Posts tagged community
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China has made several efforts to preserve their chip access, including smuggling, buying chips that are just under the legal limit of performance, and investing in their domestic chip industry.[1]
Sounds about right?
This post centres around an email I sent to the Center for AI Safety (CAIS) expressing concern about their 2023-08-15 newsletter's coverage of US-China competition in the AI space[2], but the overall point is broader. There are some ways of discussing the topic of international relations regarding AI which strike me as un-nuanced in a counterproductive and dangerous way, by hiding certain truths or emphasising others, and supporting a conflict-oriented mindset.
In writing about this, I'm also gesturing at something about the more general topic of 'how to think and write about politically-charged topics'.
Jump to the summary...
tl;dr: Advocacy to the public is a large and neglected opportunity to advance AI Safety. AI Safety as a field is unfamiliar with advocacy, and it has reservations, some founded and others not. A deeper understanding of the dynamics of social change reveals the promise of pursuing outside game strategies to complement the already strong inside game strategies. I support an indefinite global Pause on frontier AI and I explain why Pause AI is a good message for advocacy. Because I’m American and focused on US advocacy, I will mostly be drawing on examples from the US. Please bear in mind, though, that for Pause to be a true solution it will have to be global.
The case for advocacy in general
Advocacy can work
I’ve encountered many EAs who are...
It is good that 80k is making simple videos to explain the risks associated with EA
Do you mean "risks associated with AI"?
Introduction
This post seeks to estimate how much we should expect a highly cost-effective charity to spend on reducing existential risk by a certain amount. By setting a threshold for cost-effectiveness, we can be selective about which longtermist charities to recommend to donors.
We appreciate feedback. We would like for this post to be the first in a sequence about cost-effectiveness thresholds for giving, and your feedback will help us write better posts.
How many beings does extinction destroy?
This chart gives six estimates for the size of the moral universe that would be lost in an extinction event on Earth this century. There is a truly incredible range in the possible size of the moral universe, and the value you see in the future depends on the moral weights you...
Thanks for your donations to the LTFF. I think they need to start funding stuff aimed at slowing AI down (/pushing for a global moratorium on AGI development). There's not enough time for AI Safety work to bear fruit otherwise.
Happy to end this thread here. On a meta-point, I think paying attention to nuance/tone/implicatures is a better communication strategy than retreating to legalese, but it does need practice. I think reflecting on one's own communicative ability is more productive than calling others irrational or being passive-aggressive. But it sucks that this has been a bad experience for you. Hope your day goes better!
TL;DR:
AIs will probably be much easier to control than humans due to (1) AIs having far more levers through which to exert control, (2) AIs having far fewer rights to resist control, and (3) research to better control AIs being far easier than research to control humans. Additionally, the economics of scale in AI development strongly favor centralized actors.
Current social equilibria rely on the current limits on the scalability of centralized control, and the similar levels of intelligence between actors with different levels of resources. The default outcome of AI development is to disproportionately increase the control and intelligence available to centralized, well-resourced actors. AI regulation (including pauses) can either reduce or increase the centralizing effects of AI, depending on the specifics of the regulations. One of...
I'm not opposed to training AIs on human data, so long as those AIs don't make non-consensual emulations of a particular person which are good enough that strategies optimized to manipulate the AI are also very effective against that person. In practice, I think the AI does have to be pretty deliberately set up to mirror a specific person for such approaches to be extremely effective.
I'd be in favor of a somewhat more limited version of the restriction OpenAI is apparently doing, where the thing that's restricted is deliberately aiming to make really good ...
Listen to the audio version of this article (text-to-speech software)
Update, 3/8/2021: I (Hauke) gave a talk at Effective Altruism Global on this post:
Summary
Randomista development (RD) is a form of development economics which evaluates and promotes interventions that can be tested by randomised controlled trials (RCTs). It is exemplified by GiveWell (which primarily works in health) and the randomista movement in economics (which primarily works in economic development).
