trentmkelly/LessWrong-43k huggingface dataset:
A Contamination Theory of the Obesity Epidemic
by Bob Baker — 2021-07-25T02:39:14.676Z

This is a summary of a paper that I found open in a browser tab; I don't recall where I came across it. I think it's a nice paper, but it's also 63 pages long and seemed worth a synopsis for those who wouldn't otherwise tackle it.

Scott concluded in For, then Against, High-saturated-fat diets that the obesity crisis seemed to imply one of three answers:

That weight less is really hard and people in previous centuries had really hard lives and that's why there was so little obesity back then.

That it's “being caused by plastics or antibiotics affecting the microbiome or something like that”.

That there is hysteresis—once you become overweight it's semi-permanent.

This paper argues for the second answer, and against the other two.

At the outset, there are reasons to be wary of this paper: neither author (who share a family name) appear to have expertise in applicable fields, and it appears to be set in Computer Modern Roman, hardly the style of a journal submission. So it's coming from outside of traditional expertise. (I don't have any expertise here either.)

With that in mind, the paper starts by posing a series of challenging facts about obesity. (References in the original:)

It's new. One hundred years ago obesity was very rare (~1% of the population) but there were plenty of people who had enough to eat and, from our point of view, ate a lot of fattening foods.

It's not just new, it seemed to suddenly kick off around 1980. “Today the rate of obesity in Italy, France, and Sweden is around 20%. In 1975, there was no country in the world that had an obesity rate higher than 15%”.

It's still getting worse. It's less in the news but if anything it's accelerating in the US. This is despite Americans significantly cutting back on sugars and carbs since 2000.

It's not just humans: lab animals and wild animals appear to be getting fatter over time too. (A surprise to me, but casual inspection seems to confirm that this is really a thing that reviewed papers are noting.)

Junk food from a supermarket fattens rats far more than giving them more of any macro-nutrient does. Somehow junk food is more than the sum of its sugars, proteins, and fats.

Across several countries, living at sea-level seems to increase obesity.

Diets produce modest reductions in weight over the span of weeks or months, but the weight comes back over time. There's been a lot of searching for effe

...
Social media: designed to be bad for you & for society
by matto — 2021-07-24T18:59:00.600Z

After writing this, I wasn't happy enough with the result to post it. However, reading spkoc's comment on a recent COVID update made me think that I should share it because it seems as relevant as it did when I typed it out.

This post is my second attempt to explain and check the worry I feel about social media. The feedback from the first one told me that instead of clarifying my thoughts, I only managed to convey a vague sense of unease. Let's give this one more shot then.

Mainstream opinion of services like Facebook, Twitter, etc. is trending negative. It is said that, on a personal level, they're addictive, wasting people's time and harming their self-esteem. At the group level, they create echo chambers, fuel conspiracy theories, and even enable ethnic cleansing. But I think all of this misses a crucial point–that all these things are connected, parts of a single system, operating within normal parameters. And the negative effects I described? Merely expected by-products, like car exhaust or traffic jams.

This error leads us to underestimate just how much these platforms degrade global epistemic conditions. To illustrate my point, let me describe three mechanisms that I consider core to social media.

As Digital Nicotine

I used to smoke back in high school. It made me feel good–calm but awake–especially between classes when I worried about my grades and my future. Cigarettes also helped connect with other people. Smoking signaled that I was alright, that I wasn't taking life too seriously and that I could be trusted.

Social media feels similar. Seeing photos of friends partying or people liking your post activates some ancient circuits in the brain. The ones that help us work together, build relationships with others, and track our position in hierarchies. The same ones which allowed our ancestors to form stable hunter-gatherer bands, which turned out to be such a good survival strategy that we've taken over the world. That's why it feels so good to to consume all that the social web has to offer: cute babies, pretty bodies, and outrageous news.

