ofer's Shortform

Nothing in life is as important as you think it is when you are thinking about it.

--Daniel Kahneman, Thinking, Fast and Slow

To the extent that the above phenomenon tends to occur, here's a fun story that attempts to explain it:

At every moment our brain can choose something to think about (like "that exchange I had with Alice last week"). How does the chosen thought get selected from the thousands of potential thoughts? Let's imagine that the brain assigns an "importance score" to each potential thought, and thoughts with a larger score are more likely to be selected. Since there are thousands of thoughts to choose from, the optimizer's curse [LW · GW] makes our brain overestimate the importance of the thought that it ends up selecting.

[COVID-19 related]

[EDIT: for frequent LW readers this is probably old news. TL;DR: people may catch COVID-19 by inhaling tiny droplets that are suspended in the air. Opening windows is good and being outdoors is better. Get informed about masks.]

There seems to be a growing suspicion that many people catch COVID-19 due to inhaling tiny droplets that were released into the air by infected people (even by just talking/breathing). It seems that these tiny droplets can be suspended in the air (or waft through the air) and may accumulate over time. (It's unclear to me how long they can remain in the air for - I've seen hypotheses ranging from a few minutes to 3 hours.)

Therefore, it seems that indoor spaces may pose much greater risk of catching COVID-19 than outdoor spaces, especially if they are poorly ventilated. So consider avoiding shared indoor spaces (especially elevators), keeping windows open when possible, and becoming more informed about masks.

[Question about reinforcement learning]

What is the most impressive/large-scale published work in RL that you're aware of where—during training—the agent's environment is the real world (rather than a simulated environment)?

It seems that the research team at Microsoft that trained Turing-NLG (the largest non-sparse language model other than GPT-3, I think) never published a paper on it. They just published a short blog post, on February. Is this normal? The researchers have an obvious incentive to publish such a paper, which would probably be cited a lot.

[EDIT: hmm maybe it's just that they've submitted a paper to NeurIPS 2020.]

[EDIT 2: NeurIPS permits putting the submission on arXiv beforehand, so why haven't they?]

PSA for Edge browser users: if you care about privacy, make sure Microsoft does not silently enable syncing of browsing history etc. (Settings->Privacy, search and services).

They seemingly did so to me a few days ago (probably along with the Windows "Feature update" 20H2); it may be something that they currently do to some users and not others.

I'm curious how antitrust enforcement will be able to deal with progress in AI. (I know very little about antitrust laws.)

Imagine a small town with five barbershops. Suppose an antitrust law makes it illegal for the five barbershop owners to have a meeting in which they all commit to increase prices by $3.

Suppose that each of the five barbershops will decide to start using some off-the-shelf deep RL based solution to set their prices. Suppose that after some time in which they're all using such systems, lo and behold, they all gradually increase prices by $3. If the relevant government agency notices this, who can they potentially accuse of committing a crime? Each barbershop owner is just setting their prices to whatever their off-the-shelf system recommends (and that system is a huge neural network that no one understands at a relevant level of abstraction).

[COVID-19 related]

(Probably already obvious to most LW readers.)

There seems to be a lot of uncertainty about the chances of COVID-19 causing long-term effects (including for young healthy people who experience only mild symptoms). Make sure to take this into account when deciding how much effort you're willing to put into not getting infected.

Uneducated hypothesis: All hominidae species tend to thrive in huge forests, unless they've discovered fire. From the moment a species discovers fire, any individual can unilaterally burn the entire forest (due to negligence/anger/curiosity/whatever), and thus a huge forest is unlikely to serve as a long-term habitat for many individuals of that species.

There are ~4 minutes of Sam Harris & Lex Fridman talking about existential risks from AI that I liked a lot; starting here: https://youtu.be/4dC_nRYIDZU?t=8785

[COVID-19 related]

It was nice to see this headline:

https://www.theguardian.com/world/2021/jan/18/germany-weighs-up-mandatory-ffp2-masks-in-shops-and-on-transport

My own personal experience with respirators is that one with headbands (rather than ear loops) and a nose clip + nose foam is more likely to seal well.

[Online dating services related]

The incentives of online dating service companies are ridiculously misaligned with their users'. (For users who are looking for a monogamous, long-term relationship.)

A "match" between two users that results in them both leaving the platform for good is a super-negative outcome with respect to the metrics that the company is probably optimizing for. They probably use machine learning models to decide which "candidates" to show a user at any given time, and they are incentivized to train these models to avoid matches that cause users to leave their platform for good. (And these models may be way better at predicting such matches than any human).

[researcher positions at FHI] 

(I'm not affiliated with FHI.)

FHI recently announced: "We have opened researcher positions across all our research strands and levels of seniority. Our big picture research focuses on the long-term consequences of our actions today and the complicated dynamics that are bound to shape our future in significant ways. These positions offer talented researchers freedom to think about the most important issues of our era in an environment with other brilliant minds willing to constructively engage with a broad range of ideas. Applications close 19th October 2020, noon BST."

Paul Christiano's definition of slow takeoff may be too narrow, and sensitive to a choice of "basket of selected goods".

(I don't have a background in economy, so the following may be nonsense.)

Paul Christiano operationalized slow takeoff as follows:

There will be a complete 4 year interval in which world output doubles, before the first 1 year interval in which world output doubles. (Similarly, we’ll see an 8 year doubling before a 2 year doubling, etc.)

My understanding is that "world output" is defined with respect to some "basket of selected goods" (which may hide in the definition of inflation). Let's say we use whatever basket the World Bank used here.

Suppose that X years from now progress in AI makes half of the basket extremely cheaper to produce, but makes the other half only slightly cheaper to produce. The increase in the "world output" does not depend much on whether the first half of the basket is now 10x cheaper or 10,000x cheaper. In both cases the price of the basket is dominated by its second half.

If the thing we care about here is whether "incredibly powerful AI will emerge in a world where crazy stuff is already happening (and probably everyone is already freaking out)"—as Paul wrote—we shouldn't consider the above 10x and 10,000x cases to be similar.

[Coronavirus related]

If some organization had perfect knowledge about the location of each person on earth (at any moment); and got an immediate update on any person that is diagnosed with the coronavirus, how much difference could that make in preventing the spread of the coronavirus?

What if the only type of action that the organization could take is sending people messages? For example, if Alice was just diagnosed with the coronavirus and 10 days ago she was on a bus with Bob, now Bob gets a message: "FYI the probability you have the coronavirus just increased from 0.01% to 0.5% due to someone that was near you 10 days ago. Please self-quarantine for 4 days." (These numbers are made up, obviously.)

It seems likely that as technology progresses and we get better tools for finding, evaluating and comparing services and products, advertising becomes closer to a zero-sum game between advertisers and advertisees.

The rise of targeted advertising and machine learning might cause people who care less about their privacy (e.g. people who are less averse to giving arbitrary apps access to a lot of data) to be increasingly at a disadvantage in this zero-sum-ish game.

Also, the causal relationship between 'being a person who is likely to pay above-market prices' and 'being offered above-market prices' may gradually become stronger.