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gwern comments on Evaluating the feasibility of SI's plan - Less Wrong
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Lots of strawmanning going on here (could somebody else please point these out? please?) but in case it's not obvious, the problem is that what you call "heuristic safety" is difficult. Now, most people haven't the tiniest idea of what makes anything difficult to do in AI and are living in a verbal-English fantasy world, so of course you're going to get lots of people who think they have brilliant heuristic safety ideas. I have never seen one that would work, and I have seen lots of people come up with ideas that sound to them like they might have a 40% chance of working and which I know perfectly well to have a 0% chance of working.
The real gist of Friendly AI isn't some imaginary 100% perfect safety concept, it's ideas like, "Okay, we need to not have a conditionally independent chance of goal system warping on each self-modification because over the course of a billion modifications any conditionally independent probability will sum to ~1, but since self-modification is initially carried out in the highly deterministic environment of a computer chip it looks possible to use crisp approaches that avert a conditionally independent failure probability for each self-modification." Following this methodology is not 100% safe, but rather, if you fail to do that, your conditionally independent failure probabilities add up to 1 and you're 100% doomed.
But if you were content with a "heuristic" approach that you thought had a 40% chance of working, you'll never think through the problem in enough detail to realize that your doom probability is not 60% but ~1, because only somebody holding themselves to a higher standard than "heuristic safety" would ever push their thinking far enough to realize that their initial design was flawed.
People at SI are not stupid. We're not trying to achieve lovely perfect safety with a cherry on top because we think we have lots of luxurious time to waste and we're perfectionists. I have an analysis of the problem which says that if I want something to have a failure probability less than 1, I have to do certain things because I haven't yet thought of any way not to have to do them. There are of course lots of people who think that they don't have to solve the same problems, but that's because they're living in a verbal-English fantasy world in which their map is so blurry that they think lots of things "might be possible" that a sharper map would show to be much more difficult than they sound.
I don't know how to take a self-modifying heuristic soup in the process of going FOOM and make it Friendly. You don't know either, but the problem is, you don't know that you don't know. Or to be more precise, you don't share my epistemic reasons to expect that to be really difficult. When you engage in sufficient detail with a problem of FAI, and try to figure out how to solve it given that the rest of the AI was designed to allow that solution, it suddenly looks that much harder to solve under sloppy conditions. Whereas on the "40% safety" approach, it seems like the sort of thing you might be able to do, sure, why not...
If someday I realize that it's actually much easier to do FAI than I thought, given that you use a certain exactly-right approach - so easy, in fact, that you can slap that exactly-right approach on top of an AI system that wasn't specifically designed to permit it, an achievement on par with hacking Google Maps to play chess using its route-search algorithm - then that epiphany will be as the result of considering things that would work and be known to work with respect to some subproblem, not things that seem like they might have a 40% chance of working overall, because only the former approach develops skill.
I'll leave that as my take-home message - if you want to imagine building plug-in FAI approaches, isolate a subproblem and ask yourself how you could solve it and know that you've solved it, don't imagine overall things that have 40% chances of working. If you actually succeed in building knowledge this way I suspect that pretty soon you'll give up on the plug-in business because it will look harder than building the surrounding AI yourself.
full disclosure: I'm a professional cryptography research assistant. I'm not really interested in AI (yet) but there are obvious similarities when it comes to security.
I have to back Elizer up on the "Lots of strawmanning" part. No professional cryptographer will ever tell you there's hope in trying to achieve "perfect level of safety" of anything and cryptography, unlike AI, is a very well formalized field. As an example, I'll offer a conversation with a student:
How secure is this system? (such question is usually a shorthand for: "What's the probability this system won't be broken by methods X, Y and Z")
The theorem says
What's the probability that the proof of the theorem is correct?
... probably not
Now, before you go "yeah, right", I'll also say that I've already seen this once - there was a theorem in major peer reviewed journal that turned out to be wrong (counter-example found) after one of the students tried to implement it as a part of his thesis - so the probability was indeed not even close to
for any serious N. I'd like to point out that this doesn't even include problems with the implementation of the theory.
It's really difficult to explain how hard this stuff really is to people who never tried to develop anything like it. That's too bad (and a danger) because people who do get it rarely are in charge of the money. That's one reason for the CFAR/rationality movement... you need a tool to explain it to other people too, am I right?
Yup. Usual reference: "Probing the Improbable: Methodological Challenges for Risks with Low Probabilities and High Stakes". (I also have an essay on a similar topic.)
Upvoted for being gwern i.e. having a reference for everything... how do you do that?
Excellent visual memory, great Google & search skills, a thorough archive system, thousands of excerpts stored in Evernote, and essays compiling everything relevant I know of on a topic - that's how.
(If I'd been born decades ago, I'd probably have become a research librarian.)
Would love to read a gwern-essay on your archiving system. I use evernote, org-mode, diigo and pocket and just can't get them streamlined into a nice workflow. If evernote adopted diigo-like highlighting and let me seamlessly edit with Emacs/org-mode that would be perfect... but alas until then I'm stuck with this mess of a kludge. Teach us master, please!
I meant http://www.gwern.net/Archiving%20URLs
Of course your already have an answer. Thanks!
Why do you use diigo and pocket? They do the same thing. Also, with evernote's clearly you can highlight articles.
You weren't asking me, but I use diigo to manage links to online textbooks and tutorials, shopping items, book recommendations (through amazon), and my less important online article to read list. Evernote for saving all of my important read content (and I tag everything). Amazon's send to kindle extension to read longer articles (every once and a while I'll save all my clippings from my kindle to evernote). And then I maintain a personal wiki and collection of writings using markdown with evernote's import folder function in the pc software (I could also do this with a cloud service like gdrive).
