Open & Welcome Thread - February 2020

Statements of (purported) empirical fact are often strong Bayesian evidence of the speaker's morality and politics (i.e., what values one holds, what political coalition one supports), and this is a root cause of most group-level bad epistemics [LW · GW]. For example someone who thinks eugenics is immoral is less likely (than someone who doesn't think that) to find it instrumentally useful to make a statement like (paraphrasing) "eugenics may be immoral, but is likely to work in the sense that selective breeding works for animals", so when someone says that, it is evidence for them not thinking that eugenics is immoral and therefore not belonging to a political coalition that holds "eugenics is immoral" as a part of its ideology.

I think for many people this has been trained into intuition/emotion (you automatically think that someone is bad/evil or hate them if they express a statement of fact that your political enemies are much more likely to make than your political allies) or even reflexive policy (automatically attack anyone who makes such statements).

This seems pretty obvious to me, but some people do not seem to be aware of it (e.g., Richard Dawkins seemed surprised by people's reactions to his tweet linked above) and I haven't been able to find any discussion on LW about this.

(Given the above, perhaps it shouldn't be surprising that bad social epistemics abound, and what needs explaining is how good epistemic norms like "free inquiry" and "civil discourse" ever became a thing.)

Anyone else look at the coronavirus outbreak and think this is how a future existential accident will play out, with policy always one step behind what's necessary to control it, because governments don't want to take the risk of "overreacting" and triggering large or very large political and economic costs "for no good reason". So they wait until absolutely clear evidence emerge, by which time it will be too late.

Why will governments be more afraid of overreacting than underreacting? (Parallel: We don't seem to see governments doing anything in this outbreak that could be interpreted as overreacting.) Well, every alarm up to that point will have been a false alarm. (Parallel: SARS, MERS, Ebola, every flu pandemic in recent history.)

Global equity markets may have underestimated the economic effects of a potential COVID-19 pandemic because the only historical parallel to it is the 1918 flu pandemic (which is likely worse than COVID-19 due to a higher fatality rate) and stock markets didn't drop that much. But maybe traders haven't taken into account (and I just realized this) that there was war-time censorship in effect which strongly downplayed the pandemic and kept workers going to factories, which is a big disanalogy between the two cases, so markets could drop a lot more this time around. The upshot is that maybe it's not too late to short the markets.

Offering 100-300h of technical work on an AI Safety project

I am a deep learning engineer (2y exp), I currently develop vision models to be used on satellite images (I also do some software engineering around that) (Linkedin profile https://www.linkedin.com/in/maxime-riche-73696182/). On my spare time, I am organizing a EA local group in Toulouse (France), learning RL, doing a research project on RL for computer vision (only expecting indirect utility from this) and developing an EAA tool (EffectiveAnimalAdvocacy). I have been in the French EA community for 4 years. In 2020, I chose to work part time to dedicate 2 to 3 days of work per week to EA aligned projects.Thus for the next 8 months, I have ~10h / week that I want to dedicate to assist an AI safety project. For myself, I am not looking for funds, nor to publish myself a paper, nor a blog post.To me the ideal project would be:

• a relevant technical AI safety project (research or not). I am looking for advice on the "relevant" part.
• where I would be able to help the project to achieve better quality results than otherwise without my contribution. (e.g. through writing better code, doing more experiments, testing other designs)
• where I can learn more about technical AI safety
• where my contribution would include writing code. If it is a research proposal, then implement experiments. If there is no experimental part currently in the project, I could take charge of creating one.

I think I figured out a stunningly simple way to modulate interstellar radio signals so that they contain 3-D spatial information on the point of origin of an arbitrarily short non-repeating signal. I applied this method to well-known one-off galactic radio transients and got sane results. I would love to write this up for ARXIV.

Anybody got a background in astronomy that can help make sure I write this up for ARXIV in a way that uses the proper terminology and software?

A descriptive model of moral change, virtue signaling, and cause allocation that I thought of in part as response to Paul Christiano's Moral public goods . (It was previously posted [LW(p) · GW(p)] deep in a subthread and I'm reposting it here to get more attention and feedback before possibly writing it up as a top-level post.)

