Wiki Contributions


I created a simple Google Doc for anyone interested in joining/creating a new org to put down their names, contact, what research they're interested in pursuing, and what skills they currently have. Overtime, I think a network can be fostered, where relevant people start forming their own research, and then begin building their own orgs/get funding. 

But it's also an entire School of Thought in Cognitive Science. I feel like DL is the method, but without the understanding that these are based on well-thoughtout, mechanistic rules for how cognition fundamentally works, building potentially toward a unified theory of cognition and behaviour.

I don't have an adequate answer for this, since these models are incomplete. But the way I see it is that these people had a certain way of mathematically reasoning about cognition (Hinton, Rumelhart, McClelland, Smolensky), and that reasoning created most of the breakthroughs we see today in AI (backprop, multi-layed models, etc.) It seems trying to utilize that model of cognition could give rise to new insights about the questions you're asking, attack the problem from a different angle, or help create a grounded paradigm for alignment research to build on.

My answer is a bit vague, but I would say that the current DL curriculum tells you how these things work, but it doesn't go into the reasoning about cognition that allowed these ideas to exist in the first place.

You could say it "predicted" everything post-AlexNet, but it's more that it created the fundamental understanding for everything post-AlexNet to exist in the first place. It's the mathematical models of cognition that all of modern AI is built on. This is how we got back propagation, "hidden" layers, etc.

If you, or if you know someone who wants to try to start doing this, let me know. I've noticed a lot of things in AIS people will say they'd like to see, but then nothing happens. 

I guess my biggest doubt is that a dl-based AI could run interpretability on itself. Large NNs seem to "simulate" a larger network to represent more features, which results in most of the weights occupying a superposition. I don't see how a network could reflect on itself, since it seems that would require an even greater network (which then would require an even greater network, and so on). I don't see how it could eat its own tail, since only interpreting parts of the network would not be enough. It would have to interpret the whole.

The following is a conversation between myself in 2022, and a newer version of myself earlier this year.

On AI Governance and Public Policy

2022 Me: I think we will have to tread extremely lightly with, or, if possible, avoid completely. One particular concern is the idea of gaining public support. Many countries have an interest in pleasing their constituents, so if executed well, this could be extremely beneficial. However, it runs high risk of doing far more damage. One major concern is the different mindset needed to conceptualize the problem. Alerting people to the dangers of Nuclear War is easier: nukes have been detonated, the visual image of incineration is easy to imagine and can be described in detail, and they or their parents have likely lived through nuclear drills in school. This is closer to trying to explain someone the dangers of nuclear war before Hiroshima, before the Manhattan Project, and before even tnt was developed. They have to conceptualize what an explosion even is, not simply imagining an explosion at greater scale. Most people will simply not have the time or the will to try to grasp this problem, so this runs the risk of having people calling for action to a problem they do not understand, which will likely lead to dismissal by AI Researchers, and possibly short-sighted policies that don’t actually tackle the problem, or even make the problem worse by having the guise of accomplishment. To make matters worse, there is the risk of polarization. Almost any concern with political implications that has gained widespread public attention runs a high risk of becoming polarized. We are still dealing with the ramifications of well-intentioned, but misguided, early advocates in the Climate Change movement two decades ago, who set the seeds for making climate policy part of one’s political identity. This could be even more detrimental than a merely uninformed electorate, as it might push people who had no previous opinion on AI to advocate strongly in favor of capabilities acceleration, and to be staunchly against any form of safety policy. Even if executed using the utmost caution, this does not stop other players from using their own power or influence to hijack the movement and lead it astray.

2023 Me: Ah, Me’22,, the things you don’t know! Many of the concerns of Me’22 I think are still valid, but we’re experiencing what chess players might call a “forced move”. People are starting to become alarmed, regardless of what we say or do, so steering that in a direction we want is necessary. The fire alarm is being pushed, regardless, and if we don’t try to show some leadership in that regard, we risk less informed voices and blanket solutions winning-out. The good news is “serious” people are going on “serious” platforms and actually talking about x-risk. Other good news is that, from current polls, people are very receptive to concerns over x-risk and it has not currently fallen into divisive lines (roughly the same % of those concerned fall equally among various different demographics). This is still a difficult minefield to navigate. Polarization could still happen, especially with an Election Year in the US looming. I’ve also been talking to a lot of young people who feel frustrated not having anything actionable to do, and if those in AI Safety don’t show leadership, we might risk (and indeed are already risking), many frustrated youth taking political and social action into their own hands. We need to be aware that EA/LW might have an Ivory Tower problem, and that, even though a pragmatic, strategic, and careful course of action might be better, this might make many feel “shut out” and attempt to steer their own course. Finding a way to make those outside EA/LW/AIS feel included, with steps to help guide and inform them, might be critical to avoiding movement hijacking.