I used diigo for annotation before clearly had highlighting. Now, just as you, use diigo for link storage and Evernote for content storage. Diigo annotation has still the advantage that it excerpts the text you highlight. With Clearly if I want to have the highlighted parts I have to find and manually select them again... Also tagging from clearly requires 5 or so clicks which is ridiculous... But I hope it will get fixed.
I plan to use pocket once I get a tablet... it is pretty and convenient, but the most likely to get cut out of the workflow.
Thanks for the evernote import function - I'll look into it, maybe it could make the Evenote - org-mode integration tighter. Even then, having 3 separate systems is not quite optimal...
Thanks, I've read those. Good article.
So, what is our backup plan when proofs turn out to be wrong?
The usual disjunctive strategy: many levels of security, so an error in one is not a failure of the overall system.
What kind of "levels of security" do you have in mind? Can they guard against an error like "we subtly messed up the FAI's decision theory or utility function, and now we're stuck with getting 1/10 of the utility out of the universe that we might have gotten"?
Boxing is an example of a level of security: the wrong actions can trigger some invariant and signal that something went wrong with the decision theory or utility function. I'm sure security could be added to the utility function as well: maybe some sort of conservatism along the lines of the suicide-button invariance, where it leaves the Earth alone and so we get a lower bound on how disastrous a mistake can be. Lots of possible precautions and layers, each of which can be flawed (like Eliezer has demonstrated for boxing) but hopefully are better than any one alone.
That's not 'boxing'. Boxing is a human pitting their wits against a potentially hostile transhuman over a text channel and it is stupid. What you're describing is some case where we think that even after 'proving' some set of invariants, we can still describe a high-level behavior X such that detecting X either indicates global failure with high-enough probability that we would want to shut down the AI after detecting any of many possible things in the reference class of X, or alternatively, we think that X has a probability of flagging failure and that we afterward stand a chance of doing a trace-back to determine more precisely if something is wrong. Having X stay in place as code after the AI self-modifies will require solving a hard open problem in FAI for having a nontrivially structured utility function such that X looks like instrumentally a good thing (your utility function must yield, 'under circumstances X it is better that I be suspended and examined than that I continue to do whatever I would otherwise calculate as the instrumentally right thing). This is how you would describe on a higher level of abstraction an attempt to write a tripwire that immediately detects an attempt to search out a strategy for deceiving the programmers as the goal is formed and before the strategy is actually searched.
There's another class of things Y where we think that humans should monitor surface indicators because a human might flag something that we can't yet reify as code, and this potentially indicates a halt-melt-and-catch-fire-worthy problem. This is how you would describe on a higher level of abstraction the 'Last Judge' concept from the original CEV essay.
All of these things have fundamental limitations in terms of our ability to describe X and monitor Y; they are fallback strategies rather than core strategies. If you have a core strategy that can work throughout, these things can flag exceptions indicating that your core strategy is fundamentally not working and you need to give up on that entire strategy. Their actual impact on safety is that they give a chance of detecting an unsafe approach early enough that you can still give up on it. Meddling dabblers invariably want to follow a strategy of detecting such problems, correcting them, and then saying afterward that the AI is back on track, which is one of those things that is suicide that they think might have an 80% chance of working or whatever.
That was how you did your boxing experiments, but I've never taken it to be so arbitrarily limited in goals, capacities, or strategies on either end. There is no reason you cannot put the AI in a box with some triggers for it venturing into dangerous territory, and this would be merely sane for anyone doing such a thing.
Be specific? What sort of triggers, what sort of dangerous territory? I can't tell if you're still relying on a human to outwit a transhuman or talking about something entirely different.
A trans-human intelligence ought to be able to model human one with ease. This means being able to predict potential triggers and being able to predict how to trick the lack-wit humans on the other end to unwittingly reveal the location of the triggers (even if they don't consciously know it themselves). So the only trigger that matters is one to detect a hint of an intent to get out. Even that is probably too naive, as there could well be other failure modes of which AI deboxing is but a side effect, and our limited human imagination will never going to catch them all. My expectation is that if you rely on safety triggers to bail you out (instead of including them as a desperate last-ditch pray-it-works defense), then you might as well not bother with boxing at all.
That is how they build prisons. It is also how they construct test harnesses. It seems as though using machines to help with security is both obvious and prudent.
Agreed. Maybe I missed it, but I haven't seen you write much on the value of fallback strategies, even understand that (on the understanding that it's small, much less than FAI theory).
There's a little in CFAI sec.5.8.0.4, but not much more.
I understood "boxing" referred to any attempt to keep a SI in a box, while somehow still extracting useful work from it; whether said work is in the form of text strings or factory settings doesn't seem relevant.
Your central point is valid, of course.
I don't see how to make this work. Do we make the AI indifferent about Earth? If so, Earth will be destroyed as a side effect of its other actions. Do we make it block all causal interactions between Earth and the rest of the universe? Then we'll be permanently stuck on Earth even if the FAI attempt turns out to be successful in other regards. Any other ideas?
I had a similar qualm about the suicide button
Nothing comes for free.
Yes, it is this layered approach that the OP is asking about -- I don't see that SI is trying it.
In what way would SI be 'trying it'? The point about multiple layers of security being a good idea for any seed AI project has been made at least as far back as Eliezer's CFAI and brought up periodically since with innovations like the suicide button and homomorphic encryption.
I agree: That sort of innovation can be researched as additional layers to supplement FAI theory
Our question was -- to what extent should SI invest in this sort of thing.