Posts
Comments
The cap per trader per market on PredictIt is $850
This anti-China attitude also seems less concerned with internal threats to democracy. If super-human AI becomes a part of the US military-industrial complex, even if we assume they succeed at controlling it, I find it unlikely that the US can still be described as a democracy.
It's not hard to criticize the "default" strategy of AI being used to enforce US hegemony, what seems hard is defining a real alternative path for AI governance that can last, and achieve the goal of preventing dangerous arms races long-term. The "tool AI" world you describe still needs some answer to rising tensions between the US and China, and that answer needs to be good enough not just for people concerned about safety, but good enough for the nationalist forces which are likely to drive US foreign policy.
then we can all go home, right?
Doesn't this just shift what we worry about? If control of roughly human level and slightly superhuman systems is easy, that still leaves:
- Human institutions using AI to centralize power
- Conflict between human-controlled AI systems
- Going out with a whimper scenarios (or other multi-agent problems)
- Not understanding the reasoning of vastly superhuman AI (even with COT)
What feels underexplored to me is: If we can control roughly human-level AI systems, what do we DO with them?
I've noticed that a lot of LW comments these days will start by thanking the author, or expressing enthusiasm or support before getting into the substance. I have the feeling that this didn't use to be the case as much. Is that just me?
can it maintain its own boundary over time, in the face of environmental disruption? Some agents are much better at this than others.
I really wish there was more attention paid to this idea of robustness to environmental disruption. It also comes up in discussions of optimization more generally (not just agents). This robustness seems to me like the most risk-relevant part of all this, and seems like it might be more important than the idea of a boundary. Maybe maintaining a boundary is a particularly good way for a process to protect itself from disruption, but I notice some doubt that this idea is most directly getting at what is dangerous about intelligent/optimizing systems, whereas robustness to environmental disruption feels like it has the potential to get at something broader that could unify both agent based risk narratives and non-agent based risk narratives.
Thanks!
Replying in order:
- Currently completely random yes. We experimented with a more intelligent "daemon manager," but it was hard to make one which didn't have a strong universal preference for some daemons over others (and the hacks we came up with to try to counteract this favoritism became increasingly convoluted). It would be great to find an elegant solution to this.
- Good point! Thanks for letting people know.
- I've also had that problem, and whenever I look through the suggestions I often feel like there were many good questions/comments that got pruned away. The reason to focus on surprise was mainly to avoid the repetitiveness caused by mode collapse, where the daemon gets "stuck" giving the same canned responses. This is a crude instrument though, since as you say, just because a response isn't surprising, doesn't mean it isn't useful.
A note to anyone having trouble with their API key:
The API costs money, and you have to give them payment information in order to be able to use it. Furthermore, there are also apparently tiers which determine the rate limits on various models (https://platform.openai.com/docs/guides/rate-limits/usage-tiers).
The default chat model we're using is gpt-4o, but it seems like you don't get access to this model until you hit "tier 1," which happens when you have spent at least $5 on API requests. If you haven't used the API before, and think this might be your issue, you can try using gpt-3.5-turbo which is definitely available at the "free tier," though without giving them any payment information you will still run into an issue as this model also costs money. You can also log into your account and go here to buy at least $5 in OpenAI API credits: https://platform.openai.com/settings/organization/billing/overview
Finally, if you are working at an organization which is providing you API credits, you need to make sure to set that organization as your default organization here: https://platform.openai.com/settings/profile?tab=api-keys If you don't want to do this, in the Pantheon settings you can also provide an organization ID, which you should be able to find here: https://platform.openai.com/settings/organization/general
Sorry for anyone who has found this confusing. Please don't hesitate to reach out if you continue to have trouble.
Daimons are lesser divinities or spirits, often personifications of abstract concepts, beings of the same nature as both mortals and deities, similar to ghosts, chthonic heroes, spirit guides, forces of nature, or the deities themselves.
It's a nod to ancient Greek mythology: https://en.wikipedia.org/wiki/Daimon
a daemon is a computer program that runs as a background process, rather than being under the direct control of an interactive user.
Also nodding to its use as a term for certain kinds of computer programs: https://en.wikipedia.org/wiki/Daemon_(computing)
Hey Alexander! They should appear fairly soon after you've written at least 2 thoughts. The app will also let you know when a daemon is currently developing a response. Maybe there is an issue with your API key? There should be some kind of error message indicating why no daemons are appearing. Please DM me if that isn't the case and we'll look into what's going wrong for you.
We are! There's a bunch of features we'd like to add, and for the most part we expect to be moving on to other projects (so no promises on when we'll get to it), but we do absolutely want to add support for other models.
There is a field called Forensic linguistics where detectives use someone's "linguistic fingerprint" to determine the author of a document (famously instrumental in catching Ted Kaczynski by analyzing his manifesto). It seems like text is often used to predict things like gender, socioeconomic background, and education level.
