Both times my talks went that way (why they did not raise him good—why we could not program AI to be good; cant we keep on eye on them, and so on), but it would take to long to summarise something like 10 minutes dialog, so i am not going to do this. Sorry.
Muyyd
Karma: 1
It is the very same rationale that stands behind assumptions like “why Stockfish won’t execute losing set of moves”—it is just that good at chess. Or better—it is just that smart when it come down to chess.
In this thought experiment the way to go is not to “i see that AGI could likely fail at this step, therefore it will fail” but to keep thinking and inventing better moves for AGI to execute, which won’t be countered as easily. It is an important part of “security mindset” and probably major reason why Eliezer speaks about lack of pessimism in the field.
Lack of clarity when i think about this limits makes hard for me to see how end result will change if we could somehow “stop discounting” them.
It seems to my that we will have to be much more elaborete in describing parameters of this thought experiment. In particular we will have to agree on deeds and real world achivments that hypothetical AI has, so we will both agree to call it AGI (like writing interesting story and making illustrations so this particular research team now have a new revenue strem from selling it online—will this make AI an AGI?). And security conditions (air-gapped server-room?). This will get us closer to understanding “the rationale”.
But then your question is not about AGI but “superintelligent AI” so we will have to do elaborate describing again with new parameters. And that is what i expect Eliezer (alone and with other people) had done a lot. And look what it did to him (this is a joke but at the same time—not). So i will not be an active participant further.
It is not even about a single SAI in some box: compeeting teams, people running copies (legal and not) and changing code, corporate espionage, dirty code...
But how good it can be, realistically? I will be so so much surprised if all this details wont be leaked in next week. May be they will try to make several false leaks to muddle things a bit.
Does ‘ethical safe AI investments’ means ‘to help make AI safer and make some money at the same time’?
Discussion of human generality.
It should be named Discussion of “human generality versus Artificial General Intelligence generality”. And there is exist example of human generality much closer to ‘okay, let me just go reprogram myself a bit, and then I’ll be as adapted to this thing as I am to’ which is not “i am going to read a book or 10 on this topic” but “i am going to meditate for couple of weeks to change my reward circuitry so i will be as interested in coding after as i am interested in doing all side quests in Witcher 3 now”and “i as a human have documented thing known as “Insensitivity to prior probability” so i will go and find 1000 examples of some probabilistic inference in the internet and use them to train sensitivity”. And humans can’t do that. Imagine how “general” humans would be if they could? But if there will be a machine intelligence that can perform a set of tasks so it would be unquestionable named “general”, i would expect for it to be capable of rewriting part of its code with purpose. This is a speculation of course but so is this AGI-entity, whatever it is.
How to think about superintelligence
Here you completely glossed over topic of superintelligence. It is ironic. EY made his prediction about “current influx of money might stumble upon something” and he did not made a single argument in favor. But you wrote list with 11 entries and 5 paragraphs to argue why it is unlikely. But then EY speaks about efficient market hypothesis and chess and efficiency of action… You did not engaged with the specific argument here. I am in agreement that devs have more control over weaker AIs. But SUPER in superintelligence is one of the main points. It is SUPER big point. This is a speculation of course but you and EY both seems in agreement on danger and capabilities of current AIs (and they are not even “general”). I know i did not wrote a good argument here but i do not see a point there to argue against.
The difficulty of alignment
If you stop the human from receiving reward for eating ice cream, then the human will eat chocolate. And so on and so on. look at what stores have to offer that sugary but not ice cream or chocolate. You have to know in advance that liking apples and berries and milk AND HONEY will result in discovering and creating ice cream. In advance—that’s the point of ice cream metaphor.
And by the point that humanity understood connection between sugar and brain and evolution it made cocaine and meth. Because it is not about sugar but reward circuitry. So you have to select for reward circuitry that (and surrounding it apparatus) won’t invent cocaine before it does. In advance. Far in advance.
