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In this "quick take", I want to summarize some my idiosyncratic views on AI risk.  My goal here is to list just a few ideas that cause me to approach the subject differently from how I perceive most other EAs view the topic. These ideas largely push me in the direction of making me more optimistic about AI, and less likely to support heavy regulations on AI. (Note that I won't spend a lot of time justifying each of these views here. I'm mostly stating these points without lengthy justifications, in case anyone is curious. These ideas can perhaps inform why I spend significant amounts of my time pushing back against AI risk arguments. Not all of these ideas are rare, and some of them may indeed be popular among EAs.) 1. Skepticism of the treacherous turn: The treacherous turn is the idea that (1) at some point there will be a very smart unaligned AI, (2) when weak, this AI will pretend to be nice, but (3) when sufficiently strong, this AI will turn on humanity by taking over the world by surprise, and then (4) optimize the universe without constraint, which would be very bad for humans. By comparison, I find it more likely that no individual AI will ever be strong enough to take over the world, in the sense of overthrowing the world's existing institutions and governments by surprise. Instead, I broadly expect unaligned AIs will integrate into society and try to accomplish their goals by advocating for their legal rights, rather than trying to overthrow our institutions by force. Upon attaining legal personhood, unaligned AIs can utilize their legal rights to achieve their objectives, for example by getting a job and trading their labor for property, within the already-existing institutions. Because the world is not zero sum, and there are economic benefits to scale and specialization, this argument implies that unaligned AIs may well have a net-positive effect on humans, as they could trade with us, producing value in exchange for our own property and services. Note that my claim here is not that AIs will never become smarter than humans. One way of seeing how these two claims are distinguished is to compare my scenario to the case of genetically engineered humans. By assumption, if we genetically engineered humans, they would presumably eventually surpass ordinary humans in intelligence (along with social persuasion ability, and ability to deceive etc.). However, by itself, the fact that genetically engineered humans will become smarter than non-engineered humans does not imply that genetically engineered humans would try to overthrow the government. Instead, as in the case of AIs, I expect genetically engineered humans would largely try to work within existing institutions, rather than violently overthrow them. 2. AI alignment will probably be somewhat easy: The most direct and strongest current empirical evidence we have about the difficulty of AI alignment, in my view, comes from existing frontier LLMs, such as GPT-4. Having spent dozens of hours testing GPT-4's abilities and moral reasoning, I think the system is already substantially more law-abiding, thoughtful and ethical than a large fraction of humans. Most importantly, this ethical reasoning extends (in my experience) to highly unusual thought experiments that almost certainly did not appear in its training data, demonstrating a fair degree of ethical generalization, beyond mere memorization. It is conceivable that GPT-4's apparently ethical nature is fake. Perhaps GPT-4 is lying about its motives to me and in fact desires something completely different than what it professes to care about. Maybe GPT-4 merely "understands" or "predicts" human morality without actually "caring" about human morality. But while these scenarios are logically possible, they seem less plausible to me than the simple alternative explanation that alignment—like many other properties of ML models—generalizes well, in the natural way that you might similarly expect from a human. Of course, the fact that GPT-4 is easily alignable does not immediately imply that smarter-than-human AIs will be easy to align. However, I think this current evidence is still significant, and aligns well with prior theoretical arguments that alignment would be easy. In particular, I am persuaded by the argument that, because evaluation is usually easier than generation, it should be feasible to accurately evaluate whether a slightly-smarter-than-human AI is taking bad actions, allowing us to shape its rewards during training accordingly. After we've aligned a model that's merely slightly smarter than humans, we can use it to help us align even smarter AIs, and so on, plausibly implying that alignment will scale to indefinitely higher levels of intelligence, without necessarily breaking down at any physically realistic point. 3. The default social response to AI will likely be strong: One reason to support heavy regulations on AI right now is if you think the natural "default" social response to AI will lean too heavily on the side of laissez faire than optimal, i.e., by default, we will have too little regulation rather than too much. In this case, you could believe that, by advocating for regulations now, you're making it more likely that we regulate AI a bit more than we otherwise would have, pushing us closer to the optimal level of regulation. I'm quite skeptical of this argument because I think that the default response to AI (in the absence of intervention from the EA community) will already be quite strong. My view here is informed by the base rate of technologies being overregulated, which I think is quite high. In fact, it is difficult for me to name even a single technology that I think is currently clearly underregulated by society. By pushing for more regulation on AI, I think it's likely that we will overshoot and over-constrain AI relative to the optimal level. In other words, my personal bias is towards thinking that society will regulate technologies too heavily, rather than too loosely. And I don't see a strong reason to think that AI will be any different from this general historical pattern. This makes me hesitant to push for more regulation on AI, since on my view, the marginal impact of my advocacy would likely be to push us even further in the direction of "too much regulation", overshooting the optimal level by even more than what I'd expect in the absence of my advocacy. 