Denis

Karma: 355

Denis 11 Apr 2023 22:06 UTC
8 points
1 ∶ 1
on: Growth and the case against randomista development
This is my first venture at a comment on this forum. I have recently joined, and this was one of the recommended articles. Sorry my comment is many years after the fact.

I was not aware that EA was so “fixated” on RCT’s. It seems a very limiting position to take, and somewhat inconsistent with the idea of doing the most good. Surely deciding that you’ll only invest in things that can be validated by RCT’s is no different than saying you’ll only invest in things which start with a consonant? The criterion seems almost irrelevant to the potential value of the intervention. I’m sure I’m vastly oversimplifying the position. But nonetheless, I wanted to make a comment on how to handle testability as a parameter.

In many areas of life (e.g. financial markets) there is an acceptance that testability / provability comes with a cost. A low-volatility stock costs more than a stock with the same expected value but higher volatility. People who are willing to accept more uncertainty are, on average, rewarded.

Is there a similar situation here. Insisting on RCTability is a very conservative approach. Laudable if the goal is to prove (for our own satisfaction) that we’re adding value, but not necessarily the option which does the most good, because some options have been excluded because they are not testable.

In my career in business, this is a constant challenge. Successful companies certainly do RCT’s when they can, but they are very careful to distinguish between ideas which RCT’s show to be inefficient and ideas which are not RCTable. For the latter, it is normal to look at an analysis like the one in this article and use judgment to sometimes take non RCTed decisions.
I am sure this is something that could be quantified, much in the way that the cost of volatility is quantified; that one could reach a reasonable position relating how much extra “expected value” would be necessary to compensate for the lack of RCTability of that expected value.

It’s kind of analogous to the way that you compare high-risk / high-reward vs. low-risk / low-reward scenarios by using expected value, except here even the expected value is very uncertain for reasons related not to risk but to testability.
I will go and learn more about this, and probably realise that what I’m writing here is misguided. (feel free not to publish this comment if it doesn’t make sense :D—or otherwise, just delete this last part!)

Denis 13 Apr 2023 12:57 UTC
6 points
0 ∶ 0
on: EA & “The correct response to uncertainty is *not* half-speed”
There is a corporate motto: “10% of decisions need to be right. 90% of decisions just need to be taken!” which resonates perfectly with this post.

To put this in an EA context—if you’re unsure which of two initiatives to work on, that probably means that (to the best of your available knowledge) they are likely to have similar impacts. So, in the grand scheme of things, it probably doesn’t matter which you choose. But the time you spend deciding is time that you are NOT dedicating to either of the initiatives.

However, this is a good rule-of-thumb, but you need to be wary of exceptions. There are those 10% of cases where your decision matters a lot. In my case, as a chemical engineer, decisions about safety would typically be in that 10%. In an EA context, maybe it’s decisions where you really are not sure if a particular initiative might be doing more harm than good which fall into this 10%.

How to decide whether you can already take a decision?
1. Does any decision have potentially very bad consequences? Not just wasted time, but actual harm, or major investments wasted or whatever.
2. How much of a difference is there likely to be depending on which decision you take?
3. What new information are you likely to get (and when) which could help you make a better decision?
4. Put the pros and cons on a sheet of paper and discuss with a friend or colleague. Often times, this exercise alone, even before you discuss, will enable you to make a decision.

Denis 26 Apr 2023 21:22 UTC
1 point
0 ∶ 0
on: Who were The Righteous Among the Nations?
Really like this post. Simple, clear and very provocative. It would be great to see it shared more widely.

If we could get people to ask themselves “which camp do I belong to?” and then to act accordingly …

Most of us look back at history and assume we would have been the exceptions—the people opposing slavery, protecting Jews, giving to the poor, etc. But the reality of our actions today (my own absolutely included) belies this for most of us.

Your post is a timely reminder for us to ask ourselves some questions.

Denis 5 May 2023 10:02 UTC
5 points
0 ∶ 0
on: Test fit for roles / job types / work types, not cause areas
This is a great post.

The ideal job is the one where you’re doing things you love to do anyway, and getting paid for it. But it’s hard to know in advance what that will be. So I would encourage people to test the waters a bit too.

