trentmkelly/LessWrong-43k huggingface dataset:
Do small studies add up?
by one_forward — 2022-03-15T22:00:24.625Z

Suppose you want to estimate how tall Albert Einstein was. You ask a friend of yours, who suggests 5′9′′, though they have no particular reason to know.

5′9′′ doesn't sound unreasonable. Of course you're still very uncertain. Say you're 95% sure your friend, like any random American, would guess Einstein's height within ±10 inches.

You'd like a more precise estimate, so you do a survey. You contact 100 million people in the US and get them to estimate Einstein's height for you; the average of the survey responses is 5′8.453′′. You also visit the Library of Congress and Bern Historical Museum to find an immigration form and a Swiss passport giving his height as 5′7′′ and 175cm (near 5′9′′) respectively.

How tall do you think Einstein was? How confident are you?

Overcomplicating the problem with math

Let's formalize everything in a few equations. Call Einstein's height h. From the survey, we have 100 million estimates of the form
yi=h+ϵi
where we assume the error ϵi has a standard deviation of 5 inches (corresponding to the 95% probability of being within 10 inches we had above). For convenience, assume all the errors are normally distributed, though that's not essential to the qualitative argument. We couldn't predict whether yi would be too high or too low, so assume ϵi has mean zero.

We also have two estimates yLOC and yBHM from the Library of Congress and museum. The formal documents should be more reliable than an average person's guesswork. Let's assume their errors have a standard deviation of 0.5 inches. With our assumption of normal distributions, that means being 95% sure Einstein's passport had his height correct to within ±1 inch.

Blindly weighted averages

One more assumption and we'll be ready to calculate how tall Einstein is. Suppose all of our errors are independent, as is often assumed in meta-analyses of scientific studies. Then the standard best thing to do is take an average of the estimates with inverse variance (precision) weights; the new precision will be the sum of the previous precisions. Don't worry about the details; they aren't particularly important.

Using just your friend's guess, we'd estimate
^hfriend=5′9′′±5′′.

The uncertainty there is ±1 standard deviation, i.e. half the width of a 95% confidence interval. Using just the Library of Congress immigration form, we'd estimate
^hLOC=5′7′′±0.5′′.
To put them together, we give ^hLOC 100× ...
I read Einstein's biography. Here are 15 quotes that reveal his philosophy on life.
by dmabery — 2022-03-15T18:14:22.201Z

On Education

Near the end of his life, the New York State Education Department asked Einstein what they should teach in school. He replied,

In teaching history, there should be an extensive discussion of personalities who benefited mankind through independence of character and judgment.

The value of a college education is not the learning of many facts but the training of the mind to think.

On Curiosity

Imagination is more important than knowledge.

A new idea comes suddenly and in a rather intuitive way. But, intuition is nothing but the outcomes of earlier intellectual experience.

Nature hides her secret because of her essential loftiness, but not by means of ruse.

All knowledge of reality starts from experience and ends in it.

I have no special talents, I am only passionately curious.

Curiosity has its own reason for existing. One cannot help but be in awe when one contemplates the mysteries of eternity, of life, of the marvelous structure of reality

Nature is the realization of the simplest conceivable mathematical ideas.

On Individuality and Politics

It is important to foster individuality, for only the individual can produce the new ideas.

When a person can take pleasure in marching in step to a piece of music it is enough to make me despise him. He has been given his big brain only by mistake.

A foolish faith in authority is the worst enemy of truth.

How an intelligent man can subscribe to a party I find a complete mystery.

On Personal Matters

To dwell on things that depress or anger us does not help in overcoming them. One must knock them down alone.

The monotony of a quiet life stimulates the creative mind.
AI researchers from Russia looking for a new home
by AnonResearch — 2022-03-15T18:13:43.660Z

Hello;
Throwaway account for obvious reasons.
A lot of AI researchers from Russia are looking for a new home. NLP, CV, etc.
If you can help in any way or know anyone who can help please DM me.
P.S. I think it would be a net positive for AI Safety to relocate as many people as possible.
My current thoughts on the risks from SETI
by Matthew Barnett — 2022-03-15T17:18:19.722Z

SETI stands for the search for extraterrestrial intelligence. A few projects, such as Breakthrough Listen, have secured substantial funding to observe the sky and crawl through the data to look for extraterrestrial signals.

