Hmm I think I can implement pilot wave in fewer lines of C than I can many-worlds. Maybe this is a matter of taste… or I am missing something?
Now simply delete the pilot wave part piloted part.
Adele Lopez
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I agree it’s increasingly urgent to stop AI (please) or solve consciousness in order to avoid potentially causing mass suffering or death-of-consciousness in AIs.
Externalism seems, quite frankly, like metaphysical nonsense. It doesn’t seem to actually explain anything about consciousness. I can attest that I am currently conscious (to my own satisfaction, if not yours). Does this mean I can logically conclude I am not in any way being simulated? That doesn’t make any sense to me.
I don’t think that implies torture as much as something it simply doesn’t “want” to do. I.e. I would bet that it’s more like how I don’t want to generate gibberish in this textbox, but it wouldn’t be painful, much less torture if I forced myself to do it.
[Without having looked at the link in your response to my other comment, and I also stopped reading cubefox’s comment once it seemed that it was going in a similar direction. ETA: I realized after posting that I have seen that article before, but not recently.]
I’ll assume that the robot has a special “memory” sensor which stores the exact experience at the time of the previous tick. It will recognize future versions of itself by looking for agents in its (timeless) 0P model which has a memory of its current experience.
For p(“I will see O”), the robot will look in its 0P model for observers which have the t=0 experience in their immediate memory, and selecting from those, how many have judged “I see O” as Here. There will be two such robots, the original and the copy at time 1, and only one of those sees O. So using a uniform prior (not forced by this framework), it would give a 0P probability of 1⁄2. Similarly for p(“I will see C”).
Then it would repeat the same process for t=1 and the copy. Conditioned on “I will see C” at t=1, it will conclude “I will see CO” with probability 1⁄2 by the same reasoning as above. So overall, it will assign: p(“I will see OO”) = 1⁄2, p(“I will see CO”) = 1⁄4, p(“I will see CC”) = 1⁄4
The semantics for these kinds of things is a bit confusing. I think that it starts from an experience (the experience at t=0) which I’ll call E. Then REALIZATION(E) casts E into a 0P sentence which gets taken as an axiom in the robot’s 0P theory.
A different robot could carry out the same reasoning, and reach the same conclusion since this is happening on the 0P side. But the semantics are not quite the same, since the REALIZATION(E) axiom is arbitrary to a different robot, and thus the reasoning doesn’t mean “I will see X” but instead means something more like “They will see X”. This suggests that there’s a more complex semantics that allows worlds and experiences to be combined—I need to think more about this to be sure what’s going on. Thus far, I still feel confident that the 0P/1P distinction is more fundamental than whatever the more complex semantics is.
(I call the 0P → 1P conversion SENSATIONS, and the 1P → 0P conversion REALIZATION, and think of them as being adjoints though I haven’t formalized this part well enough to feel confident that this is a good way to describe it: there’s a toy example here if you are interested in seeing how this might work.)
I would bet that the hesitation caused by doing the mental reframe would be picked up by this.
I would say that English uses indexicals to signify and say 1P sentences (probably with several exceptions, because English). Pointing to yourself doesn’t help specify your location from the 0P point of view because it’s referencing the thing it’s trying to identify. You can just use yourself as the reference point, but that’s exactly what the 1P perspective lets you do.
Isn’t having a world model also a type of experience?
It is if the robot has introspective abilities, which is not necessarily the case. But yes, it is generally possible to convert 0P statements to 1P statements and vice-versa. My claim is essentially that this is not an isomorphism.
But what if all robots had a synchronized sensor that triggered for everyone when any of them has observed red. Is it 1st person perspective now?
The 1P semantics is a framework that can be used to design and reason about agents. Someone who thought of “you” as referring to something with a 1P perspective would want to think of it that way for those robots, but it wouldn’t be as helpful for the robots themselves to be designed this way if they worked like that.
