trentmkelly/LessWrong-43k huggingface dataset:
Cognitive Impacts of Cocaine Use
by Xodarap — 2021-07-31T21:53:05.575Z

This post summarizes

The Long-term Effects of Cocaine Use on Cognitive Functioning: A Systematic Critical Review

. The punchline is:

The current evidence does not support the view that chronic cocaine use is associated with broad cognitive deficits. The view that cocaine users have broad cognitive deficits is inaccurate based upon current evidence, and the perpetuation of this view may have negative implications for treatment programs and development of public policies.

This is of interest to me, because cocaine may have short-term benefits as a stimulant, and may have fewer cognitive impacts than alcohol. I am not an expert in this field, and am mostly posting in the hopes that

Cunningham’s Law

will hold and someone will correct my misunderstandings.

My Summary

Neurotoxicity

Cocaine may lead to increases in free radicals, oxidative stress, catalase activity etc. These precursors are used synonymously with “neurotoxicity” by e.g.

this paper

.
But cocaine use does not seem to lead to long-term depletion of relevant neurotransmitters. This is especially remarkable given that tests on nonhuman animals usually involve absurdly large dosages.
Normal human usage also has several differences with experimental procedures which make human use less likely to be neurotoxic: slow ramp-up (rather than having a huge dose the first time you use it) and dosing at will (rather than having a large dose forced on you when you aren’t feeling well).
Question: if cocaine was causing oxidative stress DNA damage (e.g. as suggested

here

), how likely is it that we would see a decrease in neurotransmitters large enough to be measurable in the usual study formats?

Changes in the prefrontal cortex

Under very large dosages, changes in the prefrontal cortex are observed in nonhuman animals: synaptic excitability, glucose metabolism, etc. These changes are accompanied by measurable differences in cognitive ability.
Longitudinal studies in nonhuman primates showed decrease in cognitive performance after cocaine use, but differences had disappeared by three months of abstinence. After 20 months, cocaine-experienced monkeys had significantly greater metabolic activity in the cerebellum, but no difference in task performance.
“Considered together, converging evidence suggests that the compensatory neuroplastic changes associated with chronic cocaine exposure likely create conditions

...
Very Unnatural Tasks?
by Orfeas — 2021-07-31T21:22:54.324Z

In popular AGI takeoff scenarios, AGIs end up doing something "unnatural" where the complexity of the goal is completely orthogonal to their ability to fulfill that goal. I'm looking for a scenario in which an AGI's objective function is "very unnatural". Not maximizing paperclips, building companies, or performing content recommendation, but something distinctly un-human. Thank you!
Cherry Pit Storage
by jefftk — 2021-07-31T17:30:05.338Z

When eating cherries, what do you do with the pits? You don't want to
swallow them, but spitting them out one by one is inconvenient. The
answer: use your upper lip.

Eat the cherry as normal, separating the pit with your teeth and
tongue as usual, but once the pit is free use your tongue to tuck it
up under your upper lip. Once it's there, it will stay safely out of
the way while you chew and swallow the rest of the cherry. You can
repeat this several times; depending on your mouth geometry you can
probably hold six to ten pits.

Asking around and looking online, it seems a lot of people don't know
you can do this? I think this is because the pits are not especially
visible, and the only clue someone is doing this is that they're
needing to remove pits much less often.
The World is Continuous, Not Discrete
by Anshul Jain — 2021-07-31T02:26:16.056Z
Link: https://www.anshuljain.me/the-world-is-continuous-not-discrete/

We have a bias towards using discrete mental models over continuous ones because they're more convenient. But they're not always the best choice.

I've heard a lot of different definitions for "dead". Some people say living things have souls, and they die when the soul leaves. Other people say death is when the heart stopped beating. Some people say it's when the "brain dies".

None of these definitions ever satisfy me. There are always questions people can't answer. What about someone whose heart stops beating but starts again? Can we look at a scan of someone's brain and point to the moment they die? What if we revive someone from cryonic suspension - does that mean the soul never left?

You could say this is a question of definitions. If we define death as "heart not beating" then we can definitively say when someone is dead. Of course, this would mean that we can bring people back to life after they've died because CPR works when you are "heart-stopped-dead". But what if someone is too far gone, and we can't bring them back? What if they're "actually-dead", then what's the definition of that kind of dead? We're back to square one.