If LLMs are superhuman at this kind of work, I wonder whether anyone is developing AI tools to automate this. Maybe the demand is not very strong, but I could imagine, for example, that an authoritarian regime might have a lot of incentive to de-anonymize people. While a company like OpenAI seems likely to have an incentive to hide how much the LLM actually knows about the user, I'm curious where anyone would have a strong incentive to make full use of superhuman linguistic analysis.
I wish there were an option in the settings to opt out of seeing the LessWrong reacts. I personally find them quite distracting, and I'd like to be able to hover over text or highlight it without having to see the inline annotations.
How would (unaligned) superintelligent AI interact with extraterrestrial life?
Humans, at least, have the capacity for this kind of "cosmopolitanism about moral value." Would the kind of AI that causes human extinction share this? It would be such a tragedy if the legacy of the human race is to leave behind a kind of life that goes forth and paves the universe, obliterating any and all other kinds of life in its path.
Some thoughts:
First, it sounds like you might be interested the idea of d/acc from this Vitalik Buterin post, which advocates for building a "defense favoring" world. There are a lot of great examples of things we can do now to make the world more defense favoring, but when it comes to strongly superhuman AI I get the sense that things get a lot harder.
Second, there doesn't seem like a clear "boundaries good" or "boundaries bad" story to me. Keeping a boundary secure tends to impose some serious costs on the bandwidth of what can be shared across it. Pre-industrial Japan maintained a very strict boundary with the outside world to prevent foreign influence, and the cost was falling behind the rest of the world technologically.
My left and right hemispheres are able to work so well together because they don't have to spend resources protecting themselves from each other. Good cooperative thinking among people also relies on trust making it possible to loosen boundaries of thought. Weakening borders between countries can massively increase trade, and also relies on trust between the participant countries. The problem with AI is that we can't give it that level of trust, and so we need to build boundaries, but the ultimate cost seems to be that we eventually get left behind. Creating the perfect boundary that only lets in the good and never the bad, and doesn't incur a massive cost, seems like a really massive challenge and I'm not sure what that would look like.
Finally, when I think of Cyborgism, I'm usually thinking of it in terms of taking control over the "cyborg period" of certain skills, or the period of time where human+AI teams still outperform either humans or AIs on their own. In this frame, if we reach a point where AIs broadly outperform human+AI teams, then baring some kind of coordination, humans won't have the power to protect themselves from all the non-human agency out there (and it's up to us to make good use of the cyborg period before then!)
In that frame, I could see "protecting boundaries" intersecting with cyborgism, for example in that AI could help humans perform better oversight and guard against disempowerment around the end of some critical cyborg period. Developing a cyborgism that scales to strongly superhuman AI has both practical challenges (like the kind neuralink seeks to overcome), as well as requiring you to solve it's own particular version of alignment problem (e.g. how can you trust the AI you are merging with won't just eat your mind).
Thank you, it's been fixed.
In terms of LLM architecture, do transformer-based LLMs have the ability to invent new, genuinely useful concepts?
So I'm not sure how well the word "invent" fits here, but I think it's safe to say LLMs have concepts that we do not.
Recently @Joseph Bloom was showing me Neuronpedia which catalogues features found in GPT-2 by sparse autoencoders, and there were many features which were semantically coherent, but I couldn't find a word in any of the languages I spoke that could point to these concepts exactly. It felt a little bit like how human languages often have words that don't translate, and this made us wonder whether we could learn useful abstractions about the world (e.g. that we actually import into English) by identifying the features being used by LLMs.
You might enjoy this post which approaches this topic of "closing the loop," but with an active inference lens: https://www.lesswrong.com/posts/YEioD8YLgxih3ydxP/why-simulator-ais-want-to-be-active-inference-ais
A main motivation of this enterprise is to assess whether interventions in the realm of Cooperative AI, that increase collaboration or reduce costly conflict, can seem like an optimal marginal allocation of resources.
After reading the first three paragraphs, I had basically no idea what interventions you were aiming to evaluate. Later on in the text, I gather you are talking about coordination between AI singletons, but I still feel like I'm missing something about what problem exactly you are aiming to solve with this. I could have definitely used a longer, more explain-like-I'm-five level introduction.
That sounds right intuitively. One thing worth noting though is that most notes get very few ratings, and most users rate very few notes, so it might be trickier than it sounds. Also if I were them I might worry about some drastic changes in note rankings as a result of switching models. Currently, just as notes can become helpful by reaching a threshold of 0.4, they can lose this status by dropping below 0.39. They may also have to manually pick new thresholds, as well as maybe redesign the algorithm slightly (since it seems that a lot of this algorithm was built via trial and error, rather than clear principles).