4. I view unaligned AIs as having comparable moral value to humans: This idea was explored in one of my most recent posts. The basic idea is that, under various physicalist views of consciousness, you should expect AIs to be conscious, even if they do not share human preferences. Moreover, it seems likely that AIs — even ones that don't share human preferences — will be pretrained on human data, and therefore largely share our social and moral concepts. Since unaligned AIs will likely be both conscious and share human social and moral concepts, I don't see much reason to think of them as less "deserving" of life and liberty, from a cosmopolitan moral perspective. They will likely think similarly to the way we do across a variety of relevant axes, even if their neural structures are quite different from our own. As a consequence, I am pretty happy to incorporate unaligned AIs into the legal system and grant them some control of the future, just as I'd be happy to grant some control of the future to human children, even if they don't share my exact values. Put another way, I view (what I perceive as) the EA attempt to privilege "human values" over "AI values" as being largely arbitrary and baseless, from an impartial moral perspective. There are many humans whose values I vehemently disagree with, but I nonetheless respect their autonomy, and do not wish to deny these humans their legal rights. Likewise, even if I strongly disagreed with the values of an advanced AI, I would still see value in their preferences being satisfied for their own sake, and I would try to respect the AI's autonomy and legal rights. I don't have a lot of faith in the inherent kindness of human nature relative to a "default unaligned" AI alternative. 5. I'm not fully committed to longtermism: I think AI has an enormous potential to benefit the lives of people who currently exist. I predict that AIs can eventually substitute for human researchers, and thereby accelerate technological progress, including in medicine. In combination with my other beliefs (such as my belief that AI alignment will probably be somewhat easy), this view leads me to think that AI development will likely be net-positive for people who exist at the time of alignment. In other words, if we allow AI development, it is likely that we can use AI to reduce human mortality, and dramatically raise human well-being for the people who already exist. I think these benefits are large and important, and commensurate with the downside potential of existential risks. While a fully committed strong longtermist might scoff at the idea that curing aging might be important — as it would largely only have short-term effects, rather than long-term effects that reverberate for billions of years — by contrast, I think it's really important to try to improve the lives of people who currently exist. Many people view this perspective as a form of moral partiality that we should discard for being arbitrary. However, I think morality is itself arbitrary: it can be anything we want it to be. And I choose to value currently existing humans, to a substantial (though not overwhelming) degree. This doesn't mean I'm a fully committed near-termist. I sympathize with many of the intuitions behind longtermism. For example, if curing aging required raising the probability of human extinction by 40 percentage points, or something like that, I don't think I'd do it. But in more realistic scenarios that we are likely to actually encounter, I think it's plausibly a lot better to accelerate AI, rather than delay AI, on current margins. This view simply makes sense to me given the enormously positive effects I expect AI will likely have on the people I currently know and love, if we allow development to continue.
First in-ovo sexing in the US Egg Innovations announced that they are "on track to adopt the technology in early 2025." Approximately 300 million male chicks are ground up alive in the US each year (since only female chicks are valuable) and in-ovo sexing would prevent this.  UEP originally promised to eliminate male chick culling by 2020; needless to say, they didn't keep that commitment. But better late than never!  Congrats to everyone working on this, including @Robert - Innovate Animal Ag, who founded an organization devoted to pushing this technology.[1] 1. ^ Egg Innovations says they can't disclose details about who they are working with for NDA reasons; if anyone has more information about who deserves credit for this, please comment!
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harfe
4d
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Consider donating all or most of your Mana on Manifold to charity before May 1. Manifold is making multiple changes to the way Manifold works. You can read their announcement here. The main reason for donating now is that Mana will be devalued from the current 1 USD:100 Mana to 1 USD:1000 Mana on May 1. Thankfully, the 10k USD/month charity cap will not be in place until then. Also this part might be relevant for people with large positions they want to sell now: > One week may not be enough time for users with larger portfolios to liquidate and donate. We want to work individually with anyone who feels like they are stuck in this situation and honor their expected returns and agree on an amount they can donate at the original 100:1 rate past the one week deadline once the relevant markets have resolved.
Dustin Moskovitz claims "Tesla has committed consumer fraud on a massive scale", and "people are going to jail at the end" https://www.threads.net/@moskov/post/C6KW_Odvky0/ Not super EA relevant, but I guess relevant inasmuch as Moskovitz funds us and Musk has in the past too. I think if this were just some random commentator I wouldn't take it seriously at all, but a bit more inclined to believe Dustin will take some concrete action. Not sure I've read everything he's said about it, I'm not used to how Threads works
With the US presidential election coming up this year, some of y’all will probably want to discuss it.[1] I think it’s a good time to restate our politics policy. tl;dr Partisan politics content is allowed, but will be restricted to the Personal Blog category. On-topic policy discussions are still eligible as frontpage material. 1. ^ Or the expected UK elections.