It would never have worked out without smartphones. They freed users from stationary, beige boxes that had to be shared with parents or siblings. Suddenly, everyone had a private device, a little portal into the social universe, so they could check in whenever and wherever they were. It was handy, instantaneous relief...
Believing vs understanding
by Adam Zerner — 2021-07-24T03:39:44.168Z

Every year before the NBA draft, I like to watch film on all of the prospects and predict how good everyone will be. And every year, there ends up being a few guys who I am way higher on than everyone else. Never fails. In 2020 it was Precious Achiuwa. In 2019 it was Bol Bol. In 2018 it was Lonnie Walker. In 2017 it was Dennis Smith. In 2016 it was Skal Labissiere. And now this year in 2021, it is Ziaire Williams.

I have Ziaire Williams as my 6th overall prospect, whereas the consensus opinion is more in the 15-25 range. If I'm being honest, I think I probably have him too high. There's probably something that I'm not seeing. Or maybe something I'm overvaluing. NBA draft analysis isn't a perfectly efficient market, but it is somewhat efficient, and I trust the wisdom of the crowd more than I trust my own perspective. So if I happened to be in charge of drafting for an NBA team (wouldn't that be fun...), I would basically adopt the beliefs of the crowd.

But at the same time, the beliefs of the crowd don't make sense to me.

Upside is really important for NBA players. Check this out:

It shows the Championship Odds over Replacement Player (CORP) for Michael Jordan. Role players and sub-all-stars have something like a 2-4% CORP, whereas All-NBA and MVP types have something like a 10-20% CORP. And I don't even think that fully captures the value that these unicorn-type players have. I believe it's easier to build a roster around a unicorn, as one example. So if you can hit on a unicorn, it's huge.

Ziaire Williams is one of the few guys in this draft who I think has that unicorn potential. He's 6'9 with a 6'11 wingspan: great size for a wing. He's got the shake as a ball handler to break his man down off the dribble and create. His shooting isn't there yet, but the fluidity, FT% and tough shot making ability all make me think he can develop into a really good shooter. He doesn't have the ability to get to the rim yet, but his size, explosiveness, fluidity, and shake as a ball handler all make me think that he can totally get there one day. Once he develops physically and becomes a little more savvy, I could see it happening. And then as a passer and defender I think he has the knack and can develop into a moderate plus in both of those areas. Overall, I think he's got All-NBA upside for his ceiling, but also isn't one of those complete boom or bust guys either. I could ...
Housing Without Street Parking: Implemented
by jefftk — 2021-07-24T02:40:02.341Z

In 2017,

I wrote:

What if, in places like Somerville where all parking is already
by-permit-only, we added a new category of housing unit, one that
didn't come with any rights to street parking?

It turns out this was included in the 2019 zoning overhaul (I
missed this among all the other great changes):

11.2.7: On-Street Parking in Transit Areas

Upon the adoption of an official policy limiting on-street
residential parking permits in transit areas, the review
boards shall require the following as a condition(s) of
any discretionary or administrative permit:

that the applicant complete and file formal
acknowledgment that all dwelling unit(s) are
ineligible to participate in the Somerville
Residential Permit Parking program with the with
the Middlesex South Registry of Deeds or Land
court prior to the issuance of a building permit;

that all dwelling units are advertised as ineligible
to participate in the Somerville Residential Permit
Parking program; and

that all buyers grantees, lessees, renters, or
tenants are informed that all dwelling unit(s)
is ineligible to participate in the Somerville
Residential Permit Parking program.

By

transit area they mean everything within half a mile
from a subway stop, which with the Green Line makes

most
of the city:

I'm very excited to see this! While housing without parking is not
what everyone wants or needs, this change (a) makes it an option for
people who do want it and (b) should help reduce opposition to
construction.
AXRP Episode 10 - AI’s Future and Impacts with Katja Grace
by DanielFilan — 2021-07-23T22:10:14.624Z

YouTube link

This podcast is called AXRP, pronounced axe-urp and short for the AI X-risk Research Podcast. Here, I (Daniel Filan) have conversations with researchers about their papers. We discuss the paper and hopefully get a sense of why it’s been written and how it might reduce the risk of artificial intelligence causing an existential catastrophe: that is, permanently and drastically curtailing humanity’s future potential.