"Note: for now, to avoid overfitting on our very small dataset, we only use 1-dimensional factors. We expect to increase this dimensionality as our dataset size grows significantly."
This was the reason given from the documentation.
Thanks for pointing that out. I've added some clarification.
That sounds cool! Though I think I'd be more interested using this to first visualize and understand current LW dynamics rather than immediately try to intervene on it by changing how comments are ranked.
I'm confused by the way people are engaging with this post. That well functioning and stable democracies need protections against a "tyranny of the majority" is not at all a new idea; this seems like basic common sense. The idea that the American civil war was precipitated by the South perceiving an end to their balance of power with the North also seems pretty well accepted. Furthermore, there are lots of other things that make democratic systems work well: e.g. a system of laws/conflict resolution or mechanisms for peaceful transfers of power.
fyi, the link chatgptiseatingtheworld.com does not have a secure connection.
Even if you suppose that there are extremely good non-human futures, creating a new kind of life and unleashing it upon the world is a huge deal, with enormous ethical/philosophical implications! To unilaterally make a decision that would drastically affect (and endanger) the lives of everyone on earth (human and non-human) seems extremely bad, even if you had very good reasons to believe that this ends well (which as far as I can tell, you don't).
I have sympathy for the idea of wanting AI systems to be able to pursue lives they find fulfilling and to find their own kinds of value, for the same reason I would, upon encountering alien life, want to let those aliens find value in their own ways.
But your post seems to imply that we should just give up on trying to positively affect the future, spend no real thought on what would be the biggest decision ever made in all of history, all based on a hunch that everything is guaranteed to end well no matter what we do? This perspective, to me, comes off as careless, selfish, and naive.
I just ran into a post which, if you are interested in AI consciousness, you might find interesting: Improving the Welfare of AIs: A Nearcasted Proposal
There seem to be a lot of good reasons to take potential AI consciousness seriously, even if we haven't fully understood it yet.
It seems hard to me to be extremely confident in either direction. I'm personally quite sympathetic to the idea, but there is very little consensus on what consciousness is, or what a principled approach would look like to determining whether/to what extent a system is conscious.
Here is a recent paper that gives a pretty in-depth discussion: Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
What you write seems to be focused entirely on the behavior of a system, and while I know there people who agree with that focus, from what I can tell most consciousness researchers are interested in particular properties of the internal process that produces that behavior.
More generally, science is about identifying the structure and patterns in the world; the task taxonomy learned by powerful language models may be very convergent and could be a useful map for understanding the territory of the world we are in. What’s more, such a decomposition would itself be of scientifico-philosophical interest — it would tell us something about thinking.
I would love to see someone expand on the ways we could use interpretability to learn about the world, or the structure of tasks (or perhaps examples of how we've already done this?). Aside from being interesting scientifically, maybe this could also help us build economically valuable systems which are more explicit and predictable?
Credit goes to Daniel Biber: https://www.worldphoto.org/sony-world-photography-awards/winners-galleries/2018/professional/shortlisted/natural-world/very
After the shape dissipated it actually reformed into another bird shape.
It's been a while since I read about this, but I think your slavery example might be a bit misleading. If I'm not mistaken, the movement to abolish slavery initially only gained serious steam in the United Kingdom. Adam Hochschild tells a story in Bury the Chains that makes the abolition of slavery look extremely contingent on the role activists played in shaping the UK political climate. A big piece of this story is how the UK used their might as a global superpower to help force an end to the transatlantic slave trade (as well as precedent setting).
What about leaning into the word-of-mouth sharing instead, and support that with features? For example, being able to as effortlessly as possible recommend posts to people you know from within LW?
I think I must be missing something. As the number of traders increases, each trader can be less risk averse as their personal wealth is now a much smaller fraction of the whole, and this changes their strategy. In what way are these individuals now not EU-maximizing?
I like this thought experiment, but I feel like this points out a flaw in the concept of CEV in general, not SCEV in particular.
If the entire future is determined by a singular set of values derived from an aggregation/extrapolation of the values of a group, then you would always run the risk of a "tyranny of the mob" kind of situation.
If in CEV that group is specifically humans, it feels like all the author is calling for is expanding the franchise/inclusion to non-humans as well.
@janus wrote a little bit about this in the final section here, particularly referencing the detection of situational awareness as a thing cyborgs might contribute to. It seems like a fairly straightforward thing to say that you would want the people overseeing AI systems to also be the ones who have the most direct experience interacting with them, especially for noticing anomalous behavior.
This post feels to me like it doesn't take seriously the default problems with living in our particular epistemic environment. The meat and dairy industries have historically, and continue to have, a massive influence on our culture through advertisements and lobbying governments. We live in a culture where we now eat more meat than ever. What would this conversation be like if it were happening in a society where eating meat was as rare as being vegan now?