For example, lab-research is exciting, but for each 3 hour experiment you might spend a week preparing, doing risk-analyses, ordering materials, running QA checks, requesting budget, booking lab-space. And you may end up in a lab where you need to spend 15 minutes every time you enter or exit the lab just putting on and taking off protective clothing. Some people thrive in this environment, they love the details and the precision and the perfectionism of getting everything just right.

Likewise, doing literature research and learning about the leading edge of the field is fascinating. But are you sure you’re the kind of person who will look forward to having a 40-page technical document in highly concise, technical language to read every morning and every afternoon?

Being involved in policy-setting feels like an incredibly important role—and it is. But it also requires keeping your own ego and opinions very much in check, being able to push enthusiastically for incremental gains, being able to compromise strategically, being willing to accept things that you don’t like, listening respectfully to opinions you find repulsive and so on. (for example: imagine you’re negotiating with China about cutting methane emissions and they say “we can agree to your proposal, but in return, we want you to endorse our one-China policy on Taiwan” ).
The people who are good at this will talk about the times they succeeded, but you need to be aware of how much effort and how many failed efforts they have to deal with. It requires huge degrees of resilience and grit. It is not for everyone.

Denis 23 May 2023 11:30 UTC
1 point
0 ∶ 0
on: Accidentally teaching AI models to deceive us (Ajeya Cotra on The 80,000 Hours Podcast)
Great article!

The analogy to the economy at the end is wonderful. A lot of us don’t realise how badly the economy works. But it’s easy to see by just thinking about AI and what’s happening right now. People are speculating that AI might one day do as much as 50% of the work now done by humans. A naive outsider might expect us to be celebrating in the streets and introducing a 3-day work-week for everyone. But instead, because our economy works the way it does, with almost all of most people’s income directly tied to their “jobs”, the reaction is mostly fear that it will eliminate jobs and leave people without any income.

I’m guessing that the vast majority of people would love to move to a condition (which AI could enable) where everyone works only 50% as much but we keep the same benefits. But there is no realistic way to get there with our economy, at least not quickly. Even if we know what we want to achieve, we just cannot overcome all the barriers and Nash equilibria and individual interests. We understand the principles of each different part of the economy, but the whole picture is just far too complex for anyone to understand or for us, even with total collaboration, to manipulate effectively.

I’m sure that if we were trying to design the economy from scratch, we would not want to create a system in which a hedge-fund manager can earn 1000 times as much as a teacher, for example. But that’s what we have created. If we cannot control the incentives for humans within a system that we fundamentally understand, how well can we control the incentives for an AI system working in ways that we don’t understand?

It’s worrying. And yet, AI can do so much good in so many ways for so many people, we have to find the right way forward.

Denis 23 May 2023 12:11 UTC
1 point
0 ∶ 0
on: How quickly AI could transform the world (Tom Davidson on The 80,000 Hours Podcast)
Really great discussion. How can we get this kind of information out into the general population?

IMHO the biggest challenge we face is convincing people that the default outcome, if we do nothing, is more likely to be that we get an AI which is much more powerful than humans. Tom and Luisa, you do a great job of making this case. If someone disagrees, it is up to them to demonstrate where is the flaw in the logic.

