My AI Model Delta Compared To Christiano

I was surprised to read the delta propagating to so many different parts of your worldviews (organizations, goods, markets, etc), and that makes me think that it'd be relatively easier to ask questions today that have quite different answers under your worldviews. The air conditioner one seems like one, but it seems like we could have many more, and some that are even easier than that. Plausibly you know of some because you're quite confident in your position; if so, I'd be interested to hear about them^[1].

At a meta level, I find it pretty funny that so many smart people seem to disagree on the question of whether questions usually have easily verifiable answers.

1. ^{^}

   I realize that part of your position is that this is just really hard to actually verify, but as in the example of objects in your room it feels like there should be examples where this is feasible with moderate amounts of effort. Of course, a lack of consensus on whether something is actually bad if you dive in further could also be evidence for hardness of verification, even if it'd be less clean.

I think it depends on which domain you're delegating in. E.g. physical objects, especially complex systems like an AC unit, are plausibly much harder to validate than a mathematical proof.

In that vein, I wonder if requiring the AI to construct a validation proof would be feasible for alignment delegation? In that case, I'd expect us to find more use and safety from [ETA: delegation of] theoretical work than empirical.

First, I want to flag that I really appreciate how you're making these delta clear and (fairly) simple.

I like this, though I feel like there's probably a great deal more clarity/precision to be had here (as is often the case). 
 

Under my models, if I pick one of these objects at random and do a deep dive researching that object, it will usually turn out to be bad in ways which were either nonobvious or nonsalient to me, but unambiguously make my life worse and would unambiguously have been worth-to-me the cost to make better.

I'm not sure what "bad" means exactly. Do you basically mean, "if I were to spend resources R evaluating this object, I could identify some ways for it to be significantly improved?" If so, I assume we'd all agree that this is true for some amount R, the key question is what that amount is.

I also would flag that you draw attention to the issue with air conditioners. But for the issue of personal items, I'd argue that when I learn more about popular items, most of what I learn are positive things I didn't realize. Like with Chesterton's fence - when I get many well-reviewed or popular items, my impression is generally that there were many clever ideas or truths behind those items that I don't at all have time to understand, let alone invent myself. A related example is cultural knowledge - a la The Secret of Our Success.

When I try out software problems, my first few attempts don't go well for reasons I didn't predict. The very fact that "it works in tests, and it didn't require doing anything crazy" is a significant update. 

Sure, with enough resources R, one could very likely make significant improvements to any item in question - but as a purchaser, I only have resources r << R to make my decisions. My goal is to buy items to make my life better, it's fine that there are potential other gains to be had by huge R values. 

> “verification is easier than generation [LW(p) · GW(p)]”

I feel like this isn't very well formalized. I think I agree with this comment [LW(p) · GW(p)]on that post. I feel like you're saying, "It's easier to generate a simple thing than verify all possible things", but Paul and co are saying more like, "It's easier to verify/evaluate a thing of complexity C than generate a think of complexity C, in many important conditions", or, "There are ways of delegating many tasks where the evaluation work required would be less than that of doing the work yourself, in order to get a result of a certain level of quality." 

I think that Paul's take (as I understand it) seems like a fundamental aspect about the working human world. Humans generally get huge returns from not inventing the wheel all the time, and deferring to others a great deal. This is much of what makes civilization possible. It's not perfect, but it's much better than what individual humans could do by themselves. 

> Under my current models, I expect that, shortly after AIs are able to autonomously develop, analyze and code numerical algorithms better than humans, there’s going to be some pretty big (like, multiple OOMs) progress in AI algorithmic efficiency (even ignoring a likely shift in ML/AI paradigm once AIs start doing the AI research)

I appreciate the precise prediction, but don't see how it exactly follows. This seems more like a question of "how much better will early AIs be compared to current humans", than one deeply about verification/generation. Also, I'd flag that in many worlds, I'd expect that pre-AGI AIs could do a lot of this code improvement - or they already have - so it's not clear exactly how big a leap the "autonomously" is doing here. 

--- 

I feel like there are probably several wins to be had by better formalizing these concepts more. They seem fairly cruxy/high-delta in the debates on this topic.

