RL).
- We thereby gain valuable information about the dynamics of that learning process. For example, people care about lots of things (cars, flowers, animals, friends), and don't just have a single unitary mesa-obj
... (read more)Transplanting algorithms into randomly initialized networks. I wonder if you could train a policy network to walk upright in sim, back out the "walk upright" algorithm, randomly initialize a new network which can call that algorithm as a "subroutine call" (but the walk-upright weights are frozen), and then have the new second model learn to call that subroutine appropriately? Possibly the learned representations would be convergently similar enough to interface quickly via SGD update dynamics.
If so, this provides some (small, IMO) amount of rescue fo... (read more)
When I was younger, I never happened to "look in the right direction" on my own in order to start the process of becoming agentic and coherent. Here are some sentences I wish I had heard when I was a kid:
Plausibly just hearing this would have done it for me, but probably that's too optimistic.
What's up with biological hermaphrodite species? My first reaction was, "no way, what about the specialization benefits from sexual dimorphism?"
There are apparently no hermaphrodite mammal or bird species, which seems like evidence supporting my initial reaction. But there are, of course, other hermaphrodite species—maybe they aren't K-strategists, and so sexual dimorphism and role specialization isn't as important?
I went into a local dentist's office to get more prescription toothpaste; I was wearing my 3M p100 mask (with a surgical mask taped over the exhaust, in order to protect other people in addition to the native exhaust filtering offered by the mask). When I got in, the receptionist was on the phone. I realized it would be more sensible for me to wait outside and come back in, but I felt a strange reluctance to do so. It would be weird and awkward to leave right after entering. I hovered near the door for about 5 seconds before actually leaving. I was pretty ... (read more)
Instead of waiting to find out you were confused about new material you learned, pre-emptively google things like "common misconceptions about [concept]" and put the answers in your spaced repetition system, or otherwise magically remember them.
In order to reduce bias (halo effect, racism, etc), shouldn't many judicial proceedings generally be held over telephone, and/or through digital audio-only calls with voice anonymizers?
Continuous functions can be represented by their rational support; in particular, for each real number r, choose a sequence rn of rational numbers converging to r, and let f(r):=limn→∞f(rn).
Therefore, there is an injection from the vector space of continuous functions C to the vector space of all sequences c: since the rationals are countable, enumerate them by (r1,r2,…). Then the sequence (f(r1),f(r2),…) represents continuous function f.
(Just starting to learn microecon, so please feel free to chirp corrections)
How diminishing marginal utility helps create supply/demand curves: think about the uses you could find for a pillow. Your first few pillows are used to help you fall asleep. After that, maybe some for your couch, and then a few spares to keep in storage. You prioritize pillow allocation in this manner; the value of the latter uses is much less than the value of having a place to rest your head.
How many pillows do you buy at a given price point? Well, if you buy any, you'll buy som
... (read more)How can I make predictions in a way which lets me do data analysis? I want to be able to grep / tag questions, plot calibration over time, split out accuracy over tags, etc. Presumably exporting to a CSV should be sufficient. PredictionBook doesn't have an obvious export feature, and its API seems to not be working right now / I haven't figured it out yet.
Trying to collate team shard's prediction results and visualize with plotly, but there's a lot of data processing that has to be done first. Want to avoid the pain in the future.
Consider trying to use Solomonoff induction to reason about P(I see “Canada goes to war with USA" in next year), emphasis added:
... (read more)Team shard is now accepting applications for summer MATS. SERI MATS is now accepting applications for their 4.0 program this summer. In particular, consider applying to the shard theory stream, especially if you have the following interests:
Feel free to apply if you're interested in shard theory more generally, although I expect to mostly supervise empirical work. Feel free to message me if you have questi... (read more)
The policy of truth is a blog post about why policy gradient/REINFORCE suck. I'm leaving a shortform comment because it seems like a classic example of wrong RL theory and philosophy, since reward is not the optimization target. Quotes:
... (read more)Shard-theoretic model of wandering thoughts: Why trained agents won't just do nothing in an empty room. If human values are contextually activated subroutines etched into us by reward events (e.g. "If candy nearby and hungry, then upweight actions which go to candy"), then what happens in "blank" contexts? Why don't people just sit in empty rooms and do nothing?