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Learn about Pure Earth's work to fight toxic metals pollution, the data-driven evidence they use, and projects to get involved! 

https://www.PureEarth.org

(More details to come)

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We recently published a new core career advice series. It provides a concise, accessible intro to some of the most important ideas for planning an impactful career. Check it out on our site!

What is the core advice series?

The core advice series distills the most important...

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What do you think are some of the main differences between your guide/advice and 80k's?

I realise that to some extent, merely covering similar ideas with a slightly different framing and emphasis can add value because variations in these things land more or less well with different people.

But I'm wondering about more substantive differences. E.g. this page implies that you either don't endorse longtermism or endorse it less strongly than 80k, and my impression from your content is that you do tend to highlight a broader range of opportunities, including a m... (read more)

Bella commented on What is veganism anyway? 2h ago
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Originally posted on my blog

A very interesting discussion I came across online between Cosmicskeptic (Alex) and Earthlings Ed (Ed Winters) brought forth several points that I have wondered about in the past.  In one segment, Alex poses the following question: ...

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I didn't read your post in detail, but I think these kinds of discussions often miss considerations around fine-grained vs coarse decision criteria.

  • It's really hard to try to minimise animal deaths in whatever you do
  • It's also really hard to stick by 'I try to drive as little as possible, especially when it's raining, except in emergencies where considering whether to drive would cost precious time and worsen the outcome, or when, by refusing to drive, I would cause reputational harm to utilitarians by seeming too weird, or...'
  • It's (comparatively) reall
... (read more)
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Venky1024
5h
Thanks for the response. You've summarised the post very well except that, more than limiting intellectual freedom, the convention definition leads to excessive focus on purity at the first-order at the expense of broad utilitarian considerations (think of all the vitriol that vegans throw at deserters which is so irrational).  As for your view that without the solidarity, the veganism would not be what it is today, I am not entirely convinced. To be clear, the community of interest in this discussion is the animal advocacy one and not vegans per se (notwithstanding  the fact the two of them intersect almost completely). Here are some counter-arguments to consider: 1. Animal advocates are likely to be first-order vegans or very close to it anyway. If one voluntarily chooses to make lifestyle changes based on concern for animal suffering, then one is likely to go to significant lengths to avoid animal products.  Not everyone may go  the same distance but that's okay (or so I think).   2. Peter Singer the philosopher who arguably has the greatest claim to influencing people on animal rights and liberation is not a strict vegan and in fact describes himself as being "flexible". Yuval Harari is another person who is passionate about ending industrial agriculture of animals but describes himself as "vegan-ish". If important thinkers who undoubtedly have a great influence on people refrain from using the word "vegan", then why do you  think that as a community animal advocates should not shed that label or loosen its definition? 3. Conversely, taking vegan purity to the extreme, we have people like Gary Francione who are so opposed to any welfarist progress (regardless of its consequential value) and who insist that we should avoid meat alternatives because that normalizes the idea of consuming animals. I hope we can agree that that position is counterproductive. 4. I may be extrapolating from personal experience but first-order veganism being as clearly defined (ve
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Venky1024
6h
You may be slightly mistaken about what I am stating: the ambiguity is in the official definition even if it a sensible sounding one whereas the conventional definition is well-defined ('no first-order consumption') but arbitrary. The problem arises not so much from arbitrariness in and of itself, but rather demanding strict adherence to (and unwarranted focus on) something that isn't well-justified to begin with.  That leads to all sorts of contradictions.   On the second point, I agree that the distinctions between the two examples are somewhat arbitrary. One may argue that perhaps animal-testing in many instances is unnecessary (turns out several are based on methods and assumptions that have been around for a century and have persisted more out of inertia despite no clear evaluation of their effectiveness) but conventional agriculture depends on pesticides but I wouldn't find that argument very convincing. 
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1.4 billion people live on less than $1.25 per day. What does that mean?

What it doesn’t mean is that these people are living on what $1.25 could buy in, say, Kenya. If you thought that, then you might think that $1.25 isn’t so bad, because money goes much further in developing...

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So when a person gather sticks from the forest for their own use — that counts as ‘consumption’

How could one measure consumption that includes things like this? And how would you pick a dollar value for how much the stick gathering was worth?

My credence: 33% confidence in the claim that the growth in the number of GPUs used for training SOTA AI will slow down significantly directly after GPT-5. It is not higher because of (1) decentralized training is possible, and (2) GPT-5 may be able to increase hardware...

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Executive summary: The scaling of AI training runs is expected to slow down significantly after GPT-5 due to the unsustainable power consumption required to continue scaling at the current rate, which would necessitate the equivalent of multiple nuclear power plants.