When going about trying to ensure that AI does not cause an existential catastrophe, it’s likely important to understand how AI will develop in the future, and why exactly it might or might not cause such a catastrophe. In this episode, I interview Katja Grace, researcher at AI Impacts, who’s done work surveying AI researchers about when they expect superhuman AI to be reached, collecting data about how rapidly AI tends to progress, and thinking about the weak points in arguments that AI could be catastrophic for humanity.

Topics we discuss:

AI Impacts and its research

How to forecast the future of AI

Results of surveying AI researchers

Work related to forecasting AI takeoff speeds

How long it takes AI to cross the human skill range

How often technologies have discontinuous progress

Arguments for and against fast takeoff of AI

Coherence arguments

Arguments that AI might cause existential catastrophe, and counter-arguments

The size of the super-human range of intelligence

The dangers of agentic AI

The difficulty of human-compatible goals

The possibility of AI destroying everything

The future of AI Impacts

AI Impacts vs academia

What AI x-risk researchers do wrong

How to follow Katja’s and AI Impacts’ work

Daniel Filan:
Hello everybody. Today, I’ll be speaking with Katja Grace, who runs AI Impacts, a project that tries to document considerations and empirical evidence that bears on the longterm impacts of sophisticated artificial intelligence. We’ll be talking about her paper, “When Will AI Exceed Human Performance? Evidence From AI Experts, coauthored with John Salvatier, Allan Dafoe, Baobao Zhang, and Owain Evans, as well as AI Impacts’ other work on what the development of human level AI might look like and what might happen afterwards. For links to what we’re discussing you can check the description of this episode, and you can read a transcript a...
Delta variant: we should probably be re-masking
by Aaron Bergman — 2021-07-23T21:13:15.455Z
Link: https://aaronbergman.substack.com/p/delta-variant-we-should-probably

Like you, I thought I saw the light at the end of the tunnel. It first appeared when vaccines started rolling out a few months back, and got brighter when my family got their vaccine cards signed. It got still brighter as I signed up for my first dose (#ModernaManiacs), got the first shot, waited four weeks, and got my second.

After about a week, a few days after the side effects faded, I stopped holding my breath—literally and figuratively—as people walked past. At two weeks post-second dose, I was “officially” “fully vaccinated.” The light had been reached.

My gym had just started allowing those vaccinated to unmask, and I took them up on the offer. It was science to the rescue, human ingenuity encapsulated in some messenger ribonucleic acid in half a milliliter of lipid suspension in the tip of a single-use syringe. Perhaps for the first time in my life, I was viscerally—if mildly—proud to be an American. We are the world’s biotech capital, after all.

I didn’t pay too much attention at first when the Delta variant hit. The narrative in my Twitter-centered media milieux was pretty simple: “Delta’s gonna kill a lot of unvaccinated people, which sucks, but if you’re vaccinated then you’re safe.”

And then my sister, who like me had gotten the Moderna vaccine, lost her sense of smell.

When her COVID test came back positive, she was only the ~700th confirmed COVID case in New York. Given everything we know about the Delta variant, that was almost certainly the culprit.

On Delta

If you haven’t already, please read Tomas Pueyo’s

Delta Variant: Everything You Need to Know

. There are a lot of articles out there competing for your attention, but this one is worth your time.

First, the bad news

In short, Delta is probably about 2.5 times more infectious than the OG COVID-19. Here’s what that looks like in the context of exponential growth:

Fuck.

2. Young people like my sister and me are getting infected the most.

3. Preliminary evidence suggests that vaccines aren’t nearly as effective against Delta, reducing infection rates by 64%. By comparison, they seem to reduce OG infections by around 93%.

Some good news

Vaccines are still really effective against serious illness, reducing hospitalization by 96% best we can tell at the moment.