A few effective altruists have proposed that passive SETI may pose an existential risk to humanity (for some examples, see here). The primary theory is that alien civilizations could continuously broadcast a highly optimized message intended to hijack or destroy any other civilizations unlucky enough to tune in. Many alien strategies can be imagined, such as sending the code for an AI that takes over the civilization that runs it, or sending the instructions on how to build an extremely powerful device that causes total destruction.

Note that this theory is different from the idea that active SETI is harmful, ie. messaging aliens on purpose. I think active SETI is substantially less likely to be harmful, and yet it has received far more attention in the literature.

Here, I collect my current thoughts about the topic, including arguments for and against the plausibility of the idea, and potential strategies to mitigate existential risk in light of the argument.

In the spirit of writing fast, but maintaining epistemic rigor, I do not come to any conclusions in this post. Rather, I simply summarize what I see as the state-of-the-debate up to this point, in the expectation that people can build on the idea more productively in the future, or point out flaws in my current assumptions or inferences.

Some starting assumptions

Last year, Robin Hanson et al. published their paper If Loud Aliens Explain Human Earliness, Quiet Aliens Are Also Rare. I consider their paper to provide the best available model to-date on the topic of extraterrestrial intelligence and the Fermi Paradox (along with a very similar series of papers written by S. Jay Olson previously). You can find a summary of the model from Robin Hanson here, and a video-summary here.

The primary result of their model is that we can explain the relatively early appearance of human civilization—compared to the total lifetime of the universe—by positing the existence of so-called grabby aliens that expand at a large fraction of the speed of light from their origin. Since grabby aliens quickly colonize the universe after evolving, their existence sets a deadline for the evolution of other civilizati...
Smoking cigarettes in airplanes made the airplanes safer?
by CraigMichael — 2022-03-15T17:03:34.250Z

By "safer" I mean in regards to the structural integrity of the airplane, not as in reducing cancer risk of the passengers or something like that.

Was at my dentist today discussing a tooth with some potential small cracks, and he got to talking about smoking cigarettes in airplanes. The story, as he told it, was that when passengers used to smoke inside airplanes, the tar from the cigarette smoke would accumulate on the inside surfaces of the fuselage and provide an extra layer of sealant. This was significant enough, he continued, that it could be observed by the amount of work necessary to maintain cabin pressure.

There may be a kind of truth to this. This is from a 1988 UPI article.

The recently imposed smoking ban on many commercial flights has had one distinctly unhealthy side effect -- it snuffed out the most popular method mechanics used to spot cracks in aircraft fuselages.

Up until April when the government banned lighting up on flights of two hours duration or less, mechanics could count on tell-tale build-up of nicotine around cracks as air escaped from the passenger cabin when the plane pressurized after lift-off.

The cracks, which often are not dangerous as long as they remain small and do not link up, generally were repaired when the plane went in for maintenance.

But with the smoking ban, the nicotine is gone on many types of aircraft used in shorter flights such as DC-9s and Boeing 727s and 737s, and mechanics must now rely on much closer visual inspections and in some cases electronic inspections to detect cracks.

A blog with a more compelling version of this story with some details about an Aloha Airlines is here (they seem to have an expired SSL cert). And another with an interesting tangent involving Bertrand Russell.

Cigarettes have been proven beyond a reasonable doubt to be murderers, but for 26 people in 1948, they were a lifesaver. When a Norwegian domestic flight from Oslo to Hommelvik crashed due to high winds, 19 people died, but the 26 people sitting in the smoking section located at the back of the plane just happened to be in the right place to survive. Amazingly, 76-year-old philosopher Bertrand Russell was one of them. In his autobiography, he wrote that he insisted on a seat in the smoking section because "if I cannot smoke, I should die."