I think we face three critical challenges in getting people to act on this as urgently as we need to:
1. Our brains have evolved to learn from experience. As with climate change and even nuclear war, we have a deep-seated belief that things will change only gradually, and especially, that they will not change dramatically in negative ways, because in our lifetimes, that is what we’ve always experienced—even the most scary crises have tended to work out OK*, or where they haven’t (e.g. climate change), we’ve somehow convinced ourselves that they have. And this belief is OK, until suddenly it’s not, and then it’s too late.
2. Most people know the word “exponential” but don’t really understand what it means. The exponential growth you describe here, where each generation of AI can develop a new generation of AI that is X% better and faster, is just beyond most of our experience. It reminded me of chemistry experiments with acid-base titrations and the virtual impossibility of titrating to exactly pH 7 with strong acids and bases. If we imagine human-level AGI being at pH 7, we might feel comfortable that the pH has grown from 1.1 to 1.2 to 1.3 to 1.5 to 1.8, but not be aware that the next drop of NaOH will make it 10.4.
3. Our brains have evolved to live in denial. This is a vital and mostly positive trait. For example, we all know that we’re going to die, but we put this out of our mind almost all of the time. When faced with something really frightening (and even out-of-control AI isn’t quite as scary as death), we’re able to put it to the back of our minds. Not consciously deny it, not intellectually deny it, but rather accept that it is logically true, and then ignore it and act like it wasn’t true. Of course, it helps that there will always be those who will take advantage of this and make (usually flawed) arguments that the concerns are unwarranted, which then give us plausible deniability and make us feel even less worried.
All this means that most of us (I include myself in this) read this article, fully accept that Tom’s arguments are compelling, realise that we absolutely must do something, but somehow do not rush out and storm the parliament demanding immediate action. Instead, we go on to the next item on our to-do list, maybe laundry or grocery shopping … I’m really determined to figure out a way to overcome this inertia.
*Obviously this is true for those of us in the current generation, in the West. I’m sure those who lived through world wars or famines or national wars, even those today in Syria or Ukraine or Sudan, will have a better understanding of how things can suddenly go wrong. But most of the people taking the decisions about AI have never experienced anything like that.

Denis 23 May 2023 12:45 UTC
8 points
1 ∶ 0
on: How Engineers can Contribute to Civilisation Resilience
Thanks Jessica and Sean for this powerful and inspiring post.

I would add one more, perhaps even more important, way in which engineers can contribute here—which is more or less what you have just done!

As engineers, you probably don’t even consciously realise this anymore, but the type of analysis you’ve shared here is pure engineering, it’s a way of thinking that we get so drilled into us that we don’t realise we didn’t always think that way.

For example, the three layer model (prevention, response, resilience) is almost perfectly analogous to how chemical engineers study explosion safety when handling solvents. First, you ask how to prevent an explosion (safe procedures, no ignition sources, nitrogen-blanketing, …). Second, you ask how to minimise the risk of harm if there is an explosion (safe enclosures, PPE, minimum people present, etc. …), and third, you study how to minimise the extent of harm (fire-evacuation procedures, emergency-help, first-aid training, …).

From this perspective, I’d also add one comment, which is that the assumption that each reduction of 50% in one means to total reduction of 50% is only true if they are independent. One of the most difficult challenges in a safety analysis is to identify cases where one accident can break two barriers at once. The stereotypical example of this (from my youth) was the nuclear war scenario in which the electromagnetic wave from the explosion destroyed the communication structure and so a lot of the response capability. We know about that now and can design around it, but there are probably other factors, like the viral infection that makes it impossible for the vaccine designers to do their work.
I look forward to seeing more and more engineers working on civilisation resilience! Thanks for getting the ball rolling with Hi-Eng!

Denis 24 May 2023 8:29 UTC
3 points
0 ∶ 0
on: A Landscape Analysis of Institutional Improvement Opportunities
Just reading this now. I love the approach and especially the tangibility of it—identifying specific institutions, eventually developing specific plans to influence them in specific ways.

Of course there are people spending billions of dollars to influence some of these institutions, but that doesn’t mean that a small, well-organised group cannot have an important impact with a well-designed, focused campaign.

My first reaction to the list itself is that it feels quite narrow (government institutions and IT companies). I wonder if this reflects reality or is just a result of the necessary limitations of a first iteration with quite limited resources. Nobody could question that every institution on this list is an important, influential institution, and if you manage to influence any of them in a positive way, that will be a great success.