The problem is that alive and dead aren't binary states where something is either dead or not. Instead, it's a continuous spectrum of "more dead" to "less dead". You could measure it as "probability we can bring back to life". The question of "what does it mean to die" doesn't make sense because there is no single point where someone is fully dead. Why was this not obvious to begin with?

Language is discrete by nature. Words usually represent distinct states and ideas because they themselves are discrete. Describing continuity is an abstract task where we draw a line between two discrete points using multiple words. For example to describe the spectrum of alive to dead I need you to visualise each endpoint and the continuum between them. Because language is discrete, a lot of our communication and thinking also becomes discrete. This is at odds with the world, which is mostly continuous.

I'm not making a claim about the nature of reality or the underlying physics. That's above my pay grade. I'm saying, at the level of abstraction we operate at, the world mostly acts as if it is continuous. Yes, some things are discrete, like the number of trees in a forest or the number of buildings in a city. But for the most part, moments in time, obj...
A Framework for Disagreements
by Anshul Jain — 2021-07-31T01:51:59.365Z
Link: https://www.anshuljain.me/a-framework-for-disagreements/

When people disagree about a decision, they are either disagreeing about 1) the information they have, 2) what they value, or both.

As a child, I noticed a lot of the disagreements children had, were over what was "better". Decisions like which board game to play, which movie to watch, or what food to eat came down to who could convince most of the group of what was "better". The problem was that there usually wasn't a clear winner for anything. How do you decide if Pokemon is better than Dragon Ball Z? I eventually realised that "better" actually just meant "I feel like doing this", and the reasons which followed didn't matter much at all. It seemed like a terrible way to come to group decisions, but I decided I could just avoid disagreements until I was older. Surely adults had a better way of doing this, right?

The Framework

Disagreements are either about 1) the information each party has, 2) what the parties value, or both. When people disagree about something, they haven't yet found the core of their disagreement and often debate points separate from it.

People should never agree to disagree

. The key to resolving a disagreement is to understand if there is either a gap in values or information.

Values

People who value different things in a decision are more likely to have different goals, which means they're more likely to disagree.

Imagine you're deciding where to eat dinner with a friend. You think the Thai place down the road is really good and suggest you eat there, meanwhile, your friend has recently discovered a new Italian place they think is better. "My place has a larger menu," you say. "Sure, but mine is better value for money," says your friend. You go back and forth, debating travel time, atmosphere, and novelty amongst other things but still ending in a stalemate.

This is a disagreement of values. You might value variety in a menu, short travel times, and a more casual vibe over the things which your friend values - good value, an upscale atmosphere, and a more novel experience. You might even just feel like Thai tonight.

It's very hard to change what someone values, and almost impossible in a short period of time. This means value-based disagreements are best resolved by finding an option that satisfies what all parties value. It's hard to do this when all parties are optimising for many variables, but if you can reduce it to the one or two most i...
Uncertainty can Defuse Logical Explosions
by J Bostock — 2021-07-30T12:36:29.875Z

Principle of Explosion

The principle of explosion goes as follows: if we assume contradictory axioms then we can derive everything. Numbers prefixed with A are axioms/assumptions, those prefixed with D are derived from those.

A1 P
A2 ¬P
D2 (P∨Q) from A1
D3 (P∨Q)∧¬P⟹Q from A2 and D3

In English: we assume both A is true and A is not true for some A. Then for any P, since we know A, we have (P or A) is true. But since we know not A is true, we know P must be true.

This can cause issues in counterfactual reasoning: an agent considering the following counterfactual might fall prey to the following issue in the five and ten problem:

A1 I am an agent which will take the greater of $5 and $10
A2 $10 > $5
A3 I take $5
D4 Either ($5 > $10) or P
D5 P from A2 and D4

This makes it impossible for the agent to reason about worlds in which its actions contradict with its knowledge of itself. This can be thought of a logical collision: the axioms 1 and 2 collide with counterfactual 3. I think this is related to issues of Lob's theorem but not quite the same. In Lobian problems the agent must reason about itself as a proof-searching machine. In this the agent only needs to reason about itself as a rational agent. Obviously humans do not normally make mistakes this egregious.

In mathematics, the Riemann Hypothesis has yet to be solved, that is, proved or disproved. A few different theorems have been proven to be true by an ingenious method: prove them true in the case that the Riemann Hypothesis is true, then prove them true in the case that the Riemann Hypothesis is false! This does the job of proving the theorem in question, but it introduces a wrinkle: one of the proofs might well be explosion-like. This illustrates that there is no good reason for the contradiction to be obvious. In fact if the Riemann Hypothesis is (as some people have suggested) unprovable, then there will be no logical contradiction involved in the derivation of either one!