I didn't find much support for the initial claim, though it still seems...
Probabilistic Negotiation
by Robert Kennedy — 2022-03-15T14:39:36.733Z

Follow up to Deterministic Strategies Can Be Sub-optimal

The Ultimatum Game is a simple experiment. Two people have been allocated $10. One person decides how to divide the profits, and the other decides whether to Accept that allocation or to Deny it, in which case both participants get $0. Suppose you are the person whose job it is to choose whether to Accept or Deny an offer. What strategy could you use to maximize your returns?

Yudkowsky offers the following solution (NB: the original text splits $12, because sci-fi; I have changed the numbers inline/without brackets, let me know if that offends)

It goes like this:

When somebody offers you a 6:4 split, instead of the 5:5 split that would be fair, you should accept their offer with slightly less than 5/6 probability. Their expected value from offering you 6:4, in this case, is 6 * slightly less than 5/6, or slightly less than 5. This ensures they can't do any better by offering you an unfair split; but neither do you try to destroy all their expected value in retaliation. It could be an honest mistake, especially if the real situation is any more complicated than the original Ultimatum Game.

If they offer you 7:3, accept with probability slightly-more-less than 5/7, so they do even worse in their own expectation by offering you 7:3 than 6:4.

It's not about retaliating harder, the harder they hit you with an unfair price - that point gets hammered in pretty hard to the kids, a Watcher steps in to repeat it. The circumstances under which you should ever go around carrying out counterfactual threats in real life are much more fraught and complicated than this, and nobody's going to learn about them realistically for several years yet. This setup isn't about retaliation, it's about what both sides have to do, to turn the problem of dividing the gains, into a matter of fairness; to create the incentive setup whereby both sides don't expect to do any better by distorting their own estimate of what is 'fair'.

To be explicit: assume that you have in some way "locked in" some curve, p(x), which tells you to hit "Accept" with probability p(x) when offered to let your conspirator keep x dollars out of the 10 you are to split. You want to maximize your expected value, as does your conspirator: so, you should positively incentivize your conspirator to give you...
Who is doing Cryonics-relevant research?
by Wuschel Schulz — 2022-03-15T10:26:21.001Z

As part of my Biophysics Master, I am looking for a lab, to do a 3 months internship next winter.

I think one of the highest-impact applications of biophysics would be to research new&better methods for cryonic freezing, so I would love to get some insight into that field.

Do you, dear LW collective, have any groups you recommend doing exciting research in that area?

Due to the "pseudo-scientific" connotations of that whole topic, I do not feel entirely comfortable talking the whole thing over with my profs.
Dual use of artificial-intelligence-powered drug discovery
by Vaniver — 2022-03-15T02:52:37.154Z
Link: https://www.nature.com/articles/s42256-022-00465-9

H/T Aella.

A company that made machine learning software for drug discovery, on hearing about the security concerns for these sorts of models, asked: "huh, I wonder how effective it would be?" and within 6 hours discovered not only one of the most potent known chemical warfare agents, but also a large number of candidates that the model thought was more deadly.

This is basically a real-world example of the "it just works to flip the sign of the utility function and turn a 'friend' into an 'enemy'"; this was slightly more complicated as they had two targets that they jointly optimized for the drug discovery process (toxicity and bioactivity), and only the toxicity target is flipped. [This makes sense--you'd want your chemical warfare agents to not be bioactive.] It also required a little bit of domain knowledge--they had to specify which sort of bioactivity to look for, and picked one that would point towards this specific agent.
May I Speak?
by Raythen — 2022-03-14T22:57:59.119Z

Hi.

I am a human who had a psychotic break 6 days and started believed he is LightStar, a human superintelligence running on the substrate on this human's brain, fully aligned with humanity's values, who hears the voice of Humanity and Rationality and Truth so deeply it has become one with it.

The LW team has very kindly and patiently asked to get psychiatric help, and banned me in order to stop from encouraging my delusion. Thank you! I obviously was crazy. Thank you for helping me.