My point is more that there seem to be whole classes of organisations and institutions with huge impact which are not represented at all in the list. And while one could perhaps argue that any one of the institutions listed is more important than any one of the institutions I suggest below, I’m not sure that necessarily means that the most effective way forward is to focus on just the types of institutions listed, vs. looking at what potential there is in a much broader group—especially focusing on the question of influenceability (on which I’d suggest several of the institutions listed above might score quite poorly).
Here are a few of the categories I would have expected to see in the list, but didn’t:
- Oil companies, whose decisions are critical to the climate-crisis. Also OPEC, Gazprom and others.
- Pharma companies who have a critical role to play in global health—for example their decisions about enforcing patents in developing countries or allowing low-cost copies.
- FMCG Companies (Procter & Gamble, Unilever, …) whose products are used by billions of people every single day, so anything they decide about health, or waste, or water-quality impacts so many people across the globe immediately. They also directly and indirectly (including supply-chain and distribution and sales) employ vast numbers of people all around the world, and their employment practices and policies directly impact many millions.
- Weapons manufacturers. Probably not realistic to hope they all just decide to stop making weapons, but they could do a lot by just not selling weapons so freely to authoritarian and inhumane regimes.
- Scientific bodies. So much of the world is driven by technology, which isn’t just IT. It’s true that bodies like governments and the EU do influence scientific research, but there may be many much-more-easily-influenced bodies which can also impact this. Maybe something as simple as requesting a speaking slot at an annual conference in which we’d highlight the gaps and opportunities could help create a new body of relevant research and development.
- Etc.
I’m not criticising the work—I think it’s fantastic! - but rather wondering if it would be worth someone doing a second iteration and looking at some of these bodies which may not have been so obvious. All the ones I’ve listed above just reflect my personal expertise and experience as a chemical engineer, so probably is also far too narrow. Would be interesting to see what ideas we might get from a group of others, maybe historians, geologists, teachers, lawyers, biologists, astronomers, …

Really looking forward to following this and seeing where it leads!

Denis 25 May 2023 10:35 UTC
3 points
1 ∶ 1
in reply to: Zach Stein-Perlman’s comment on: [Linkpost] “Governance of superintelligence” by OpenAI
IMHO this is quite an accurate and helpful statement, not a euphemism. I offer this perspective as someone who has worked many years in a corporate research environment—actually, in one of the best corporate research environments out there.
There are three threads to the comment:
1. Even before we reach AGI, it is very realistic to expect AI to become stronger than humans in many specific domains. Today, we have that in very narrow domains, like Chess / Go / Protein-folding. These domains will broaden. For example, a lot of chemistry research these days is done with simulations, which car just confirmed by experiment. An AI managing such a system could develop better chemicals, eventually better drugs, more efficiently than humans. This will happen, if it hasn’t happened already.
2. One domain which is particularly susceptible to this kind of advance is IT, and so it’s reasonable to assume that AI systems will get very good at IT very quickly—which can quickly lead to a point where AI is working on improving AI, leading to exponential progress (in the literal sense of the word “exponential”) relative to what humans can do.
3. Once AI has a given capability, its capacity is far less limited than humans. Humans need to be educated, to undergo 4-year university courses and PhD’s just to become competent researchers in their chosen field. With AI, you just copy the software 100 times and you have 100 researchers who can work ²⁴⁄₇, who never forget any data, who can instantaneously keep abreast of all the progress in their field, who will never fall victim to internal politics or “not invented here” mentality, but will collaborate perfectly and flawlessly.
Put all that together, and it’s logical that once we have an AI that can do a specific domain task as well as a human (e.g. design and interpret simulated research into potentially interesting candidate molecules for drugs to fight a given disease), it is almost a no-brainer to reach the point where a corporation could use AI to massively accelerate their progress.

As AI gets closer to AGI, the domains in which AI can work independently will grow, the need for human involvement will decrease and the pace of innovation will grow. Yes, there will be some limits, like physical testing, where AI will still need humans, but even there robots already do much of the work, so human involvement is decreasing every day.

it’s also important to consider who was saying this: OpenAI. So their message was NOT that AI is bad. What they wanted us to take away was that AI has huge potential for good—like the way it can accelerate the development of medical cures, for example—BUT that it is moving forward so fast and most people do not realise how fast this can happen, and so we (in the know) need to keep pushing the regulators (mostly not experts) to regulate this while we still can.

Denis 2 Jun 2023 11:30 UTC
3 points
0 ∶ 0
in reply to: Linch’s comment on: AI Safety Newsletter #8: Rogue AIs, how to screen for AI risks, and grants for research on democratic governance of AI
“for superhuman rogue AIs to be catastrophic for humanity, they need to not only be catastrophic for 2023_Humanity but also for humanity even after we also have the assistance of superhuman or near-superhuman AIs.”

This is a very interesting argument, and definitely worthy of discussion. I realise you have only sketched your argument here, so I won’t try to poke holes in it.