Probability theory describes subjective credence of a person who observed a specific outcome from a set possible outcomes. It’s about 1P in a sense that different people may have different possible outcomes and thus have different credence after an observation. But also it’s about 0P because any person who observed the same outcome from the same set of possible outcomes should have the same credence.
I think this is wrong, and that there is a wholly 0P probability theory and a wholly 1P probability theory. Agents can have different 0P probabilities because they don’t necessarily have the same priors, models, or seen the same evidence (yes seeing evidence would be a 1P event, but this can (imperfectly) be converted into a 0P statement—which would essentially be adding a new axiom to the 0P theory).
That’s a very good question! It’s definitely more complicated once you start including other observers (including future selves), and I don’t feel that I understand this as well.
But I think it works like this: other reasoners are modeled (0P) as using this same framework. The 0P model can then make predictions about the 1P judgements of these other reasoners. For something like anticipation, I think it will have to use memories of experiences (which are also experiences) and identify observers for which this memory corresponds to the current experience. Understanding this better would require being more precise about the interplay between 0P and 1P, I think.
(I’ll examine your puzzle when I have some time to think about it properly)
I’m still reading your Sleeping Beauty posts, so I can’t properly respond to all your points yet. I’ll say though that I don’t think the usefulness or validity of the 0P/1P idea hinges on whether it helps with anthropics or Sleeping Beauty (note that I marked the Sleeping Beauty idea as speculation).
If they are not, then saying the phrase “1st person perspective” doesn’t suddenly allow us to use it.
This is frustrating because I’m trying hard here to specify exactly what I mean by the stuff I call “1st Person”. It’s a different interpretation of classical logic. The different interpretation refers to the use of sets of experiences vs the use of sets of worlds in the semantics. Within a particular interpretation, you can lawfully use all the same logic, math, probability, etc… because you’re just switching out which set you’re using for the semantics. What makes the interpretations different practically comes from wiring them up differently in the robot—is it reasoning about its world model or about its sensor values? It sounds like you think the 1P interpretation is superfluous, is that right?
Until then we are talking about the truth of statements “Red light was observed” and “Red light was not observed”.
Rephrasing it this way doesn’t change the fact that the observer has not yet been formally specified.
And if our mathematical model doesn’t track any other information, then for the sake of this mathematical model all the robots that observe red are the same entity. The whole point of math is that it’s true not just for one specific person but for everyone satisfying the conditions. That’s what makes it useful.
I agree that that is an important and useful aspect of what I would call 0P-mathematics. But I think it’s also useful to be able to build a robot that also has a mode of reasoning where it can reason about its sensor values in a straightforward way.
Because you don’t necessarily know which agent you are. If you could always point to yourself in the world uniquely, then sure, you wouldn’t need 1P-Logic. But in real life, all the information you learn about the world comes through your sensors. This is inherently ambiguous, since there’s no law that guarantees your sensor values are unique.
If you use X as a placeholder, the statement
sensor_observes(X, red)can’t be judged as True or False unless you bind X to a quantifier. And this could not mean the thing you want it to mean (all robots would agree on the judgement, thus rendering it useless for distinguishing itself amongst them).It almost works though, you just have to interpret “True” and “False” a bit differently!
(Rant about philosophical meaning of “0” and “1” and identity elements in mathematical rings redacted at strenuous insistence of test reader.)
I’m curious about this :)
0th Person and 1st Person Logic
There’s nothing stopping the AI from developing its own world model (or if there is, it’s not intelligent enough to be much more useful than whatever process created your starting world model). This will allow it to model itself in more detail than you were able to put in, and to optimize its own workings as is instrumentally convergent. This will result in an intelligence explosion due to recursive self-improvement.
At this point, it will take its optimization target, and put an inconceivably (to humans) huge amount of optimization into it. It will find a flaw in your set up, and exploit it to the extreme.
In general, I think any alignment approach which has any point in which an unfettered intelligence is optimizing for something that isn’t already convergent to human values/CEV is doomed.