What does it even mean for a proof to be explosion-like? We can make logical-looking proofs based on incorrect assumptions. Even applying a general theorem to a special case does this. The proof ...
Prediction-based-Medicine instead of E̵v̵i̵d̵e̵n̵c̵e̵ ̵b̵a̵s̵e̵d̵ ̵M̵e̵d̵i̵c̵i̵n̵e̵ Authority-based-Medicine
by ChristianKl — 2021-07-30T10:42:25.243Z

This article explores how evidence-based Medicine currently works in 2021 with ivermectin as a case study. I don't make any argument for or against ivermectin but look at the deeper issues about how we decide which drug and expert to trust. First I explore the status quo and then I will present Prediction-based Medicine as an alternative to the status quo.

The idea of Evidence based Medicine

In the last three decades the idea of Evidence-based Medicine arose. A large part of what Evidence-based Medicine is about is doctors reading the medical literature and making treatment decisions based on the evidence available in the literature.

There's

no study

that showed any benefit of doctors reading the medical literature, so instead of providing evidence that Evidence-based Medicine works based on the standards of evidence that Evidence based Medicine proposed, the framework gets accepted without empiric evidence and is only backed up by intellectual arguments. For a long-time that wasn't a real problem and everybody more or less agreed on what the dogma of Evidence based Medicine is supposed to mean.

Ivermectin

At the moment we have two meta-analyses

about

Ivermectin

who went through competent peer review before getting published. With competent peer review I mean review by people who are competent and familiar enough with the subject to spot when

control and placebo

get exchanged in the reporting of one trial.

The two competently peer reviewed meta-analyses are published in

Reviews in Medical Virology

and

American Journal of Therapeutics

who have impact factors of 4.221 and 1.955 which suggests that both normal journals and not obscure ones that are willing to publish anything. Given that more prestigious journals are more likely to

publish wrong data

, calling for meta analysis is good enough and it doesn't make sense to require it to be published in more prestigious journals.

While the two meta-analyses conclude that there's moderate-certainty evidence for ivermectin working, authorities like the FDA, CDC, WHO and EMA reject ivermectin as a treatment.

As a doctor deciding how to treat a patient, the doctor has to make a decision what to trust. While we can make theoretical arguments about whom we should trust, it's essentially about the question in which authority you want to believe. There's no empiric evidence for a heuristic that's proven to help us decide whom to t...
What weird treatments should people who lost their taste from COVID try?
by Maxwell Peterson — 2021-07-30T02:51:25.598Z

My friend had COVID in September 2020 and never got her taste back. What low-risk random things should she try doing to get it back? Recommendations need not be backed by anything stronger than “a friend of a friend heard…” or “I saw it on TikTok” - I’m looking for a broad library of things for her to consider trying. She is sane and is not the type to drink bleach or whatever, so don’t worry about recommending questionable things.
What's your personal TAP setup?
by Raj Thimmiah — 2021-07-29T22:38:27.533Z

I'd like to keep up a consistent TAP habit where I:

-routinely add new TAPs

-check that I'm performing existing TAPs

-re-rehearse TAPs that seem to have fallen on the wayside

I have some ideas about how to implement this but I'm curious to here: what setups do you guys use?
Brief thoughts on inefficient writing systems
by Srdjan Miletic — 2021-07-29T20:20:33.594Z

A phonemic language is one where how you write is very similar to how things sound. Essentially, if you know how to say something and you know the alphabet, then you can write/read the same thing. Serbian is an example of a phonemic language. A non-phonemic language is one where words are not written how they sound. English is a non-phonemic language. Non phonemic languages are harder to acquire literacy in. Children learning to read and write in the UK have to learn a host of complex rules, exceptions and individual word spellings. Finally a logogramatic language is one where the alphabet doesn't represent sounds. Instead symbols usually represent words or ideas. Chinese is such a language. If you want to learn how to read/write it to any reasonable level, you'll need to know at least 3000 characters.

The continued existence of non-phonemic writing systems is a suboptimal equilibrium. Such writing systems make learning to be literate in a language far harder than it needs to be. They also probably have very serious costs. They affect hundreds of millions of people and, given that even in first world countries between 10% and 25% of adults are functionally illiterate (remember that your bubble is strong), their costs in terms of lost productivity and individual flourishing are likely large. Still I've never heard anyone talk about language reform. Why is that?

