Hypothesis I is testable! Instead of prompting with a string of actual tokens, use a “virtual token” (a vector v from the token embedding space) in place of ‘ petertodd’.
It would be enlightening to rerun the above experiments with different choices of v:
A random vector (say, iid Gaussian )
A random sparse vector
(apple+banana)/2
(villain-hero)+0.1*(bitcoin dev)
Etc.
How can the Continuum Hypothesis be independent of the ZFC axioms? Why does the lack of “explicit” examples of sets with a cardinality between that of the naturals and that of the reals not guarantee that there are no examples at all? What would an “implicit” example even mean?
It means that you can’t reach a contradiction by starting with “Let S be a set of intermediate cardinality” and following axioms of ZFC.
All the things you know and love doing with sets —intersection, union, choice, comprehension, Cartesian product, power set — you can do those things with S and nothing will go wrong. S “behaves like a set”, you’ll never catch it doing something unsetlike.
Another way to say this is: There is a model of ZFC that contains a set S of intermediate cardinality. (There is also a model of ZFC that doesn’t. And I’m sympathetic to the view that—since there’s no explicit construction of S -we’ll never encounter an S in the wild and so the model not including S is simpler and better.)
Caveat: All of the above rests on the usual unstated assumption that ZFC is consistent! Because it’s so common to leave it unstated, this assumption is questioned less than maybe it should be, given that ZFC can’t prove its own consistency.
Young kids don’t make a clear distinction between fantasy and reality. The process of coming to reject the Santa myth helps them clarify the distinction.
It’s interesting to me that young kids function as well as they do without the notions of true/false, real/pretend! What does “belief” even mean in that context? They change their beliefs from minute to minute to suit the situation.
Even for most adults, most beliefs are instrumental: We only separate true from false to the extent that it’s useful to do so!
I agree this is a problem. We need to keep it guessing about the simulation target. Some possible strategies:
Add noise, by grading it incorrectly with some probability.
On training point , reward it for matching for a random value of .
Make humans a high-dimensional target. In my original proposal, was strictly stronger as increases, but we could instead take to be a committee of experts. Say there are 100 types of relevant expertise. On each training point, we reward the model for matching a random committee of 50 experts selected from the pool of 100. It’s too expensive simulate all (100 choose 50) possible committees!
None of these randomization strategies is foolproof in the worst case. But I can imagine proving something like “the model is exponentially unlikely to learn an simulator” where is now the full committee of all 100 experts. Hence my question about large deviations.
There’s a trap here where the more you think about how to prevent bad outcomes from AGI, the more you realize you need to understand current AI capabilities and limitations, and to do that there is no substitute for developing and trying to improve current AI!
A secondary trap is that preventing unaligned AGI probably will require lots of limited aligned helper AIs which you have to figure out how to build, again pushing you in the direction of improving current AI.
The strategy of “getting top AGI researchers to stop” is a tragedy of the commons: They can be replaced by other researchers with fewer scruples. In principle TotC can be solved, but it’s hard. Assuming that effort succeeds, how feasible would it be to set up a monitoring regime to prevent covert AGI development?
AGI timeline is not my motivation, but the links look helpful, thanks!
Stupid proposal: Train the reporter not to deceive us.
We train it with a weak evaluator H_1 who’s easy to fool. If it learns an H_1 simulator instead of direct reporter, then we punish it severely and repeat with a slightly stronger H_2. Human level is H_100.
It’s good at generalizing, so wouldn’t it learn to never ever deceive?
Did you ever try this experiment? I’m really curious how it turned out!
Anecdotal example of trade with ants (from a house in Bali, as described by David Abrams):
The daily gifts of rice kept the ant colonies occupied–and, presumably, satisfied. Placed in regular, repeated locations at the corners of various structures around the compound, the offerings seemed to establish certain boundaries between the human and ant communities; by honoring this boundary with gifts, the humans apparently hoped to persuade the insects to respect the boundary and not enter the buildings.
if you are smarter at solving math tests where you have to give the right answer, then that will make you worse at e.g. solving math “tests” where you have to give the wrong answer.
Is that true though? If you’re good at identifying right answers, then by process of elimination you can also identify wrong answers.
I mean sure, if you think you’re supposed to give the right answer then yes you will score poorly on a test where you’re actually supposed to give the wrong answer. Assuming you get feedback, though, you’ll soon learn to give wrong answers and then the previous point applies.
“Train the predictor on lots of cases until it becomes incredibly good; then train the reporter only on the data points with missing information, so that it learns to do direct translation from the predictor to human concepts; then hope that reporter continues to do direct translation on other data points.”
That’s different from what I had in mind, but better! My proposal had two separate predictors, and what it did is reduce the human strong predictor OI problem (OI = “ontology identification”, defined in the ELK paper) to the weak predictor strong predictor OI problem. The latter problem might be easier, but I certainly don’t see how to solve it!