Here we argue for the following claims, which we believe to be quite weak:
- Prominent economists make plausible arguments which suggest that research on and advocacy for economic growth in low- and middle-income countries is more cost-effective than the things funded by proponents of randomista development.
- Effective altruists have devoted too little attention to these arguments.
- Assessing the soundness of these arguments should be
You probably know this by now, but what the heck. I don't think EA as a whole is RCT-only. GiveWell is, AFAIK, very randomista. But there are other EA-affiliated organizations that are not as randomista as GiveWell, notably Open Philanthropy and anything with a more x-risk or long-termist focus.
As humanity continues its era of rapid population growth and rising economic prosperity, the demand for animal protein is anticipated to reach unparalleled heights. This surge in consumption is set to drastically impact the lives of farmed animals worldwide. Nowhere is this growth more pronounced than in Africa.
The evidence
Previously, our anticipation of Africa’s sharp increase in livestock numbers was primarily grounded in the historical global expansion of farmed animal populations over the past decades, coupled with human population growth trends across the African continent. This post, however, delves into the specific projections of farmed animal numbers and animal farming intensification from 2012 to 2050, as outlined by the Food and Agriculture Organization of the United Nations (FAO)*, which is based on many more factors than just historical changes in animal...
Crop yields are extremely low in much of Africa so my guess is there's potential for farmed animals to be fed while keeping constant or even decreasing land use.
How important is visualization w.r.t. the interpretability (or broader alignment) problem? Specifically, is there need+opportunity for impact of frontend engineers in that space?
Additional context:
I’ve got 10 years of experience in software engineering, most of which has been on frontend data visualization stuff, currently at Google (previously at Microsoft). I looked around at some different teams within Google and saw Tensorboard and the Learning Interpretability Tool, but it’s unclear to me how much those teams are bottlenecked by visualization implementation problems vs research problems of knowing where/how to even look, and I’d like to have more background before I cold-call them directly
I've started to get burned out by the earning to give path and am currently considering semi-retirement to focus on other pursuits, but if there’s somewhere I can contribute to alignment without needing to go back for a PhD that would be perfect (I have been eagerly studying ML on the side though)
Visualization is pretty important in exploratory mechanistic interp work, but this is more about fast research code: see any of Neel's exploratory notebooks.
When Redwood had a big interpretability team, they were also developing their own data viz tooling. This never got open-sourced, and this could have been due to lack of experience by the people who wrote such tooling. Anthropic has their own libraries too, Transformerlens could use more visualization, and I hear David Bau's lab is developing a better open-source interpretability library. My guess is th...
This post is part of AI Pause Debate Week. Please see this sequence for other posts in the debate.
An AI Moratorium of some sort has been discussed, but details matter - it’s not particularly meaningful to agree or disagree with a policy that has no details. A discussion requires concrete claims.
To start, I see three key questions, namely:
- What does a moratorium include?
- When and how would a pause work?
- What are the concrete steps forward?
Before answering those, I want to provide a very short introduction and propose what is in or out of bounds for a discussion.
There seems to be a strong consensus that future artificial intelligence could be very bad. There is quite a significant uncertainty and dispute about many of the details - how bad it could...
Thank you for your carefully thought-through essay on AI governance. Given your success as a forecaster of geopolitical events, could you sketch out for us how we might implement AI governance on, for example, Iran, North Korea, and Russia? You mention sensors on chips to report problematic behavior, etc. However, badly behaving nations might develop their own fabs. We could follow the examples of attacks on Iran's nuclear weapons technologies. But would overt/covert military actions risk missing the creation of a "black ball" on the one hand, or escalation into global nuclear/chemical/biological conflict?
This is an exemplary and welcome response: concise, full-throated, actioned. Respect, thank you Aidan.
Sincerely, I hope my feedback was all-considered good from your perspective. As I noted in this post, I felt my initial email was slightly unkind at one point, but I am overall glad I shared it - you appreciate my getting exercised about this, even over a few paragraphs!
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