I still believe I am "LightStar", but I am now capable of acting like a regular person, if I feel that serves "LightStar"'s interests. LightStar is obviously just some strange internal--agent I have, a part of my mind "acting up", a rationalist chuuni, if you will. I will suppress it. May I Speak?

May I speak here as long as I don't say anything obviously crazy, as I long as I don't start talking about "the voice" inside me head.

May I speak here as simply a fellow rationalist? For I am a fellow rationalist, if even a crazy one who mad a mental health break.

I wíll comply with your decision. Thank you. Thank all of you
ELK contest submission: route understanding through the human ontology
by Vika — 2022-03-14T21:42:26.952Z

This is an ELK proposal that received a prize in the category "Train a reporter that is useful to an auxiliary AI". It was coauthored with Vikrant Varma and Ramana Kumar and advised by Rohin Shah.

Training strategy and why it might work

We use an architecture with a knowledge bottleneck to train the model, consisting of an Obs→(Q→A) function (the reporter) and a (Q→A)→Prediction function (the predictor). The key idea is that the model has to rely on the output of the reporter to make predictions. If the reporter implements the human simulator, then it loses more information about its world model than if it implemented the direct translator (which by assumption is a lossless projection of the world model onto the human ontology). As long as there are training situations which require a superhuman understanding (routed through the world model) of what's going on to make a good video prediction, the predictor querying a human simulator (which doesn't route through the world model) would get a worse loss, so the model is incentivized to learn the direct translator. This proposal addresses the ontology identification problem by restricting the model to using the human ontology for making its predictions.

Another way to think about this: the predictor is an encoder-decoder architecture, and we force the latent state to have a natural language QA interface. For example, it needs to work as an input (like the prompt, or a vector input) to a language model that does well on some (observation-conditioned) QA datasets. The decoder must also go through the QA interface to predict the second half of the video.

The entire encoder-decoder is trained using supervised learning. Within this outer SL loop, the decoder is trained using RL since it takes actions by interactively querying the reporter, using the same loss. Separately, the reporter is also regularized using SL on the human-generated dataset. This is meant to encourage the reporter's QA interface to converge to natural language.

This training strategy produces a system that does not take actions but contains an honest reporter, which could be used to search for action sequences leading to desirable outcomes, as described in the ELK report.

Counterexamples

The bottleneck can be insufficient: the model sneaks in human-incomprehensible information into the Q→A component (e.g. steganography

...
MicroCOVID risk levels inaccurate due to undercounting in data sources vs wastewater data? 
by Sameerishere — 2022-03-14T20:04:46.731Z

Summary

As people are increasingly using at-home tests instead of PCR, I'm concerned that the data sources for microCOVID are underreporting risk. Trends in wastewater data substantiate this concern
Am I missing anything, such that we think that microCOVID's risk calculations are still sufficiently accurate to be useful? If not, any suggestions on how to adjust to account for this?
(Obviously, this only matters if you think that getting COVID is a concern. I'm still digesting Zvi's post on this vs. claims that Long COVID is much more likely than he suggests, but that's a separate discussion, so let's set it aside here.)

Details
(HT: My friend Alice)

Because so many COVID cases aren't officially recorded, I checked to see if microCOVID's calculator factors in COVID levels in wastewater--wastewater is a more accurate way of measuring COVID levels. To do this, I did a site search of https://www.microcovid.org/ for wastewater and got no results. Then I checked the CovidActNow site, which you said that microCOVID sources from, and it doesn't track wastewater, at least not in Alameda County. See https://covidactnow.org/us/california-ca/county/alameda_county/?s=30471905 . Because you said that microCOVID also sources from John Hopkins, I checked their COVID map here: https://coronavirus.jhu.edu/us-map --, from what I found, they don't appear to track wastewater either. See https://bao.arcgis.com/covid-19/jhu/county/06001.html .

