Disagree that improving batteries was ever popular vs wonkish. Definitely disagree that average climate change advocates discussed this.
I went to college in 2012-2016 at a liberal arts college (emphasis on liberal, folks certainly cared about climate change). I can’t recall a single conversation about working on battery tech. I can recall several on geoengineering & pumping white sulfur into the atmosphere.
If many native speakers use a word in a way that you think is wrong, you are probably misunderstanding them. They are probably using a different definition of bribe than you.
I feel like many folks use ‘bribe’ to just mean any positive but nonstandard reward for an action.
OOC, what are some pieces of fiction of similar length that you consider peak human level?
I found both of these interesting and quite good. Not earth-shakingly good, but enjoyable. The sentences in the second had a simple, Ernest Hemingway quality I liked (the ending was of course visible a mile away, but that is IMO fine for many stories).
Senior SWE at Alphabet: the complaints read to me like stylistic nits, and not particularly good ones.
Ex:
1) As Zack says, the negative keyword regex is a very reasonable way to (extremely quickly & roughly) get a sense of negative sentiment. Not all sentiment analysis is load bearing, so doing something fast & cheap often makes sense.
2) Complaints about detailed comment explanations is a weird flex. If you are doing something unusual in your code, it is sometimes helpful to include a paragraph explaining why (otherwise later folks need to rederive its purpose).
3) He laughs at the instructions to not introduce security vulnerabilities (and lists specific types). This is IMO a bad take. Reminding ppl (& LLMs) about common error patterns really does help avoid that.
Some of the code is not ideal (very little code in existence is), but the complaints in question IMO have a worse hit rate than if you asked your favorite LLM to critique the code.
I am heart-broken I don’t have more rat friends to share “The Sequences” with. Actual hot fire. Congrats on putting this together.
Donated 5k. I think LessWrong is a big reason why I got into EA, quantified self (which has downstream effects on me getting married), and exposed me to many useful ideas.
I’m not sure about the marginal value of my donation, but I’m viewing this more as payment for services rendered. I think I owe LessWrong a fair amount, since I expect my counter-factual life to be much worse.
I still think that if you want to know where X is on someone’s TODO list, you should ask that instead of asking for their full TODO list. This feels nearly as wrong as asking for someone’s top 5 movies of the year, instead of whether or not they liked Oppenheimer (when you want to know if they liked Oppenheimer).
I don’t think this level of trickery is a good idea.
If you’re working with someone honest, you should ask for the info you want. On the other hand, if you’re working with someone who will obfuscate when asked “Are you working on X?”, I don’t see a strong reason to believe that they will give better info when instead asking about their top priorities.
In regard to Waymo (and Cruise, although I know less there) in San Francisco, the last CPUC meeting for allowing Waymo to charge for driverless service had the vote delayed. Waymo operates in more areas and times of day than Cruise in SF last I checked.
https://abc7news.com/sf-self-driving-cars-robotaxis-waymo-cruise/13491184/
I feel like Paul’s right that the only crystal clear ‘yes’ is Waymo in Phoenix, and the other deployments are more debatable (due to scale and scope restrictions).
You gave the caveats, but I’m still curious to hear what companies you felt had this engineer vs manager conflict routinely about code quality. Mostly, I’d like to know so I can avoid working at those companies.
I suspect the conflict might be exacerbated at places where managers don’t write code (especially if they’ve never written code). My managers at Google and Waymo have tended to be very supportive of code health projects. The discussion of how to trade-off code debt and velocity is also very explicit. We’ve gotten pretty guidance in some quarters along the lines of ‘We are sprinting and expect to accumulate debt’ vs ‘We are slowing down to pay off tech debt’. This makes it pretty easy to tell if a given code health project is something that company leadership wants me to be doing right now.
Agreed, although it feels like in that case we should be comparing ‘donating to X-risk organizations’ vs ‘working at X-risk organizations’. I think that by default I would assume that the money vs talent trade-off is similar at global health and X-risk organizations though.
Fair point that GiveWell has updated their RFMF and increased their estimated cost per QALY.
I do think that 300K EAs doing something equivalent to eliminating the global disease burden is substantially more plausible than 66K doing so. This seems trivially true since more people can do more than fewer people. I agree that it still sounds ambitious, but saying that ~3X the people involved in the Manhattan project could eliminate the disease burden certainly sounds easier than doing the same with half the Manhattan project’s workforce size.
This is getting into nits, but ruling out all arguments of the form ‘this seems to imply’ seems really strong? Like, it naively seems to limit me to only discussing to implications that the argument maker explicitly acknowledges. I’m probably mis-interpreting you here though, since that seems really silly! This is usually what I’m trying to say when I ask about implications; I note something odd to see if the oddness is implied or if I misinterpreted something.
Agreed that X-risk is very important and also hard to quantify.
I’m surprised that you think that direct work has such a high impact multiplier relative to one’s normal salary. The footnote seems to suggest that you expect someone who could get a $100K salary trying to earn to give could provide $3M in impact per year.
I think GiveWell still estimates it can save a life for ~$6K on the margin, which is ~50 QALYs.
(life / $6K) X (50 QALY / life) X ($3 million / EA year) ~= 25K QALY per EA year
Which both seems like a very high figure and seems to imply that 66K EAs would be sufficient to do good equivalent to totally eliminating the burden of all disease (I’m ignoring decreasing marginal returns). This seems like an optimistic figure to me, unless you’re very optimistic about X-risk charities being effective? I’d be curious to hear how you got to the ~3 million figure intuitively.
I would guess something closer to 5-10X impact relative to industry salary, rather than a 30X impact.
Note that it might be very legally difficult to open source much of Space-X technology, due to the US classifying rockets as advanced weapons technology (because they could be used as such).
I’m not sure that contagiousness is a good reason to believe that an (in)action is particularly harmful, outside of the multiplier contagiousness creates by generating a larger total harm. It seems clear that we’d all agree that murder is much worse than visiting a restaurant with a common cold, despite the fact that the latter is a contagious harm.
Although there is a good point that the analogy breaks down because a DUI doesn’t cause harm during your job (assuming you don’t drive in your work), whereas being unvaccinated does cause expected harm to colleagues and customers.
Perhaps too tongue in cheek, but there is a strong theoretical upper bound on R0 for humans as of ~2021. It’s around 8 billion, the current world population.
I think you’re correct that the difference between R0 and Rt is that Rt takes into account the proportion of the population already immune.
However, R0 is still dependent on its environment. A completely naive (uninfected) population of hermits living in caves hundreds of miles distant from one another has an R0 of 0 for nearly anything. A completely naive population of immunocompromised packed-warehouse rave attendees would probably have an R0 of 100+ for measles.
I don’t know if there is another Rte type variable that tries to define the infectiveness of a disease given both the prevalence of immunity and the environment. Seems like most folks just kinda assume that environment other than immune proportion is constant when comparing R0/Rt figures.
A one point improvement (measured on a ten point scale) feels like a massive change to expect. I like the guts to bet that it’ll happen and change your mind otherwise, but I’m curious if you actually expected that scale of change.
For me, a one point change requires super drastic measures (ex. getting 2 hours too few sleep for 3+ days straight). Although I may well be arbitrarily compressing too much of my life into 6-9 on the ten point scale.
The Laffer curve sloping downward (once past the optimal tax rate) does not require agents entering a shadow economy. It is very much possible that agents merely choose not to work, as the cost of marginal effort is not sufficiently rewarded with enough marginal income.
So in the specific example, there is no guarantee some secret agent must emerge. Higher tax rates merely increase the incentive to avoid taxation, they don’t guarantee malfeasance.