Pueyo thinks that vaccines will be comparably effective against

long COVID

, bringing the percent of infected people who experience ongoing symptoms dow...
How is low-latency phasic dopamine so fast?
by Steven Byrnes — 2021-07-23T17:43:59.565Z

(Quick poorly-researched blog post, probably only of interest to neuroscientists.)

There’s a paper

“What is reinforced by phasic dopamine signals?” (Redgrave, Gurney, Reynolds 2008)

. I was interested in it as part of my project to continue checking whether I should really believe all the crap I wrote in

Big Picture Of Phasic Dopamine

, and trying to fill in the missing pieces. The paper is also related to a different ongoing project of mine, namely to resolve various confusions I have about the superior colliculus.

Anyway, in a classical conditioning experiment (flash of light then reward), when the flash of light happens, there’s a reward-related phasic dopamine signal. The paper points out that this phasic signal is very fast, starting just 70-100ms after the light (in rodents).

Supposedly this is faster than the cortex can do object identification (“signals related to object identity can be detected in inferotemporal cortex within 80-100ms after stimulus onset”), and also faster than the amygdala (“visual response latencies in the amygdala are…ranging between 100 and 200 ms from stimulus onset with mean value of ~150 ms”). But the superior colliculus (SC) is fast, and it’s right next to and connected to the dopamine neurons in VTA / SNc, and there is various other streams of evidence that SC can cause dopamine release. So that’s their theory! “We have proposed the superior colliculus as the primary, if not exclusive source of short-latency visual input to ventral midbrain DA neurons.”

I found this very confusing. I thought the amygdala learns that the flashing light leads to reward! It’s been studied to death, right? That’s what everyone’s always said, I like that story, it

fits in beautifully with everything else I think I know about the brain

. What the heck??

Anyway, I was relieved to see that Redgrave seems to have changed his mind on this point: a decade later he co-authored

Cortical visual processing evokes short-latency reward-predicting cue responses in primate midbrain dopamine neurons

, which offered experimental evidence that deactivating (part of) SC does not stop the low-latency phasic dopamine reward signal. I don’t know enough to evaluate how water-tight the experiment was, but I guess if Redgrave signed on, after previously arguing the other side, presumably it’s pretty solid evidence.

(I hope I’m understanding this story correctly, and not putting...
[AN #157]: Measuring misalignment in the technology underlying Copilot
by Rohin Shah — 2021-07-23T17:20:03.424Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

Please note that while I work at DeepMind, this newsletter represents my personal views and not those of my employer.

HIGHLIGHTS

Evaluating Large Language Models Trained on Code (Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan et al) (summarized by Rohin): You’ve probably heard of GitHub Copilot, the programming assistant tool that can provide suggestions while you are writing code. This paper evaluates Codex, a precursor to the model underlying Copilot. There’s a lot of content here; I’m only summarizing what I see as the highlights.

The core ingredient for Codex was the many, many public repositories on GitHub, which provided hundreds of millions of lines of training data. With such a large dataset, the authors were able to get good performance by training a model completely from scratch, though in practice they finetuned an existing pretrained GPT model as it converged faster while providing similar performance.

Their primary tool for evaluation is HumanEval, a collection of 164 hand-constructed Python programming problems where the model is provided with a docstring explaining what the program should do along with some unit tests, and the model must produce a correct implementation of the resulting function. Problems are not all equally difficult; an easier problem asks Codex to “increment all numbers in a list by 1” while a harder one provides a function that encodes a string of text using a transposition cipher and asks Codex to write the corresponding decryption function.

To improve performance even further, they collect a sanitized finetuning dataset of problems formatted similarly to those in HumanEval and train Codex to perform well on such problems. These models are called Codex-S. With this, we see the following results:

1. Pretrained GPT models get roughly 0%.

2. The largest 12B Codex-S model succeeds on the first try 29% of the time. (A Codex model of the same size only gets roughly 22%.)