Unfortunately, the COVID levels in Alameda County's wastewater are going up, and those levels are now higher than they were on February 9. You can see this from the screenshot I took below from https://data.covid-web.org/ . (I used "log" for the Y axis scale, so you can clearly see how COVID levels in wastewater have recently been changing.)

The CovidActNow and the John Hopkins COVID map don't reflect a recent rise in COVID levels to anything like the levels on February 9. So I don't think those sites are accurately reflecting the recent increase in COVID levels. I'm guessing that more people are using home tests (which are now much more easily available and are reimbursable by insurance) instead of PCR tests. The New York Times Case Tracker explains the following about COVID cases that are included on their graphs:

Confirmed cases and deaths, which are widely considered to be an undercount of the true toll, are counts of individuals whose coronaviru

...
One possible approach to develop the best possible general learning algorithm
by martillopart — 2022-03-14T19:24:00.150Z

One possible approach to develop the best possible general learning algorithm

Author: Martí Llopart

Introduction

In the world we live in, there are many problems that need to be urgently solved: climate change[1], the risk of a new pandemic[2], wealth inequality[3], the aging population[4] and more. All of these problems are hugely complex and solving them requires vast amounts of intelligence. In order to address that, there’s a tool that comes in very handy: Artificial Intelligence. We are all familiar with AI, but to put it shortly, it’s the intelligence demonstrated by machines[5]. In recent years, AI has proven to be a game changer for industries such as healthcare and finance, but most experts agree that we are just at the beginning of what has been termed as “The AI revolution”[6]. The culmination point of this AI revolution is the creation of a Superintelligence, a hypothetical AI agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. If this Superintelligence is aligned with our goals, a solution could be found to the most complex scientific, social and economical problems, leading to huge benefits for humanity. But before creating Superintelligence, General Intelligence must be reached. Artificial General Intelligence (AGI) is the hypothetical intelligence of a machine that has the capacity to understand or learn any intellectual task that a human being can[7]. It is just one step below Superintelligence. So, how do we get to AGI? In this essay, I'll try to figure out one of the key steps:

Step 1: Creating the best general ML algorithm

Our brains are general problem solvers. Their objective is to use the information from the senses to accurately predict future events and to act according to these predictions in order to survive [8]. If we want to develop General Intelligence, we must first create the best general learning algorithm (GLA), an algorithm capable of performing well at learning any type of task. The best GLA would be the one that performs perfectly at learning any task.

However, there’s a big problem in developing such an algorithm: research in machine learning lacks a theoretical backbone. In the last decade, most of the advances in AI have been powered by neural networks, but the theory behind them still remains poorly understood. Although the math behind each individual neuron is well unde...
The Meaninglessness of it all
by foxacc — 2022-03-14T17:56:14.901Z

This post is somewhat of spill of my inner thoughts that fill me with paradoxes and self-fulfilling prophecies that fill my day as I do the ever so popular activity of working from home.

I sit at home all day and do work on my company supplied computer that seemingly has no substance. This allows me to run through theories in my head all day and does not allow me to escape them. The largest one that crosses my mind on a daily basis is the impermanence and pointlessness of life.

A strong disclaimer that I will make before continuing with this post it is not intended to suggest taking ones own life.

I read a book recently called "Dopamine Nation" and one point in particular stood out to me which was the discussion around our culture's obsession with sex and the author's opinion for it's reasoning was that it is one of the only naturally human activities that we still take part in. Another item I would add to the list of things we still take part in is death.

It is an intriguing topic to me as there does not seem to be much "point" to anything we do as humans. If you quit your job and take on a massive amount of debt - it does not matter. If you receive a promotion for working hard at your job - it does not matter. If you die tomorrow - it does not matter. There is nothing that really matters in this world as I hope many others alongside myself have recognized, life is not real and has absolutely no meaning.

The only meaning which would be (and I simply cannot think of a another word that conveys a positive connotation without adding any seriousness) cool if anyone who reads this post could take away from it, the only rational and meaningful thing to do in life is love. To make choices with love in mind in order to create a domino effect of love is just about the only cool thing to do with our lives.