1. People are socially rewarded for exhibiting above average virtue/morality (for certain kinds of virtue/morality that are highly regarded in their local community) and punished for exhibiting below average virtue/morality.
2. As a result, we evolved two internal mechanisms: preference alteration (my own phrase) and preference falsification. Preference alteration is where someone's preferences actually change according to the social reward gradient, and preference falsification is acting in public according to the reward gradient but not doing so in private. The amounts of preference alteration and preference falsification can vary between individuals. (We have preference alteration because preference falsification is cognitively costly, and we have preference falsification because preference alteration is costly in terms of physical resources.)
3. Preference alteration changes one's internal "moral factions" to better match what is being rewarded. That is, the factions representing virtues/morals being socially rewarded get a higher budget.
4. For example there is a faction representing "family values" (being altruistic to one's family), one for local altruism, one for national altruism, one for global altruism, one for longtermism, etc., and #3 is mainly how one ends up allocating resources between these factions, including how much to donate to various causes and how to weigh considerations for voting.
5. In particular, public goods and other economics considerations do not really apply (at least not directly), as far as relative spending across different moral factions, because resources are allocated through budgets rather than weights in a utility function. For example if global anti-poverty suddenly becomes much more cost effective, one doesn't vote or donate to spend more on global poverty, because the budget allocated to that faction hasn't changed. Similarly, larger countries do not have higher ODA as the public goods model predicts. Instead the allocation is mostly determined by how much global altruism is rewarded by one's local community, which differs across communities due to historical contingencies, what kinds of people make up the community, etc.
6. On top of this, preference falsification makes one act in public (through, e.g., public advocacy, publicly visible donations, conspicuously punishing those who fall short of the local norms) more like someone who fully subscribes to the virtues/morals being socially rewarded, even if one's preference alteration falls short of that. [Added: This is probably responsible for purity spirals / runaway virtue signaling. E.g., people overcompensate for private deviations from moral norms by putting lots of effort into public signaling including punishing norm violators and non-punishers, causing even more preference alteration and falsification by others.]
7. This system probably evolved to "solve" local problems like local public goods and fairness within the local community, but has been co-opted by larger-scale moral memeplexes.
8. "Rhetoric about doing your part" is how communities communicate what the local norms are, in order to trigger preference alteration. "Feelings of guilt" is what preference alteration feels like from the inside. [Added: This is referring to some things Paul said earlier in that subthread.]

After the hospitals fill up, the COVID-19 death rate is going to get a lot higher. How much higher? What's the fatality rate from untreated COVID-19?

This article may be an answer: it lumps together ICU, mechanical ventilation, and death into a "primary composite end point". That seems like an OK proxy for "death without treatment", right?

If so, Table 1 suggests fatality rate of 6% overall, 0% ages 0-14, 2% ages 15-49, 7% ages 50-64, 21% ages 65+. There's more in the table about pre-existing conditions and so on.

(ETA one more: 2.5% for people of all ages with no preexisting condition.)

Thoughts? Any other data?

(ETA: This should be viewed as a lower bound on fatality rate, see comments.)

Is there something you think we can all do on LessWrong can do to help with the coronavirus? 

We have a justified practical advice thread [LW · GW] and some solid posts about quarantine prepations [LW · GW], not acting out of social fears [LW · GW], and a draft model of risks from using delivery services [LW · GW].

We also have a few other questions:

• What should my triggers be for quarantining? [LW · GW]
• Is this a good or bad time for extended travel? [LW · GW]
• What will be the big-picture implications if this affects 10%+ of the world? [LW · GW]
• Will the coronavirus increase use of blockchain? [LW · GW]

Finally, here's the advice that my house and some friends put together.

I'm interested if people have ideas for better ways we could organise info on LessWrong or something.

Steven Pinker is running a general-education course on rationality at Harvard University. There are some interesting people booked as guest lecturers. Details on Pinker's website, including links that will get you to video of all the lectures (there have been three so far).

I've watched only the first, which suggests unsurprisingly that a lot of the material will be familiar to LW regulars.

An observation on natural language being illogical: I've noticed that at least some native Chinese speakers use 不一定 (literally "not certain") to mean "I disagree", including when I say "I think there's 50% chance that X." At first I was really annoyed with the person doing that ("I never said I was certain!") but then I noticed another person doing it so now I think it's just a standard figure of speech at this point, and I'm just generally annoyed at ... cultural evolution, I guess.

Google's AI folks have made a new chatbot using a transformer-based architecture (but a network substantially bigger than full-size GPT2). Blog post; paper on arXiv. They claim it does much better than the state of the art (though I think everyone would agree that the state of the art is rather unimpressive) according to a human-evaluated metric they made up called "sensibleness and specificity average", which means pretty much what you think it does, and apparently correlates with perplexity in the right sort of way.