And some humans like cocaine so much that we could say that their value system cleanly revolves around the one single goal. Or may be there is no equal example of cocaine for AI. But then sugar is still valid. Because we are at “worm intelligence” (?) now in terms of evolution metaphor and it is hard to tell at this point in time will this thing make an ice cream truck sometime (5 to 10 years from now) in the future. But the you wrote a lot about why there is a better examples than evolution. But you also engaged with ice cream argument so i engaged with it too.
Why aren’t other people as pessimistic as Yudkowsky?
As much as i agree with EY, even for me a thought “they should spend 10 times as much in alignment research than in capability increasing research” truly alien and counterintuitive. I mean “redistribution” not “even more money and human resourses”. And for people whose money and employees i am now so brazenly boss here this kind of thinking even more alien and counterintuitive.
I can see that most of your disagreement here comes from different value theory and how fragale human value is. And it is a crux of the matter on “99% and 100%”. That’s why you wrote [4].
I expect there are pretty straightforward ways of leveraging a 99% successful alignment method into a near-100% successful method by e.g., ensembling multiple training runs, having different runs cross-check each other, searching for inputs that lead to different behaviors between different models, transplanting parts of one model’s activations into another model and seeing if the recipient model becomes less aligned, etc
It would be great if you are right. But you wrote [4] and this is prime example of “They’re waiting for reality to hit them over the head”. If you are wrong on value theory then this 1% is what differentiate “inhuman weirdtopia” from “weird utopia” of post-ASI world in the best case.
Overall. You have different views on what is AGI, and what is a superintelligence, and your shard theory of human values. But you missed “what is G means in AGI” argument. And did not engage in “how to think about superintelligence” part (and it is superimportant). And missed “ice cream” argument. The shard theory of values i did not know, may be i will read it now—is seems major point in your line of reasoning.
DeepMind can’t just press a button and generate a million demonstrations of scientific advances, and objectively score how useful each advance is as training data, while relying on zero human input whatsoever.
It can’t now (or it can?). Is there no 100 robots in 100 10x10 meters labs trained with recreating all human technology from stone age and after? If it is cost less than 10 mil then they probably are. This is a joke but i don’t know how offtarget it is.
If we held anything in the nascent field of Artificial General Intelligence to the lesser standards of engineering rigor that apply to a bridge meant to carry a couple of thousand cars, the entire field would be shut down tomorrow.
What are examples that can help to see this tie more clearly? Procedures that works similarly enough to say “we do X during planning and building a bridge and if we do X in AI building...”. Is there are even exist such X that can be applied to enginering a bridge and enginering an AI?
Do we have an idea of how this tables about ML should look like? I dont know about ML that much.
Capabilities advance much faster that alignment, so there is likely no time to do meticulous research. And if you will try to use weak AIs as shortcut to outrun current “capabilities timeline” then you will somehow have to deal with suggestor and verifier problem (with much harder to verify suggestions than a simple math problems) which is not wholly about deception but also filtering somewhat working staff that may steer alignment in right direction. And may be not.
But i agree that this collaboration will be successfully used for patchwork (because shortcuts) alignment of weak AIs to placate general public and politicians. All of this depends on how hard Alignment problem is. Hard as EY think or may be harder or easier.
Humans, presumably, wont have to deal with deception between themselves so if there is sufficient time they can solve Alignment. If pressed for time (as it is now) then they will have to implement less understood solutions because thats the best they will have at the time.
I was hoping that he meant some concrete examples but did not elaborate on this due this being letter in magazine and not a blog post. The only thing that comes to my mind in somehow measure unexpected behavior and if bridge some times lead people in circles then it will be definitely cause for concern and reevaluation of used technics.
Is there are work being done to map (in transcommunity or somewhere else) with precise language this distinct observable objects? Or it is to early and there is no consensus?
Born female, calls themselves “woman” (she/her)
Born female, calls themselves “man” (he/him)
Born female, had pharmacological and/or surgical interventions, calls themselves “man” (he/him)
Born male, calls themselves “man” (he/him)
Born male, calls themselves “woman” (she/her)
Born male, had pharmacological and/or surgical interventions, calls themselves “woman” (she/her)
It is not really a question but i will formulate it as if it was.