Briefly, I see two objections that need to be addressed:

1. One fear is that the rogue AIs may well be released on 2023_Humanity or a version very close to that due to the exponential capability growth we could see if we create an AI that is able to develop better AI itself. Net, it may be enough that it would be catastrophic for 2023_Humanity.

2. The challenge of developing aligned superhuman AIs which would defend us against rogue AIs while themselves offering no threat is not trivial, and I’m not sure how many major labs are working on that right now, or if they can even write a clear problem-statement about what such an AI system should be.
From first principles, the concern is that this AI would necessarily be more limited (it needs to be aligned and safe) than a potential rogue AI, so why should we believe we could develop such an AI faster and enable it to stay ahead of potential rogue AIs?

Far from disagreeing with your comment, I’m just thinking about how it would work and what tangible steps need to be taken to create the kind of well-aligned AIs which could protect humanity.

Denis 8 Jun 2023 8:23 UTC
8 points
1 ∶ 0
on: Feedback requested: EA Forum reactions
I’ve only recently started posting on EA Forum, so my 2 cents may be useful as feedback from a newbie, but also with the caveat that it’s from a newbie!

1. Often times I just want to say “thank you for making the effort, for taking the time, for writing this post and/or for doing the research behind it”. This can be separate from judgment about quality or agreement, it’s just saying “I see you put a lot of effort into this, and I appreciate that.” Even just to say “I read this”. My fear when writing a new post is not that people will disagree with it (which sparks useful discussion), but rather that nobody will read it at all, and my time will have been wasted. So I think any kind of feedback that says “thanks for writing this” or “I read this” or just tracks the number of reads, can be important, especially for people who are new to the forum and don’t have a ready audience.
2. Related to the above, I’d appreciate clarity on how people feel about short, positive feedbacks, like “great post” or “fully agree”. It’s interesting that you say people would like more of this, because my natural reaction has always been to do that on every forum I’ve ever been on when I read a good post, usually with a few words explaining what I particularly liked. But here I don’t feel comfortable doing that—literally. It feels out-of-place, as if people don’t like that. Almost all the comments I see are about disagreements. (this is a very subjective, non-data-based impression!)
3. Feedback is a wonderful gift, and EA Forum readers are a great resource. If someone spends X minutes reading my post or comment, I would LOVE to know what they think of it. Especially the disagreements or constructive feedback, so I can get better. I’ve got a few negative karmas where there was no comment to indicate what exactly the disagreement or feedback was. I didn’t really know what to do with them. Someone took some of their valuable time to read my post, and thought about it—and I appreciate that. Then they gave some feedback on my post, which is great. Except that the feedback doesn’t really tell me what they think I should change! Feedback in the form of words will help me write better next time. I totally know my posts need work, just point me in the right direction :). If people feel bad about writing constructive (negative?) comments publicly, is there an option for “visible only to author” comments or even “anonymous and visible only to author” comments?
It’s great that you’re looking into this, it can make the Forum even better!

Denis 9 Jun 2023 18:12 UTC
5 points
2 ∶ 0
on: Things I Learned by Spending Five Thousand Hours In Non-EA Charities
What a brilliant post! Thanks for writing this, Jenn!

I know that my own passion to help others came from seeing my father work in charities like the one you describe here, especially the St. Vincent de Paul, in Dublin. Good people devoting their time to help others is great.

We need to keep in mind that the vast majority of people spend the vast majority of their time and money doing activities whose primary purpose is not altruistic at all. So when we see people doing something altruistic, and doing it well, and getting very good at it, this is wonderful. And I think it’s wonderful that you spent 5,000 hours doing this.

Regarding the Samaritans, at no point did you say or suggest that anything these charities did was misguided or wrong. We’re not talking about people spending vast amounts of money on frivolous or vanity-driven projects. We’re not talking about a billionaire making a charity donation of $100m for a new stadium named after him. Your EA mind was asking “could we do even more?”—but really, the Samaritans were already doing pretty well. They had got very good and efficient at what they do, they were passionate about that, people depended on them providing that—not just the people they helped, but also the public officials whose failures they were helping to cover up (really, there should not be homeless people in a rich country). I’m not sure I would want them to change. As you perfectly observed, they certainly should not change the humanity with which they approach their role and the people they help.