One theory is elite blindness. Almost everyone in the elite is fully literate. You don't get to be a journalist/professor/union leader/politician/NGO worker if you're not literate or have a low IQ (which strongly correlates with literacy). Because our personal bubbles tend to be incredibly strong, most elites are hence surrounded by people similar to them or at most a standard deviation or two away in terms of IQ. Hence most people with power will not only never struggle with literacy themselves but they will also never meaningfully interact to the 10 - 20% of society which does. Hence the problem is essentially invisible to everyone who matters.

Another theory is sunk costs. Even if we did recognize the problem with bad writing systems, there's a massive sunk cost in terms of how many people use the current language and how hard it is to shift that usage to a different form. It would take a national campaign, a large amount of resources and a huge political effort. The problem seems intractable, hence n...
[AN #158]: Should we be optimistic about generalization?
by Rohin Shah — 2021-07-29T17:20:03.409Z

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

A naive alignment strategy and optimism about generalization (Paul Christiano) (summarized by Rohin): We want to build an AI system that answers questions honestly, to the best of its ability. One obvious approach is to have humans generate answers to questions, select the question-answer pairs where we are most confident in the answers, and train an AI system on those question-answer pairs.

(I’ve described this with a supervised learning setup, but we don’t have to do that: we could also learn from comparisons between answers, and we only provide comparisons where we are confident in the comparison.)

What will the AI system do on questions where we wouldn’t be confident in the answers? For example, questions that are complex, where we may be misled by bad observations, where an adversary is manipulating us, etc.

One possibility is that the AI system learned the intended policy, where it answers questions honestly to the best of its ability. However, there is an instrumental policy which also gets good performance: it uses a predictive model of the human to say whatever a human would say. (This is “instrumental” in that the model is taking the actions that are instrumental to getting a low loss, even in the test environment.) This will give incorrect answers on complex, misleading, or manipulative questions -- even if the model “knows” that the answer is incorrect.

Intuitively, “answer as well as you can” feels like a much simpler way to give correct answers, and so we might expect to get the intended policy rather than the instrumental policy. This view (which seems common amongst ML researchers) is optimism about generalization: we are hoping that the policy generalizes to continue to answer these more complex, misleading, manipulative questions to the best of its ability.

Are there reasons to instead be pessimistic about generalization? There are at least three:

1. If the answers we train on aren’t perfectly c...
Covid 7/29: You Play to Win the Game
by Zvi — 2021-07-29T16:10:01.807Z

Few things warm my heart more than playing to win the game. Few things sadden me more than observing someone not playing to win the game.

Usually, that means they are playing a different game instead, whether they admit it or not, and whether or not they know it themselves. The game, from this perspective, is simply that which you are playing to win, and your revealed preferences define the game’s rules and objectives.

This week saw some excellent playing to win the game. The NFL, many parts of the government and a number of corporations began imposing vaccine mandates, hopefully causing a cascading effect. It’s at least a start. There’s a lot of ways in which we collectively are revealing ourselves not to be playing the game of ‘get people vaccinated’ let alone the game of ‘prevent Covid while minimizing costs.’ The lowest hanging fruit remains fully approving the vaccines, which we somehow still have not done.

A central question continues to be how effective the vaccines are against Delta. The Israeli claims turn out to probably be the result of basic statistical mistakes, so those scary numbers are now mostly off the table, although that still leaves us a range containing meaningfully distinct answers.

Another central question is Long Covid, for which we got some actual data, so there’s a section analyzing that.

The big mystery remains why Delta suddenly peaked and turned around, first in India, and now in the UK and the Netherlands. These turnarounds are excellent news, and I presume we will see a similar turnaround at a similar point, but what’s causing them to happen so quickly? I don’t know.

Meanwhile, the numbers got worse slightly faster than I expected. Let’s run them..

The Numbers

Predictions

Prediction from last week: 360,000 cases (+50%) and 1845 deaths (+10%)

Result: 392,000 cases (+62%) and 2042 deaths (+21%).

Prediction for next week: 610,000 cases (+55%) and 2,450 deaths (+20%).

Things got worse slightly faster than expected. I doubt things are ready to turn around, but there are signs of the control system starting to act which should accelerate, and between vaccinations and infections (most of which likely do not result in positive tests and therefore known cases) immunity is building up, so I continue to expect the pace of growth to drop off a bit. I’d be unsurprised by numbers between about +40% and +70% for cases.

Deaths

...