I would naively approach some of this with some simple expected value/accuracy lens. There are many assistants (including AIs) that I'd expect would improve the expected accuracy on key decisions, like knowing which AI systems to trust. In theory, it's possible to show a bunch of situations where delegation would be EV-positive. 

That said, a separate observer could of course claim that one using the process above would be so wrong as to be committing self-harm. Like, "I think that when you would try to use delegation, your estimates of impact are predictably wrong in ways that would lead to you losing." But this seems like mainly a question about "are humans going to be predictably overconfident in a certain domain, as seen by other specific humans". 

I put approximately-zero probability on the possibility that Paul is basically right on this delta; I think he’s completely out to lunch.

Very strong claim which the post doesn't provide nearly enough evidence to support

I'm curious what you think of Paul's points (2) and (3) here [LW(p) · GW(p)]:

• Eliezer often talks about AI systems that are able to easily build nanotech and overpower humans decisively, and describes a vision of a rapidly unfolding doom from a single failure. This is what would happen if you were magically given an extraordinarily powerful AI and then failed to aligned it, but I think it’s very unlikely what will happen in the real world. By the time we have AI systems that can overpower humans decisively with nanotech, we have other AI systems that will either kill humans in more boring ways or else radically advanced the state of human R&D. More generally, the cinematic universe of Eliezer’s stories of doom doesn’t seem to me like it holds together, and I can’t tell if there is a more realistic picture of AI development under the surface.
• One important factor seems to be that Eliezer often imagines scenarios in which AI systems avoid making major technical contributions, or revealing the extent of their capabilities, because they are lying in wait to cause trouble later. But if we are constantly training AI systems to do things that look impressive, then SGD will be aggressively selecting against any AI systems who don’t do impressive-looking stuff. So by the time we have AI systems who can develop molecular nanotech, we will definitely have had systems that did something slightly-less-impressive-looking.

And specifically to what degree you think future AI systems will make "major technical contributions" that are legible to their human overseers before they're powerful enough to take over completely.

You write:

I expect that, shortly after AIs are able to autonomously develop, analyze and code numerical algorithms better than humans, there’s going to be some pretty big (like, multiple OOMs) progress in AI algorithmic efficiency (even ignoring a likely shift in ML/AI paradigm once AIs start doing the AI research). That’s the sort of thing which leads to a relatively discontinuous takeoff.

But how likely do you think it is that these OOM jumps happen before vs. after a decisive loss of control? 

My own take: I think there will probably be enough selection pressure and sophistication in primarily human-driven R&D processes alone to get to uncontrollable AI. Weak AGIs might speed the process along in various ways, but by the time an AI itself can actually drive the research process autonomously (and possibly make discontinuous progress), the AI will already also be capable of escaping or deceiving its operators pretty easily, and deception / escape seems likely to happen first for instrumental reasons.

But my own view isn't based on the difficulty of verification vs. generation, and I'm not specifically skeptical of bureaucracies / delegation. Doing bad / fake R&D that your overseers can't reliably check does seem somewhat easier than doing real / good R&D, but not always, and as a strategy seems like it would usually be dominated by "just escape first and do your own thing".

I expect you still believe P != NP?

“keyboard and monitor I’m using right now, a stack of books, a tupperware, waterbottle, flip-flops, carpet, desk and chair, refrigerator, sink, etc. Under my models, if I pick one of these objects at random and do a deep dive researching that object, it will usually turn out to be bad in ways which were either nonobvious or nonsalient to me, but unambiguously make my life worse"

But, I think the negative impacts that these goods have on you are (mostly) realized on longer timescales - say, years to decades. If you’re using a chair that is bad for your posture, the impacts of this are usually seen years down the line when your back starts aching. Or if you keep microwaving tupperware, you may end up with some pretty nasty medical problems, but again, decades down the line. 

The property of an action having long horizons until it can be verified as good or bad for you makes delegating to smarter-than-you systems dangerous. My intuition is that there are lots of tasks that could significantly accelerate alignment research that don’t have this property, examples being codebase writing (unit tests can provide quick feedback), proof verification etc. In fact, I can’t think of many research tasks in technical fields that have month/year/decade horizons until they can be verified - though maybe I’ve just not given it enough thought.

In HCH, the human user does a little work then delegates subquestions/subproblems to a few AIs, which in turn do a little work then delegate their subquestions/subproblems to a few AIs, and so on until the leaf-nodes of the tree receive tiny subquestions/subproblems which they can immediately solve.