On Capabilities vs. Alignment Research:

2022 Me: While I strongly agree that not increasing capabilities is a high priority right now, I also question if we risk creating a state of inertia. In terms of the realms of safety research, there are very few domains that do not risk increasing capabilities research. And, while capabilities continues to progress every day, we might risk failing to keep up the speed of safety progress simply because every action risks an increase in capabilities. Rather than a “do no harm” principle, I think counterfactuals need to be examined in these situations, where we must consider if there is a greater risk if we *don’t* do research in a certain domain.

2023 Me: Oh, oh, oh! I think Me’22 was actually ahead of the curve on this one. This might still be controversial, but I think many got the “capabilities space” wrong. Many AIS-inspired theories that could increase capabilities are for systems that could be safer, more interpretable, and easier to monitor by default. And by not working on such systems we instead got the much more inscrutable, dangerous models by default, because the more dangerous models are easier. To quote the vape commercials, “safer != safe” but I still quit smoking in favor of electronics because safer is still at least safer. This is probably a moot point now, though, since I think it’s likely too late to create an entirely new paradigm in AI architectures. Hopefully Me’24 will be happy to tell me we found a new, 100% safe and effective new paradigm that everyone’s hopping on. Or maybe he’ll invent it.


[crossposting my reply]

Thank you for taking the time to read and critique this idea. I think this is very important, and I appreciate your thoughtful response.

Regarding how to get current systems to implement/agree to it, I don't think that will be relevant longterm. The mechanisms current institutions use for control I don't think can keep up with AI proliferation. I imagine most existing institutions will still exist, but won't have the capacity to do much once AI really takes off. My guess is, if AI kills us, it will happen after a slow-motion coup. Not any kind of intentional coup by AIs, but from humans just coup'ing themselves because AIs will just be more useful. My idea wouldn't be removing or replacing any institutions, but they just wouldn't be extremely relevant to it. Some governments might try to actively ban use of it, but these would probably be fleeting, if the network actually was superior in collective intelligence to any individual AI. If it made work economically more useful for them, they would want to use it. It doesn't involve removing them, or doing much to directly interfere with things they are doing. Think of it this way, recommendation algorithms on social media have an enormous influence on society, institutions, etc. Some try to ban or control them, but most can still access them if they want to, and no entity really controls them. But no one incorporates the "will of twitter" into their constitution.

The game board isn't any of the things you mention. All the things you mention I don't think have the capacity to do much to change the board. The current board is fundamentally adversarial, where interacting with it increases the power of other players. We've seen this with OpenAI, Anthropic, etc. The new board would be cooperative, at least at a higher level. How do we make the new board more useful than the current one? My best guess would be economic advantage of decentralized compute. We've seen how fast the OpenSource community has been able to make progress. And we've seen how a huge amount of compute gets used doing things like mining bitcoin, even though the compute is wasted on solving math puzzles. Contributing decentralized compute to a collective network could actually have economic value, and I imagine this will happen one way or another, but my concern is it'll end up being for the worse if people aren't actively trying to create a better system. A decentralized network with no safeguards would probably be much worse than anything a major AI company could create.

"But wouldn't the market be distorted by the fact that if everyone ends up dead, there is nobody left alive to collect their prediction-market winnings?"

This seems to be going back to the "one critical shot" approach which I think is a terrible idea that won't possibly work in the real world under any circumstances. This would be a progression overtime, not a case where an AI goes supernova overnight. This might require slower takeoffs, or at least no foom scenarios. Making a new board that isn't adversarial might mitigate the potential of foom. What I proposed was my first naive approach, and I've since thought that maybe it's the collective intelligence of the system that should be increasing, not a singleton AI being trained at the center. Most members in that collective intelligence would initially be humans, and slowly more and more AIs would be a more and more powerful part of the system. I'm not sure here, though. Maybe there's some third option where there's a foundational model at the lowest layer of the network, but it isn't a singular AI in the normal sense. I imagine a singular AI at the center could give rise to agency, and probably break the whole thing.

"It seems to me that having a prediction market for different alignment approaches would be helpful, but would be VERY far from actually having a good plan to solve alignment."

I agree here. They'd only be good at maybe predicting the next iteration of progress, not a fully scalable solution.

"I feel like we share many of the same sentiments -- the idea that we could improve the general level of societal / governmental decision-making using innovative ideas like better forms of voting, quadratic voting & funding, prediction markets, etc"

This would be great, but my guess is they would probably progress too slowly to be useful. Mechanism design that has to deal with currently existing institutions I don't think will happen quickly enough. Technically-enforced design might.

I love the idea of shovel-ready strategies, and think we need to be prepared in the event of a crisis. My issue is even most good strategies seem to just deal with large companies, and don't know how to deal with the likelihood that such power will fall into more and more actors.

"If the boxed superintelligence with the ability to plan usage of weapons when authorized by humans, and other boxed superintelligences able to control robotics in manufacturing cells are on humans side, the advantage for humans could be overwhelming"

As I said, I do not expect boxed AIs to be a thing most will do. We haven't seen it, and I don't expect to see it, because unboxed AIs are superior. This isn't how people in control are approaching the situation, and I don't expect that to change.

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