Your version is better because it bypasses the OI problem entirely (the two predictors are the same!)
Now for the problem you point out:
The problem as I see it is that once the predictor is good enough that it can get data points right despite missing crucial information,
Here’s how I propose to block this. Let be a high-quality video and an action sequence. Given this pair, the predictor outputs a high-quality video of its predicted outcome. Then we downsample and to low-quality and , and train the reporter on the tuple where is the human label informed by the high-quality and .
We choose training data such that
1. The human can label perfectly given the high-quality data ; and
2. The predictor doesn’t know for sure what is happening from the low-quality data alone.
Let’s compare the direct reporter (which truthfully reports the probability that the diamond is in the room, as estimated by the predictor who only has the low-quality data) with the human simulator.
The direct reporter will not get perfect reward, since the predictor is genuinely uncertain. Sometimes the predictor’s probability is strictly between 0 and 1, so it gets some loss.
But the human simulator will do worse than the direct reporter, because it has no access to the high-quality data. It can simulate what the human would predict from the low-quality data, but that is strictly worse than what the predictor predicts from the low-quality data.
I agree that we still have to “hope that reporter continues to do direct translation on other data points”, and maybe there’s a counterexample that shows it won’t? But at the very least the human simulator is no longer a failure mode!
Idea: Withhold Material Information
We’re going to prevent the reporter from simulating a human, by giving the human material information that the reporter doesn’t have.
Consider two camera feeds:
Feed 1 is very low resolution, and/or shows only part of the room.
Feed 2 is high resolution, and/or shows the whole room.
We train a weak predictor using Feed 1, and a strong predictor using Feed 2.
We train a reporter to report the beliefs of the weak predictor, using scenarios labeled by humans with the aid of the strong predictor. The humans can correctly label scenarios that are hard to figure out with Feed 1 alone, by asking the strong predictor to show them its predicted Feed 2. The reporter is unable to simulate the human evaluators because it doesn’t see Feed 2. Even if it has perfect knowledge of the human Bayes net, it doesn’t know what to plug in to the knowledge nodes!
Then we fine-tune the reporter to work with the strong predictor to elicit its beliefs. I haven’t figured out how to do this last step, maybe it’s hard?
However, there is some ambiguity, as at temperature 0, ‘ petertodd’ is saving the world
All superheroes are alike; each supervillain is villainous in its own way.
Yep, it’s a funny example of trade, in that neither party is cognizant of the fact that they are trading!
I agree that Abrams could be wrong, but I don’t take the story about “spirits” as much evidence: A ritual often has a stated purpose that sounds like nonsense, and yet the ritual persists because it confers some incidental benefit on the enactor.
“no free lunch in intelligence” is an interesting thought, can you make it more precise?
Intelligence is more effective in combination with other skills, which suggests “free lunch” as opposed to tradeoffs.
Thanks for the comment!
I know you are saying it predicts *uncertainly,* but we still have to have some framework to map uncertainty to a state, we have to round one way or the other. If uncertainty avoids loss, the predictor will be preferentially inconclusive all the time.
There’s a standard trick for scoring an uncertain prediction: It outputs its probability estimate p that the diamond is in the room, and we score it with loss if the diamond is really there, otherwise. Truthfully reporting p minimizes its loss.
So we could sharpen case two and say that sometimes the AI’s camera intentionally lies to it on some random subset of scenarios
You’re saying that giving it less information (by replacing its camera feed with a lower quality feed) is equivalent to sometimes lying to it? I don’t see the equivalence!
if you overfit on preventing human simulation, you let direct translation slip away.
That’s an interesting thought, can you elaborate?
Great links, thank you!!
So your focus was specifically on the compute performed by animal brains.
I expect total brain compute is dwarfed by the computation inside cells (transcription & translation). Which in turn is dwarfed by the computation done by non-organic matter to implement natural selection. I had totally overlooked this last part!
Question: Would a proposal be ruled out by a counterexample even if that counterexample is exponentially unlikely?
I’m imagining a theorem, proved using some large deviation estimate, of the form: If the model satisfies hypotheses XYZ, then it is exponentially unlikely to learn W. Exponential in the number of parameters, say. In which case, we could train models like this until the end of the universe and be confident that we will never see a single instance of learning W.
Great points about not wanting to summon the doom memeplex!
It sounds like your proposed narrative is not doom but disempowerment: humans could lose control of the future. An advantage of this narrative is that people often find it more plausible: many more scenarios lead to disempowerment than to outright doom.
I also personally use the disempowerment narrative because it feels more honest to me: my P(doom) is fairly low but my P(disempowerment) is substantial.
I’m curious though whether you’ve run into the same hurdle I have, namely that people already feel disempowered! They know that some humans somewhere have some power, but it’s not them. So the Davos types will lose control of the future? Many people express indifference or even perverse satisfaction at this outcome.
A positive narrative of empowerment could be much more potent, if only I knew how to craft it.