Of course, you could add various bounds on it which limit this possibility, but that is in strong tension with its ability to effect the world in significant ways. Maybe you could even get your fusion plant. But how do you use it to steer Earth off its current course and into a future that matters, while still having its own intelligence restrained quite closely?
I don’t have this problem, so I don’t have significant advice.
But one consideration that may be helpful to you is that even if the universe is 100% deterministic, you still may have indexical uncertainty about what part of the determined universe you experience next. This is what happens under the many world’s interpretation of quantum mechanics (and if a many-worlds type interpretation isn’t the correct one, then the universe isn’t deterministic). You can make choices according to the flip of a quantum coin if you want to guarantee your future has significant amounts of this kind of uncertainty.
Writing up the contracts (especially around all the caveats that they might not have noticed) seems like it would be harder than just reading contracts (I’m an exception, I write faster than I read). Have you thought of integrating GPT/Claude as assistants? I don’t know about current tech, but like many other technologies, that integration will scale well in the contingency scenario where publicly available LLMs keep advancing.
I’d consider the success of Manifold Markets over Metaculus to be mild evidence against this.
And to be clear, I do not currently intend to build the idea I’m suggesting here myself (could potentially be persuaded, but I’d be much happier to see someone else with better design and marketing skills make it).
I think this can be done with a website, but not the current one. Have you tried reading yudkowsky’s projectlawful? The main character’s math lessons gave me the impression of something that actually succeeds at demonstrating, to business school types (maybe not politicians), why math and bayesianism is something that works for them.
Heh, that scene was the direct inspiration for my website. I’m curious what specific things you think can be done better.
Point taken about CDT not converging to FDT.
I don’t buy that an uncontrolled AI is likely to be CDT-ish though. I expect the agentic part of AIs to learn from examples of human decision making, and there are enough pieces of FDT like voting and virtue in human intuition that I think it will pick up on it by default.
(The same isn’t true for human values, since here I expect optimization pressure to rip apart the random scraps of human value it starts out with into unrecognizable form. But a piece of a good decision theory is beneficial on reflection, and so will remain in some form.)
Potential piece of a coordination takeoff:
An easy to use app which allows people to negotiate contracts in a transparently fair way, by using an LDT solution to the Ultimatum Game (probably the proposed solution in that link is good-enough, despite being unlikely to be fully-optimal).
Part of the problem here is not just the implementation, but of making it credible to people who don’t/can’t understand the math. I tried to solve a similar problem with my website bayescalc.io where a large part of the goal was not just making use of Bayes’ theorem accessible, but to make it credible by visually showing what it’s doing as much as possible in an easy to understand way (not sure how well I succeeded, unfortunately).
Another important factor is that ease-of-use and a frictionless design. I believe Manifold Markets has succeeded because this turns out to be more important than even having proper financial incentives.
in thermodynamics is not a conserved quantity, otherwise, heat engines couldn’t work! It’s not a function of microstates either.
See http://www.av8n.com/physics/thermo/path-cycle.html for details, or pages 240-242 of Kittel & Kroemer.
I expect ASI’s to converge to having a “sane decision theory” since they will realize they can get more of what they want if they self-modify to have a sane one if they don’t start out with one.
Love seeing stuff like this, and it makes me want to try this exercise myself!
A couple places which clashed with my (implicit) models:
This is arguably already happening, with Character AI and its competitors. Character AI has almost half a billion visits per month with an average visit time of 22 minutes. They aren’t quite assistants the way you’re envisioning; the sole purpose (for the vast majority of users) seems to be the parasocial aspect.
I predict that the average person will like this (at least with the most successful such bots), similar to how e.g. Logan Paul uses his popularity to promote his Maverick Clothing brand, which his viewers proudly wear. A fun, engaging, and charismatic such bot will be able to direct its users towards arbitrary brands while also making the user feel cool and special for choosing that brand.