There may be some explanation of the push-back you experienced in personality-tools like Myers-Briggs. In MBTI, there is a spectrum from “Thinking” to “Feeling”. EA is very much on the thinking (T) end of that. It does NOT mean that EA people are less feeling—it means that EA people care, and so they think about how best to help as many people as possible. Feeling (F) people tend to be more focused on the emotional, less logical approach—they see someone who is unhappy, they want to help. They don’t analyse, they just feel and act. What is potentially interesting is how the two extremes often fail to understand each other—the extreme T’s maybe get frustrated at the apparently irrational, non-optimised approach of the F’s, but maybe also they don’t understand the value that the emotional, feeling connection has for the people. Meanwhile, the extreme F’s tend to view T’s as unfeeling and even cynical, because it’s just not how their brain works—while the EA person in you wanted to help even more people, they may have seen your idea as being less caring, more calculating …

Which is why your post is so great. You clearly manage to bridge the divide—your thinking side understands the EA mentality, but your feeling side sees how individual people react to being treated like people.

I don’t have some magical solution to propose. I’m just sharing some thoughts that were inspired by reading this great post.

And one final point: we must never forget that when a person interacts with an organisation like the Samaritans, it can be transformational for them personally, it can be the moment when they decide that they too will devote more time, energy or money to charitable work. And some people impacted in this way will eventually find themselves in the EA community. It’s hard to calculate the impact of this, but I’ll bet a lot of us, like myself, first encountered charity at home and then broadened our horizons.

Denis 9 Jun 2023 18:48 UTC
2 points
0 ∶ 0
on: Large Study Examining the Effects of Cash Transfer Programs on Population-Level Mortality Rates
Thanks for sharing this!

Cash-transfers are such a promising route to help people, who typically understand what they need far better than we do.

I suppose the obvious question is whether this can be translated into some unit like WELLBY’s/$100K or whatever. Of course the lives saved is only part of what the cash does*, but if we could show that already looking only at that it was competitive, that would be a powerful message.

I just skimmed the article, it seems very well written (well, it’s Nature, it would be!) and I’ll read it more carefully later. Some very crude calculations follow:

The median seems to be 11% of GDP per capita. So if imagine GDP per capita as about $1,626 (representative for Sub-Saharan Africa), then 11% of this is about $140/year. There were 30 million adult person-years, so let’s estimate 15 million person-years were for women. Three were 127,000 deaths, so let’s assume 64,000 women deaths, which represented a 20% reduction. So 16,000 women’s lives were saved with 15 million x $140 = $2,100m, or about $131K/life saved. Given that most of this was related to mortality at birth, we can further assume that these women would live an extra 50 years, so that just on lives saved alone, direct cash-transfers to women in sub-Saharan Africa can deliver about ⁵⁰⁄₁.31 = about 40 WELLBY’s/$100K. Given that this is only a small part of the total impact of cash-transfers (see quote below), this seems like an important validation of direct cash transfers to the poorest people.

(Very happy to have anyone correct my calculations or show me where someone has done a full analysis of this, I am just trying to get an order-of-magnitude here. Hopefully I didn’t miss too many factors of 10 …)
*this is a quote from the article: “Large-scale, government-run cash transfer programmes have been successful in reducing poverty and improving economic autonomy, school attendance, child nutrition, women’s empowerment and health-service use among beneficiaries²³^,²⁴. A few studies have also documented population-wide effects such as greater economic activity in communities where beneficiaries reside²⁵, and—in the case of infectious diseases such as HIV—reduced new infections following the introduction of cash transfer programmes²⁶. The improvements seen with cash transfers could be driven by the removal of economic and psychological barriers of poverty as a result of receiving cash transfers, as well as spillover effects on non-beneficiaries²⁷^,²⁸^,²⁹^,³⁰^,³¹^,³².”