3. There is a consistent scaling law for reduction in loss. This translates into a less consistent graph for...
Hypergames 101
by Josephine — 2021-07-23T13:19:46.620Z

(Status: This may be a relatively well-known topic, but apart from a single footnote I haven't found it mentioned here. For now I would rate my numeracy below the average person I'd expect to read this, so I expect there may be connections or applications left out that would be obvious to a graduate-level audience. For a drier, more rigorous introduction, I'd recommend the Aljefri (2018) paper in the citations.)

Hypergames

Hypergames are game-theoretic objects that model information asymmetry and misperception between players. Each player has a separate payoff matrix, and their preferences, available choices, and even awareness of the number of other players may be different. Because of this, hypergames have been used in the literature to model international conflicts, and to a lesser degree coordination problems.

A 2-person hypergame where one player sees an extra choice for both players the other doesn't. Taken from Kovac, Gibson, Lamont (2015).

A chief feature of a hypergame is that to any individual player it can look and behave just like a classical game, so a player can be playing a hypergame without knowing. It's equally possible, however, to be aware that you're in one and try to model the perceptions of other players before making any decisions yourself. This can be described as trying to determine another player's rank-ordering of the choices available, or their "preference vector", and can lead recursively to agents predicting each other's predictions, given as N-level predictions of (N-1)-level predictions.

Sometimes a hypergame is named after the highest level of prediction in it, making it an "N-level hypergame". If at least one player is aware that a hypergame is happening, then the game itself must be at least 2nd-level.

Hypergames can be reduced into classical games if perfect information is achieved - all the payoffs and choices are simply collated into one matrix and the classical tools can be brought to bear. In cases of conflict this is much harder to achieve, but because of this equilibria are classed as either "hypergame-preserving" or "hypergame-destroying" based on whether all players perceive them as equilibria or some gain unexpected information from them.

One motivation for studying hypergames in particular is stability analysis, the study of decisions in a game that an agent wouldn't opt to change. Some strategies are ...
Decision Duels
by Josephine — 2021-07-23T09:22:13.294Z

(Crosspost from my more casual blog.)

Decision duels are a feature of David's Sling, a novel by Marc Stiegler about technology, nuclear suppression and human rationality. They're used as an organizational means of decision-making, not dissimilar to the double crux - they're not quite debates or policy meetings or games, but they have elements of all three. This is a description of them as they appear in the novel, so that any useful marrow can be extracted.

Duels are best at resolving problems that seem political but are actually engineering problems.

This means that there are, in principle, crisp answers separate from the human element.

Good for: whether a budget is appropriate, which programs to fund, whether to continue a project or stop it, which avenues of research will be fruitful.
Bad for: who deserves a promotion or a leadership position, what an organization's public-facing message should be, which solutions are more ethical.

Duels are always between two alternatives, which are stated outright.
Both sides are displayed on a screen for an audience, with each side taking up nearly half.

A grey section is left to run down the middle for third suggestions.

Duels that settle on third suggestions tend to produce the best policies.
In some duels third suggestions are prohibited, especially when the question is vulnerable to being redefined or slipped out of.

At the top of this screen are the words "LET ACCURACY TRIUMPH OVER VICTORY"

Winners are not recorded at the end of a decision duel, but whenever possible both sides are judged based on whether decision that results was the correct one.

Each alternative has a representative, called a slant moderator or, informally, a decision duelist.

Each may receive suggestions from the audience, and decides whether to use them.
Duelists chiefly create text boxes of various colors and draw lines between them.
There are no turns taken, and each duelist acts at their own pace.

At the start both sides are written as statements, and under these statements are a list of assumptions, placed in amber text boxes.

Assumptions can, and in many cases should, be multi-part.
Zooming in on these amber boxes shows an explanation for why the assumption is needed.
Most of the duel consists of the duelists each challenging these assumptions.

Under the assumptions are the opening arguments.

Any overly popular, bumper-stickerish slogans are usually list

...