This does not agree with my understanding of what HCH is at all. HCH is a definition of an abstract process for thought experiments, much like AIXI is. It's defined as the fixed point of some iterative process of delegation expanding out into a tree. It's also not something you could actually implement, but it's a platonic form like "circle" or "integral".

This has nothing to do with the way an HCH-like process would be implemented. You could easily have something that's designed to mimic HCH but it's implemented as a single monolithic AI system.

As you're doing these delta posts, do you feel like it's changing your own positions at all?

For example, reading this one what strikes me is that what's portrayed as the binary sides of the delta seem more like positions near the edges of a gradient distribution, and particularly one that's unlikely to be uniform across different types of problems.

To my eyes the most likely outcome is a situation where you are both right.

Where there are classes of problems where verification is easy and delegation is profitable, and classes of problems where verification will be hard and unsupervised delegation will be catastrophic (cough glue on pizza).

If we are only rolling things up into aggregate pictures of the average case across all problems, I can see the discussion filtering back into those two distinct deltas, but a bit like flip-flops and water bottles, the lack of nuance obscures big picture decision making.

So I'm curious if as you explore and represent the opposing views to your own, particularly as you seem to be making effort to represent without depicting them as straw person arguments, if your own views have been deepening and changing through the process?

Under my models, if I pick one of these objects at random and do a deep dive researching that object, it will usually turn out to be bad in ways which were either nonobvious or nonsalient to me, but unambiguously make my life worse and would unambiguously have been worth-to-me the cost to make better.

Crucially, this is true only because you're relatively smart for a human: smarter than many of the engineers that designed those objects, and smarter than most or all of the committee-of-engineers that designed those objects. You can come up with better solutions then they did, if you have a similar level of context.

But that's not true of most humans. Most humans, if they did a deep dive into those objects wouldn't notice the many places where there is substantial room for improvement. Just like most humans don't spontaneously recognized blatant-to-me incentive problems in government design (and virtually every human institution), and just as I often wouldn't be able to tell that a software solution was horrendously badly architected, at least without learning learning a bunch of software engineering in addition to doing a deep dive into this particular program. 

It’s a failure of ease of verification: because I don’t know what to pay attention to, I can’t easily notice the ways in which the product is bad.

Is there an opposite of the "failure of ease of verification" that would add up to 100% if you would categorize the whole of reality into 1 of these 2 categories? Say in a simulation, if you attributed every piece of computation into following 2 categories, how much of the world can be "explained by" each category?

• make sure stuff "works at all and is easy to verify whether it works at all"
• stuff that works must be "potentially better in ways that are hard to verify"

Examples:

• when you press the "K" key on your keyboard for 1000 times, it will launch nuclear missiles ~0 times and the K key will "be pressed" ~999 times
• when your monitor shows you the pixels for a glyph of the letter "K" 1000 times, it will represent the planet Jupyter ~0 times and "there will be" the letter K ~999 times
• in each page in your stack of books, the character U+0000 is visible ~0 times and the letter A, say ~123 times
• tupperware was your own purchase and not gifted by a family member? I mean, for which exact feature would you pay how much more?!?
• you can tell whether a water bottle contains potable water and not sulfuric acid
• carpet, desk, and chair haven't spontaneously combusted (yet?)
• the refrigerator doesn't produce any black holes
• (flip-flops are evil and I don't want to jinx any sinks at this time)

That propagates into a huge difference in worldviews. Like, I walk around my house and look at all the random goods I’ve paid for - the keyboard and monitor I’m using right now, a stack of books, a tupperware, waterbottle, flip-flops, carpet, desk and chair, refrigerator, sink, etc. Under my models, if I pick one of these objects at random and do a deep dive researching that object, it will usually turn out to be bad in ways which were either nonobvious or nonsalient to me, but unambiguously make my life worse and would unambiguously have been worth-to-me the cost to make better.

Based on my 1 deep dive on pens a few years ago this seems true. Maybe that is too high dimensional and too unfocused a post, but maybe there should be a post on "best X of every common product people use every day"? And then we somehow filter for people with actual expertise? Like for pens you want to go with the recommendations of "the pen addict".

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