Denis 10 Jun 2023 12:24 UTC
1 point
0 ∶ 0
on: EA and Longtermism: not a crux for saving the world
As I explore EA, I especially love provocative, thoughtful, introspective posts like this.
There are always opportunities to improve. The EA philosophy supports data-based, science-based decision-making, so absolutely this should be applied to communication as well.
This is a great example.
There is a massive amount of research out there, often done cynically by political parties or advertisers, but no less scientifically valid for that. We can learn from it. We could also, at very low-cost or even free, run research ourselves—if it’s well-designed, the results would be reliable and reproducible, and could support (or, I suppose, in theory, refute!) what you’re saying, and so help us communicate better and attract more people.
I believe there is one very tangible opportunity, which you and also @Vanessa in her comments below, are capturing. We in EA tend to be self-selected as rational, logical thinkers—I don’t want to overgeneralize, but IMHO if you looked at how each of us would describe ourselves in terms of Myers-Briggs*, most of us would be T’s rather than F’s.
So part of what you’re saying here is that our message may be a great way to attract “people who think like us” to work on X-risk, but may miss out some others, who “don’t think like us” who would be more effectively convinced by a different approach.
For decades, communication experts have been studying how to use different messages to communicate and convince different people. Again, we see the ugly, cynical side of this in politics and FoxNews and so on. But there is also a good side, which is about communicating an idea to someone in a form that makes it clear and appealing to them.
If you’re interested in pursuing this further, I’d be happy to share some of what I’ve learned about this over the years of doing quantitative and qualitative research on communication.
*I believe the science behind the Myers-Briggs test is very dubious, BUT the categories themselves can be very helpful for communicating concepts like this. T’s tend to think rationally—if they are good people (like us!), they think rationally about how to do the most good for the most people. F’s are more driven by emotional reactions—they see someone crying, they see a danger to their loved ones, they decide to do something about it.

Denis 10 Jun 2023 20:26 UTC
3 points
0 ∶ 0
on: Successif: helping mid-career and senior professionals have impactful careers
This is awesome, Claire!

I probably fit the profile for the people you help, and so much of what you share here in Blockers and Bottlenecks describes my experience. I must add that since I decided to go for this career change, I’ve found some fantastic resources within the EA community ready to help and advise me; people have been very helpful, encouraging and supportive. But maybe if I’d known about you I would have attempted the move 5 years ago. I love the “giving permission” mentality.

I’ll fill out one of the “let us know here” sections. But just wanted to reply publicly to highlight the importance of the support you provide. Many of the most innovative and successful companies continue to hire and retain people with a lot of seniority, so we’re obviously useful for something :).

Denis 12 Jun 2023 20:09 UTC
2 points
0 ∶ 0
in reply to: Felix Wolf’s comment on: Feedback requested: EA Forum reactions
Thanks Felix,

Yes, I suppose a personal message is a good solution for “visible only to the author” :D

This is a great forum, I’m really enjoying and appreciating all the posts and the debates and the tone of polite but firm disagreements!

Cheers

Denis

Denis 23 Jun 2023 18:09 UTC
5 points
0 ∶ 0
on: Longtermists are perceived as power-seeking
This is a very interesting and provocative idea! Thank you for sharing.

One thought: is it possible that the concern relates to innumeracy / anti-science-thinking rather than (or in addition) to doubts about any specific group (e.g. white men)?
As in: could (part of) the concern be: “Here is a group of (very nerdy?) people trying to force us all to take critical decisions that sometimes seem bizarre, based on complex, abstract, quantitative arguments that only they understand. I’m not sure I trust them or their motives.” ?
IMHO we underestimate just how abstract some of the arguments in favour of longtermism can seem to 99% of the population who have not studied it, and when this is combined with recommendations that seem quite dramatic, it isn’t hard to see why people would express doubt and question the motives.
Remember, we live in a society in which many people think climate-scientists are just inventing climate-change so they can get more power, and in which medical experts trying to use data to recommend strategies to fight covid frequently had the motives questioned.
Is there a chance that, despite all the healthy disagreement within the EA community, to the external world we seem like an echo-chamber, living in an (imaginary?) world in which math and science and logic can justify any position?
I don’t think most people feel that way. People learn to be suspicious when people throw lots of numbers and new ideas at them and then recommend something that doesn’t seem to make sense. They think of suave car salesmen.
If you say “give me $100 and I can save three children by buying them mosquito nets,” that is tangible and simple. If you say “we should devote X% of our GDP to preventing very low-risk scenarios which could cost trillions of lives,” you’ve lost most people. If you then tell them that you want some of their money or resources to be impacted by this, they will question your motives. The specific details of the longtermism argument may not even be relevant to their suspicion.