Consider that, for an agent with lots of value shards (e.g. candy, family, thrill-seeking, music), the "doing nothing" context is a very unstable equilibrium. I think these shards will activate on t... (read more)
I think people go in silence retreats to find out what happens when you take out all the standard busy work. I could imagine the "fresh empty room" and "accustomed empty room" being the difference of calming down for an hour in contrast to a week.
Are there any alignment techniques which would benefit from the supervisor having a severed corpus callosum, or otherwise atypical neuroanatomy? Usually theoretical alignment discourse focuses on the supervisor's competence / intelligence. Perhaps there are other, more niche considerations.
Does anyone have tips on how to buy rapid tests in the US? Not seeing any on US Amazon, not seeing any in person back where I'm from. Considering buying German tests. Even after huge shipping costs, it'll come out to ~$12 a test, which is sadly competitive with US market prices.
Wasn't able to easily find tests on the Mexican and Canadian Amazon websites, and other EU countries don't seem to have them either.
The Baldwin effect
I couldn't find great explanations online, so here's my explanation after a bit of Googling. I welcome corrections from real experts.
Organisms exhibit phenotypic plasticity when they act differently in different environments. The phenotype (manifested traits: color, size, etc) manifests differently, even though two organisms might share the same genotype (genetic makeup).
(This is a basic point on conjunctions, but I don't recall seeing its connection to Occam's razor anywhere)
When I first read Occam's Razor back in 2017, it seemed to me that the essay only addressed one kind of complexity: how complex the laws of physics are. If I'm not sure whether the witch did it, the universes where the witch did it are more complex, and so these explanations are exponentially less likely under a simplicity prior. Fine so far.
But there's another type. Suppose I'm weighing whether the United States government is currently engaged in a v... (read more)
At a poster session today, I was asked how I might define "autonomy" from an RL framing; "power" is well-definable in RL, and the concepts seem reasonably similar.
I think that autonomy is about having many ways to get what you want. If your attainable utility is high, but there's only one trajectory which really makes good things happen, then you're hemmed-in and don't have much of a choice. But if you have many policies which make good things happen, you have a lot of slack and you have a lot of choices. This would be a lot of autonomy.
This has to b... (read more)
In Markov decision processes, state-action reward functions seem less natural to me than state-based reward functions, at least if they assign different rewards to equivalent actions. That is, actions a,a′ at a state s can have different reward R(s,a)≠R(s,a′) even though they induce the same transition probabilities: ∀s′:T(s,a,s′)=T(s,a′,s′). This is unappealing because the actions don't actually have a "noticeable difference" from within the MDP, and the MDP is visitation-distribution-isomorphic to an MDP without the act... (read more)
The answer to this seems obvious in isolation: shaping helps with credit assignment, rescaling doesn't (and might complicate certain methods in the advantage vs Q-value way). But I feel like maybe there's an important interaction here that could inform a mathematical theory of how a reward signal guides learners through model space?
Reasoning about learned policies via formal theorems on the power-seeking incentives of optimal policies
One way instrumental subgoals might arise in actual learned policies: we train a proto-AGI reinforcement learning agent with a curriculum including a variety of small subtasks. The current theorems show sufficient conditions for power-seeking tending to be optimal in fully-observable environments; many environments meet these sufficient conditions; optimal policies aren't hard to compute for the subtasks. One highly transferable heuristic would therefore... (read more)
I prompted GPT-3 with modified versions of Eliezer's Beisutsukai stories, where I modified the "class project" to be about solving intent alignment instead of quantum gravity.
... (read more)Transparency Q: how hard would it be to ensure a neural network doesn't learn any explicit NANDs?
Physics has existed for hundreds of years. Why can you reach the frontier of knowledge with just a few years of study? Think of all the thousands of insights and ideas and breakthroughs that have been had - yet, I do not imagine you need most of those to grasp modern consensus.
... (read more)Idea 1: the tech tree is rather horizontal - for any given question, several approaches and frames are tried. Some are inevitably more attractive or useful. You can view a Markov decision process in several ways - through the Bellman equations, through the structure of the state
When under moral uncertainty, rational EV maximization will look a lot like preserving attainable utility / choiceworthiness for your different moral theories / utility functions, while you resolve that uncertainty.
To prolong my medicine stores by 200%, I've mixed in similar-looking iron supplement placebos with my real medication. (To be clear, nothing serious happens to me if I miss days)