Key points:

  1. Current large data centers consume around 100 MW of power, limiting the number of GPUs that can be supported.
  2. GPT-4 used an estimated 15k to 25k GPUs, requiring 15 to 25 MW of power.
  3. A 10-fold increase in GPUs above GPT-5 would require a 1 to 2.5 GW data center, which doesn't exist and
... (read more)
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Erich_Grunewald
2h
Why do you think decentralized training using several data centers will lead to a significant slowdown in scaling runs? Gemini was already trained across multiple data centers.
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The Centre for Exploratory Altruism Research (CEARCH) is an EA organization working on cause prioritization research as well as grantmaking and donor advisory. This project was commissioned by the leadership of the Meta Charity Funders (MCF) – also known as the Meta Charity...

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[with respect to talent] In terms of age prioritization, it is suboptimal that EA focuses more on outreach to university students or young professionals as opposed to mid-career people with greater expertise and experience.

 

I think this is conditional on the object-level positions available being reasonably well-suited to mid-career folks. For instance, job security becomes increasingly important as one ages. Even to the extent that successful mid-career folks face a risk of being let go in their current positions, they know they should be able to fin... (read more)

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James Herbert
4h
I'm surprised by the scepticism re 80k. The OP EA/LT survey from 2020 seems to suggest one can be quite confident of the positive impact of 80k on the small (but important) population surveyed. As the authors noted in their summary:
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SummaryBot
7h
Executive summary: The EA meta funding landscape saw rapid growth and contraction from 2021-2023, with Open Philanthropy being the largest funder and longtermism receiving the most funding, while the landscape faces high uncertainty in the short-to-medium term. Key points: 1. EA meta funding grew from $109M in 2021 to $193M in 2022, before shrinking to $117M in 2023, driven largely by changes in Open Philanthropy's spending. 2. Funding allocation by cause, in descending order: longtermism ($274M) >> global health and development ($67M) > cross-cause ($53M) > animal welfare ($25M). 3. Funding allocation by intervention, in descending order: other/miscellaneous ($193M) > talent ($121M) > prioritization ($92M) >> effective giving ($13M). 4. The EA meta grantmaking landscape is highly concentrated, with core funders (Open Philanthropy, EA Funds, and SFF) making up 82% of total funding. 5. Crucial considerations for prioritizing direct vs meta work include the relative cost-effectiveness of the direct work and whether opportunities are getting better or worse. 6. The meta funding landscape faces high uncertainty in the short-to-medium term due to turnover at Open Philanthropy, uncertainty at EA Funds, and the ongoing consequences of the FTX collapse.     This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

This post has something strange going on:

  1. When I give people this advice face to face, it helps, they change their path, say it saved time and/or focused them and/or they say "seems obvious in hindsight". I'll call this situation "works"
  2. When people read this at home, they
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Paul Graham agrees that building something you're excited about is a top way to get good at technology:

https://forum.effectivealtruism.org/posts/mtBwjfygyAudDakyC/yonatan-cale-s-shortform?commentId=KBeNwzkiAKFp5Lw9T

Yonatan Cale posted a Quick Take 3h ago

Paul Graham about getting good at technology (bold is mine):

How do you get good at technology? And how do you choose which technology to get good at? Both of those questions turn out to have the same answer: work on your own projects. Don't try to guess whether gene editing or LLMs or rockets will turn out to be the most valuable technology to know about. No one can predict that. Just work on whatever interests you the most. You'll work much harder on something you're interested in than something you're doing because you think you're supposed to.

If you're not sure what technology to get good at, get good at programming. That has been the source of the median startup for the last 30 years, and this is probably not going to change in the next 10.

From "HOW TO START GOOGLE", March 2024. It's a talk for ~15 year olds, and it has more about "how to get good at technology" in it.

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This is the latest of a theoretically-three-monthly series of posts advertising EA infrastructure projects that struggle to get and maintain awareness (see original advertising post for more on the rationale).

I italicise organisations added since the previous post was originally...

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Thank you so much for creating this series of posts! I think this is a great idea to get the word out about these and there is a lack of visibility into these important services and orgs. 

Is there any other marketing that is going along with these posts? e.g. posting on Slack channels, Facebook groups, or the like? I think that could potentially multiply the benefits and help get the word out even more. 

I'm definitely supportive of being more broadly inclusive of services/orgs here that are fee-charging and for the same justification you list.&nb... (read more)

1
atury
4h
I suggest adding the EA Services Directory (managed by Deena Englander) here. EASE is: "A directory of independent agencies and freelancers offering expertise to EA-aligned organisations." Their problem statement: "Many organizations in the EA world have similar needs but lack the bandwidth or expertise to realize them. By providing a directory of experts covering many common challenges, we aim to save the movement time whilst addressing key skill shortages."