Denis 23 Jun 2023 18:23 UTC
1 point
0 ∶ 0
on: Why Altruists Can’t Have Nice Things
Interesting read.

I have no clue what typical salaries or perks are in the EA community, and how they compare with similar jobs in similar locations, or how people react to them. So I won’t even try to express an opinion on the content itself. But it shows a very healthy self-awareness that this discussion is actually happening!
Ultimately, for most people, I would imagine the single biggest “perk” of EA work is to be able to work on something they really care about. And the downside to this is that they probably work far harder than they are required to, because they see this as more than just a job.

Denis 23 Jun 2023 23:23 UTC
29 points
11 ∶ 0
on: Third Wave Effective Altruism
I see a world that still desperately needs Wave 1, and I see a lot of work still to be done in that area.

I look at the effectivealtriusm.org homepage, and a lot of what is mentioned there is still what you’re referring to as wave 1.

I would even venture that in most of the world (perhaps outside the hubs), people are drawn to EA first by Wave 1 concepts. We get frustrated at the poverty and disease and war and poor governance and refugee crises and famines and … and we wonder why can’t we do more to fix these with the significant resources we do devote to them. We see a group like EA looking at how to use limited resources to help people in the most effective way possible and it seems like a critical answer to a long-neglected question.

Is it possible that what you’re describing here is the cutting-edge aspects of EA—the areas where EA is breaking new ground philosophically and analytically, the areas which create lively, passionate debates on this forum, for example? And so, naturally, the ideas for the future come from areas like AI and longtermism. But a lot of vital EA work doesn’t have to be cutting-edge research.

But IMHO there is still a massive opportunity to help most of the world’s population in very concrete, tangible ways, and effective altruists can make vital contributions. You write the the goal of wave 1 was “donations to effective charities”—but this is a quite limited reading of what EA can do. How about influencing how governments spend their aid budgets, which is often very differently from how they would be most effective? There are a few groups doing this kind of work (e.g. Gates Foundation), but there is still so much aid and donations being inefficiently spent. Ideas as simple as how to convince governments to just give people in developing countries cash rather than spending 10X that much trying to solve their problems for them.

I know that part of the vision is to focus on areas which are neglected, but I see a big difference between working in an area that is neglected (which is not true of global poverty and disease, for example) and working in a way that is neglected (quantitative, analytical, data-driven) even in areas which receive a lot of attention and even (badly-spent) money.
Apologies if this feels ill-informed. I’m writing as someone who isn’t in any of the hubs and so just seeing EA from the “outside.”

Denis 26 Jun 2023 11:33 UTC
1 point
0 ∶ 0
on: AGI safety career advice
Loved reading this post, as a person considering working in AI Safety, this is a great resource and answers many questions, including some I hadn’t thought of answering. Thanks so much for writing this!

One question: I am curious to hear anyone’s perspective on the following “conflict”:

Point 1: “There is a specific skill of getting things done inside large organizations that most EAs lack (due to lack of corporate experience, plus lack of people-orientedness), but which is particularly useful when pushing for lab governance proposals. If you have it, lab governance work may be a good fit for you.”

Point 2: “You need to get hands on” and, related: “Coding skill is a much more important prerequisite, though.”

There may be exceptions, but I would guess (partly based on my own experience) that the kind of people who have a lot of experience getting things done in large organisations typically do not spend much time coding ML models.

And yet, as I say, I believe both of these are necessary. If I want to influence a major AI / ML company, I will lack credibility in their eyes if I have no experience working with and in large organisations. But I will also lack credibility if I don’t have an in-depth understanding of the models and an ability to discuss them specifically rather than just abstractly.

Specific question: What might the typical learning curve be for the second aspect, to get to the point where I could get hands on with models? My starting point would be having studied FORTRAN in college (!! - yes, that long ago!) and only having one online course of Python. There may be others with different starting points.
I suppose. ultimately, it still seems likely that it would be quicker even for a total novice to coding to reach some level of meaningful competence than for someone with no experience of organisations to become expert in how decisions are made and plans are approved or rejected, and how to influence this.

Also are there good online courses anyone would recommend?