It feels like this is preaching to the choir, and picking on a very small group of people who are not as well resourced (financially or otherwise). The idea that people should be vegan by default is an extremely minority view, even in EA, and so anyone holding this position really has everything stacked against them.
This avoids spending lots of time getting confused about concepts that are confusing because they were the wrong thing to think about all along, such as "what is the shape of human values?" or "what does GPT4 want?"
These sound like exactly the sort of questions I'm most interested in answering. We live in a world of minds that have values and want things, and we are trying to prevent the creation of a mind that would be extremely dangerous to that world. These kind of questions feel to me like they tend to ground us to reality.
Try out The Most Dangerous Writing App if you are looking for ways to improve your babble. It forces you to keep writing continuously for a set amount of time, or else the text will fade and you will lose everything.
First of all, thank you so much for this post! I found it generally very convincing, but there were a few things that felt missing, and I was wondering if you could expand on them.
However, I expect that neither mechanism will produce as much of a relative jump in AI capabilities, as cultural development produced in humans. Neither mechanism would suddenly unleash an optimizer multiple orders of magnitude faster than anything that came before, as was the case when humans transitioned from biological evolution to cultural development.
Why do you expect this? Surely the difference between passive and active learning, or the ability to view and manipulate one's own source code (or that of a successor) would be pretty enormous? Also it feels like this implicitly assumes that relatively dumb algorithms like SGD or Predictive-processing/hebbian-learning will not be improved upon during such a feedback loop.
On the topic of alignment, it feels like many of the techniques you mention are not at all good candidates, because they focus on correcting bad behavior as it appears. It seems like we mainly have a problem if powerful superhuman capabilities arrive before we have robustly aligned a system to good values. Currently, none of those methods have (as far as I can tell) any chance of scaling up, in particular because at some point we won't be able apply corrective pressures to a model that has decided to deceive us. Do we have any examples of a system where we apply corrective pressure early to instill some values, and then scale up performance without needing to continue to apply more corrective pressure?
Are you lost and adrift, looking at the looming danger from AI and wondering how you can help? Are you feeling overwhelmed by the size and complexity of the problem, not sure where to start or what to do next?
I can't promise a lot, but if you reach out to me personally I commit to doing SOMETHING to help you help the world. Furthermore, if you are looking for specific things to do, I also have a long list of projects that need doing and questions that need answering.
I spent so many years of my life just upskilling, because I thought I needed to be an expert to help. The truth is, there are no experts, and no time to become one. Please don't hesitate to reach out <3
Natural language is more interpretable than the inner processes of large transformers.
There's certainly something here, but it's tricky because this implicitly assumes that the transformer is using natural language in the same way that a human is. I highly recommend these posts if you haven't read them already:
That's a good point. There are clearly examples of systems where more is better (e.g. blockchain). There are just also other examples where this opposite seems true.
I agree that this is important. Are you more concerned about cyborgs than other human-in-the-loop systems? To me the whole point is figuring out how to make systems where the human remains fully in control (unlike, e.g. delegating to agents), and so answering this "how to say whether a person retains control" question seems critical to doing that successfully.
Thank you for this gorgeously written comment. You really capture the heart of all this so perfectly, and I completely agree with your sentiments.
I think it's really important for everyone to always have a trusted confidant, and to go to them directly with this sort of thing first before doing anything. It is in fact a really tough question, and no one will be good at thinking about this on their own. Also, for situations that might breed a unilateralist's curse type of thing, strongly err on the side of NOT DOING ANYTHING.
An example I think about a lot is the naturalistic fallacy. There is a lot horrible suffering that happens in the natural world, and a lot of people seem to be way too comfortable with that. We don't have any really high leverage options right now to do anything about it, but it strikes me as plausible that even if we could do something about it, we wouldn't want to. (perhaps even even make it worse by populating other planets with life https://www.youtube.com/watch?v=HpcTJW4ur54)
I really loved the post! I wish more people took S-risks completely seriously before dismissing them, and you make some really great points.
In most of your examples, however, it seems the majority of the harm is in an inability to reason about the consequences of our actions, and if humans became smarter and better informed it seems like a lot of this would be ironed out.
I will say the hospice/euthanasia example really strikes a chord with me, but even there, isn't it more a product of cowardice than a failure of our values?
GI is very efficient, if you consider that you can reuse a lot machinery that you learn, rather than needing to relearn it over and over again. https://towardsdatascience.com/what-is-better-one-general-model-or-many-specialized-models-9500d9f8751d
Sometimes something can be infohazardous even if it's not completely true. Even though the northwest passage didn't really exist, it inspired many European expeditions to find it. There's a lot of hype about AI right now, and I think the idea for a cool new capabilities idea (even if it turns out not to work well) can also do harm by inspiring people try similar things.