I'd be curious how people relate to this Open Thread compared to their personal ShortForm posts. I'm trying to get more into LessWrong posting and don't really understand the differences between these.

This has probably already been discussed, and if so please link me to that discussion if it's easy.

I noticed that even though I may not be as optimized in the matter of investments as others (hi, Wei Dai!), the basic rationality principles still help a lot. This morning, when I went to invest my usual chunk of my paycheck, I reflected on my actions and realized that the following principles were helping me (and had helped me in the past) buy stuff that were likely undervalued:

• pre-commitment (to a certain fund allocation)
• think about it for more than 5 min (to putting in the up front leg work and reading to determine my investing approach)
• use the try harder, Luke (repetitively following the investment plan week after week even in bad times; overcoming changes in brokerages and entire financial firms going away)

It's nice to have dramatic examples of "rationalists should win" from other people. But for me, nothing beats personal experience with even small examples of "winning".

Further alarming evidence of humanity's inability to coordinate (especially in an emergency), and also relevant to recent discussions around terminology: ‘A bit chaotic.’ Christening of new coronavirus and its disease name create confusion

Hi! I have been reading lesswrong for some years but have never posted, and I'm looking for advice about the best path towards moving permanently to the US to work as a software engineer.

I'm 24, single, currently living in Brazil and making 13k a year as a full stack developer in a tiny company. This probably sounds miserable to a US citizen but it's actually a decent salary here. However, I feel completely disconnected from the people around me; the rationalist community is almost nonexistent in Brazil, specially in a small town like the one I live. In larger cities there's a lot of crime, poverty and pollution, which makes moving and finding a job in a larger company unattractive to me. Add that to the fact that I could make 10x what I make today at an entry level position in the US and it becomes easy to see why I want to move.

I don't have formal education. I was approved at University of São Paulo (Brazil's top university) when I was 15 but I couldn't legally enroll, so I had to wait until I was approved again at 17. I always excelled at tests, but hated attending classes, and thought classes were progressing too slowly for me. So I dropped out the following year (2014). Since then, I taught myself how to program in several languages and ended up in my current position.

The reason I'm asking for help is that I think it would save me a lot of time if someone gave me the right pointers as to where to look for a job, which companies to apply to, or if there's some shortcut I could take to make that a reality. Ideally I'd work in the Bay Area, but I'd be willing to move anywhere in the US really, at any living salary (yeah I'm desperate to leave my current situation). I'm currently applying to anything I can find on Glassdoor that has visa sponsorship.

Because I'm working in a private company I don't have a lot to show to easily prove I'm skilled (there's only the company apps/website but it's hard to put that in a resume), but I could spend the next few months doing open source contributions or something that I could use to show off. The only open source contribution I currently have is a fix to the Kotlin compiler.

Does anyone have any advice as to how to proceed or has done something similar? Is it even feasible, will anyone hire me without a degree? Should I just give up and try something else? I have also considered travelling to the US with a tourism visa and looking for a job while I'm there, could that work (I'm not sure if it's possible to get work visa when already in the US)?

ETA: It's out of stock again just a couple of hours later, but you can sign up for be notified when it's back in stock.

Possible source of medicine for COVID-19. Someone in the FB group Viral Exploration suggested inhousepharmacy.vu as an online pharmacy to buy medicine without prescription. I don't know them personally but they seem trustworthy enough. (ETA: Another seemingly trustworthy person has also vouched for it.) Hydroxychloroquine has been out of stock until a few minutes ago. I bought some myself in case the medical system get overwhelmed. Relevant links:

• https://www.ncbi.nlm.nih.gov/pubmed/32075365
• https://www.who.int/blueprint/priority-diseases/key-action/Table_of_therapeutics_Appendix_17022020.pdf
• https://www.inhousepharmacy.vu/p-1106-plaquenil-tablets-200mg.aspx

As of right now, I think that if business-as-usual continues in AI/ML, most unskilled labor in the transportation/warehousing of goods will be automatable by 2040.

Scott Anderson, Amazon’s director of Robotics puts it at over 10 years. https://www.theverge.com/2019/5/1/18526092/amazon-warehouse-robotics-automation-ai-10-years-away.

I don’t think it requires any fundamental new insights to happen by 2040, only engineering effort and currently available techniques.

I believe the economic incentives will align with this automation once it becomes achievable.

Transportation and warehousing currently accounts for ~10% of US employment.