Is current LLMs are capable enough to output real rationality exercises (and not a somewhat plausible sounding nonsense) that follows natural way (you dont get a “15% of the cabs are Blue” usually) of how information is presented in life? Can it give 5 new problems to do every day so i can train my sensitivity to prior probability of outcomes? In real life you don’t get percentages. Just a problems like: “can’t find my wallet, was it stolen?” Can they guide through solution process?
There also:P(D/~H) weight of evidence assigned by alternative hypotheses types of problems
How to notice hidden conjunctions (as in Linda problem)
Less obvious problems than probability of pregnancy, given that intercourse has occurred and probability of intercourse, given that pregnancy has occurred.
Syllogisms.
From how discrepancy between temp/resources allocated to alignment research and capability research looks to lay person (to me), the doom scenario is closer to a lottery than to a story. I don’t see why it would be winning number. I 99,999 sure that ASI will be proactive (and all kind of synonyms to this word). It all mostly can be summarised with “fast takeoff” and “human values are fragile”.
>But again… does this really translate to a proportional probability of doom?
If you buy a lottery ticket and get all (all out of n) numbers right, then you have glorious transhumanists utopia (still some people will get very upset). And if you get wrong a single number, then you get a weirdtopia and may be distopia. There is an unknown quantity of numbers to guess, and single ticket cost a billion now (and here enters the discrepancy). Where i get so many losing tickets? From Mind Design Space. There is also and alternative that suggests that space of possibilities is much smaller.
It is not enough to get some alignment, and it seems that we need to get clear on difference between utility maximisers (ASI and AGI) and behavior executors (humans and dogs and monkeys). That’s is where “AGI is proactive (and synonyms)” part based on.
So the probability of doom is proportioned to the probability of buying a losing (not getting all numbers right) ticket.
Evolutionary metaphors is about huge differences between evolutionary pressure in ancestral environment and what we have now: ice cream, transgenders, lesswrong, LLMs, condoms and other contraceptives. What kind of “ice cream” AGI and ASI will make for itself? May be it can be made out of humans, put them in vats and let them dream inputs for GPT10?
Mimicry is product of evolution too. Also—social mimicry.
I have thoughts about reasons for AI to evolve human-like morality too. But i also have thoughts like “this coin turned up heads 3 times in a row, so it must turn tails next”.
Did not expect to see such strawmanning from Hanson. I can easily imagine a post with less misrepresentation. Something like this.
Yudkowsky and the signatories to the moratorium petition worry most about AIs getting “out of control.” At the moment, AIs are not powerful enough to cause us harm, and we hardly know anything about the structures and uses of future AIs that might cause bigger problems. But instead of waiting to deal with such problems when we understand them better and can envision them more concretely later, AI “doomers” want to redirect most if not all computational, capital and human resources from making black-boxed AIs more capable to research avenues that directed to the goal of obtaining precise understanding of inner structure of current AIs now and make this redirection enforced by law including most dire (but legal) methods of law enforcement.
instead of this (original). But that’s would be a different article written by someone else.
Yudkowsky and the signatories to the moratorium petition worry most about AIs getting “out of control.” At the moment, AIs are not powerful enough to cause us harm, and we hardly know anything about the structures and uses of future AIs that might cause bigger problems. But instead of waiting to deal with such problems when we understand them better and can envision them more concretely, AI “doomers” want stronger guarantees now.
Evolution: taste buds and ice cream, sex and condoms… This analogy always was difficult to use in my experience. A year ago i came up with less technical. KPIs (key performance indicators) as inevitable way to communicate goals (to AI) to ultra-high-IQ psycopath-genius who’s into malicious compliance (kinda cant help himself being clone of Nicola Tesla, Einstain and bunch of different people, some of them probably CEO, becouse she can).
I have used it only 2 times and it was way easier than talks about different optimisation processes. And it took me only something like 8 years to come up with!