Aprillion (Peter Hozák)
Sounds like an AI would be searching for Pareto optimality to satisfy multiple (types of) objectives in such a case—https://en.wikipedia.org/wiki/Multi-objective_optimization ..
The `nearcasting` links point to an edit version of the page. So for us mortals who don’t have edit access, the page is https://www.lesswrong.com/posts/Qo2EkG3dEMv8GnX8d/ai-strategy-nearcasting
“Can’t we test whether the code works without knowing anything about programming?”
Knowing what to test to reliably decrease uncertainty about “whether the code works” includes knowing “a fair bit” about software engineering.
I agree with the distinction that being a programmer is not the only way how to know about programming, many hiring managers are not programmers themselves, they just have to know a fair bit about software engineering.
These google drive links don’t work for me .. I get redirected to Bin (when logged in to google) or HTTP 404 not found in private window. Could you please generate a link for sharing?
For a programmer who is not into symbolic math, would you say that following summary is accurate enough or did I miss some intuition here:
if an overparametrized network has linear dependency between paramaters, it can perform as if it was an underparametrized network
but the trick is that a flat basin is easier to reach by SGD or similar optimizations processes than if we had to search small targets
hm, I gave it some time, but still confused .. can you name some types of reward that humans have?
I see. These are implemented differently in humans, but my intuition about the implementation details is that “reward signal” as a mathematically abstract object can be modeled by single value even if individual components are physically implemented by different mechanisms, e.g. an animal could be modeled as if was optimizing for a pareto optimum between a bunch of normalized criteria.
reward = S(hugs) + S(food) + S(finishing tasks) + S(free time) - S(pain) ...People spend their time cooking, risk cutting fingers, in order to have better food and build relationships. But no one would want to get cancer to obtain more hugs, presumably not even to increase number of hugs from 0 to 1, so I don’t feel human rewards are completely independent magisteria, there must be some biological mechanism to integrate the different expected rewards and pains into decisions.
Spending energy on computation of expected value can be included in the model, we might decide that we would get lower reward if we overthink the current decision and that would be possible to model as included in the one “reward signal” in theory, even though it would complicate predictability of humans in practice (however, it turns out that humans can be, in fact, hard to predict, so I would say this is a complication of reality, not a useless complication in the model).
Allostasis is a more biologically plausible explanation of “what a brain does” than homeostasis, but to your point: I do think optimizing for happiness and doing kinda-homeostasis are “just the same somehow”.
I have a slightly circular view that the extension of happiness exists as an output of a network with 86 billion neurons and 60 trillion connections, and that it is a thing that the brain can optimize for. Even if the intension of happiness as defined by a few English sentences is not the thing, and even if optimization for slightly different things would be very fragile, the attractor of happiness might be very small and surrounded by dystopian tar pits, I do think it is something that exists in the real world and is worth searching for.
Though if we cannot find any intension that is useful, perhaps other approaches to AI Alignment and not the “search for human happiness” will be more practical.
Why square errors?
[EDITED]: good point, no idea what they meant with “uniform” distribution, the realization for me was about the connection that I can often assume errors are normally distributed, thus L2 is often the obvious choice
see your β there? you assume that people remember to “control for bias” before they apply tools that assume Gaussian error
that is indeed what I should have remembered about the implications of “we can often assume approximately normal distribution” from my statistics course ~15 years ago, but then I saw people complaining about sensitivity to outliers in 1 direction and I failed to make a connection until I dug deeper into my reasoning
I mostly fixed the page by removing quotes from links (edited as markdown in VS Code, 42 links were like [](”...”) and 64 quotes were double-escaped \”) … feel free to double check (I also sent feedback to moderators, maybe they want to check for similar problems on other pages on DB level)
TypeError: Comparing different “solutions”.
How do I know that I generated a program that halts?
a) I can prove to myself that my program halts ⇒ the solution consists of both the program and the proof ⇒ the verification problem is a sub-problem of the generation problem.
b) I followed a trusted process that is guaranteed to produce valid solutions ⇒ the solution consists of both the program and the history that generated the proof of the process ⇒ if the history is not shared between the 2 problems, then you redefined “verification problem” to include generation of all of the necessary history, and that seems to me like a particularly useless point of view (for the discussion of P vs NP, not useless in general).
In the latter point of view, you could say:
Predicate: given a set of numbers, is the first the sum of the latter 2?
Generation problem: provide an example true solution: “30 and prime factors of 221”
Verification problem: verify that 30 is the sum of prime factors of 221
WTF does that have to say about P vs NP? ¯\_(ツ)_/¯
The Usefulness Paradigm
See the
Humortag ¯\_(ツ)_/¯
To be continued in the form of a science fiction story Unnatural Abstractions.
No idea about original reasons, but I can imagine a projected chain of reasoning:
there is a finite number of conjunctive obstacles
if a single person can only think of a subset of obstacles, they will try to solve those obstacles first, making slow(-ish) progress as they discover more obstacles over time
if a group shares their lists, each individual will become aware of more obstacles and will be able to solve more of them at once, potentially making faster progress
Staying in meta-level, if AGI weren’t going to be created “by the ML field”, would you still believe problems on your list cannot possibly be solved within 6-ish months if companies would throw $1b at each of those problems?
Even if competing groups of humans augmented by AI capabilities existing “soon” were trying to solve those problems with combined tools from inside and outside ML field, the foreseeable optimization pressure is not enough for those foreseeable collective agents to solve those known-known and known-unknown problems that you can imagine?
Building a tunnel from 2 sides is the same thing even if those 2 sides don’t see each other initially. I believe some, but not all, approaches will end up seeing each other, that it’s not a bad sign if we are not there yet.
Since we don’t seem to have time to build 2 “tunnels” (independent solutions to alignment), a bad sign would be if we could prove all of the approaches are incompatible with each other, which I hope is not the case.
oh, I didn’t realize there was this event yesterday, I wrote an ai-safety inspired short story independently 😅 if anyone would wish to comment, feel free to leave me a github issue
https://peter.hozak.info/fiction/heat_death/prologue