trentmkelly/LessWrong-43k huggingface dataset:
Up close and personal with the world
by dominicq — 2020-09-25T06:52:28.302Z

Explicitly building models of the world is often done through understanding the mechanics of how things work, the relations between them, causal effects and more. But I don't put enough emphasis on quantifying. I'm not talking about doing Fermi estimates because I do them and my rationalist(-adjacent) friends do them. There is a difference between Fermi-estimating quantities and knowing them from heart. I think there is value in the latter.

It is good to know, on an intuitive level, how a force of 100 newtons feels like. To have an intuitive grasp of how much a volt is, and how some number of amperes feels like. Take acceleration and velocity, for example. You have a very intuitive grasp of them because of the experience of driving and looking at the speedometer. The idea is to expand the same intuition to other units of measurement. You want to feel what the numbers mean. When you do this, you get very up close and personal to your model of the world. Things are not abstract, black boxes anymore.

Tech-savvy dads were right all along when they were impressed by some specific number related to fuel consumption, or torque, or clamping pressure, or whatever, and I failed for not being interested in what seemed like random numbers and units. I understood the mechanism and the underlying idea, wasn't that enough? But it wasn't, because I was not fluent, I did not own the model in my head. It didn't yet get that quality of ordinariness that I now seek.
Human Biases that Obscure AI Progress
by Danielle Ensign — 2020-09-25T00:24:45.451Z

There are some common biases that are often used to discount AI progress. We should keep these in mind, as they can prevent us from having an objective understanding of progress in the field.

I'm going to use AI here instead of ML because usually these biases are relevant to any AI technique, not just ML. But in practice, ML is usually what I'm referring to.

1. AI uses the easiest solution it can find. Often people argue that a system isn't intelligent because it did some task in a simpler way than humans would have done it. This is especially applicable if there is some simple heuristic the AI found that did well enough. If finding the word "death" in a sentence is sufficient to do perfect classification, the AI will probably only learn to do that, no matter how intelligent it is. If your test set includes a case where that rule isn't sufficient, but the training set does not, the AI will probably fail on the test set case, because it has no way of knowing that "looking for the word death" wasn't the rule you wanted. AI will only do more complicated things once that simple thing is no longer sufficient, and it'll keep around the dumb heuristics whenever they work.

2. AI is different than us. Sometimes people argue that a system isn't intelligent because it does something in a more complicated way than is necessary (or just in a different way than what humans would have done, but not necessarily more or less complicated). Just because a specific rule is intuitive to us humans doesn't mean it's the easiest for an AI to find. A more complicated rule that also works and is easier for the AI to find will be found and used, no matter how intelligent an AI is. Regularization could in theory help with this, but only if regularization pushes towards the natural "human" approach, and only if the researchers continue training it after it's performance is very good. It's also not guaranteed that the rules that seem sensible to us are as good as what the AI does, our approach could be worse.
3. Many models will be better than humans in some ways and worse in others, and which aspects those are often won't be correlated with what we view as "difficult".

There are certain things that are easy for us to do (continuing to use the same name to refer to someone over a long story, preserving world details like the size of a horse) and things that are difficult for u...
Losing the forest for the trees with grid drawings
by Adam Zerner — 2020-09-24T21:13:35.180Z

Last night I learned about grid drawings and spent some time trying to work on them. In doing so, I ended up having a lightbulb moment where I really internalized what it means to lose the forest for the trees.

I'll explain what I mean. To start, let's back up and talk about what grid drawings are.

https://www.free-for-kids.com/drawing-grid-with-picture.shtml

The top image is the reference image, the bottom is your version. You draw grid lines in the reference image. Then you draw grid lines on your version. Then you go square-by-square and try to copy what you see.

For example, let's say that rows are labeled A through H and columns are labeled 1 through 11. For the flower, maybe we start at square A6. We look at what A6 looks like in the reference image and try to copy it into our version of A6. Then we move on to A5 and do the same thing. Then maybe B5. So on and so forth until we have gone through every square.

I think the idea is that there's less going on inside an individual square than there is when you zoom out and look at the image as a whole, so by approaching it this way it ends up being easier for the artist.

At first this felt like it made a ton of sense. I was successfully using the technique to draw easy things. But then once I moved on to a harder image, I ran into a weird problem. I was going square-by-square, and I felt like each individual square I drew was pretty close to the one in the reference image. But then I zoomed out and looked at what I had... and it looked terrible!

Huh? How could that be? So I went through each of the squares I drew and compared them to the ones in the reference image. Maybe I screwed one of them up or something.

Nope. Each one seemed pretty good. And yet... the zoomed-out perspective still looked like garbage!

I was confused. Reductionism is a thing, right?

And then I realized: small errors in each individual square can compound and make the zoomed-out version look terrible.

And furthermore, I was paying too much attention to the individual squares and not enough attention to the zoomed-out version. To the trees instead of the forest. Let me explain what I mean by that.

Say that in cell E21 the line in the reference image starts about 25% down from the top and goes at an angle of about 45°. I was just trying to imitate that. But if my version of the adjacent square, E20, is screwed up, I should factor that in to my E21 s...
Petrov Event Roundup 2020
by Raemon — 2020-09-24T21:07:38.757Z

Petrov Day is this weekend. Various LessWrong folk have taken the opportunity to reflect on the Day that Stanislav Petrov Didn't Destroy the World. Some people have employed ritual, and others parties. Some employ Big Red Buttons of some sort.

This year, many of the traditional manners people have commemorated the event are a bit different.

East Coast Petrov Megameetup

The New York Rationality community will be holding an Online Petrov Megameetup, which includes:

3:00pm - 3:15am Welcome
3:15pm - 4:15pm Ice Breakers
4:15pm - 5:30pm Lightning Talks & Activities
5:30pm - 6:00pm Snack/Water Break
6:00pm - 7:00pm Ritual

It's recommended that you get 8 candles/candleholders for the ritual if you can, but if not you can listen along, and maybe download a Candle App for your phone that you can use for a few key moments.

Austin Petrov Day

The folks in Austin are holding an in person, masked, outdoor Petrov Day.

We have modified the ceremonial manual to accommodate the outdoor setting. If possible, please bring a printed copy of the double-sided booklet version (or a mobile device on which you can read the mobile-friendly version), and a pen/pencil.

Littleton, CO Petrov Day

Littleton is having an outdoor potluck:

Come join us as we celebrate the world not having been destroyed, and raise a glass of Vodka to Stanislav Petrov.

We have chosen an outdoor venue for pandemic concerns, and will have a potluck for anyone willing to break bread during these troubled times. Masks are encouraged if you are not eating or drinking, and social distancing is recommended for anyone not already spending time together (some attendees have created quarantine circles, so don't be surprised if you see people ignoring typical precautions).

If you're hosting a Petrov Day celebration of some kind that people are welcome to join, please list them in the comments below!
Visualizing the textbook for fun and profit
by DirectedEvolution — 2020-09-24T19:37:47.932Z

Here we go again. It's the beginning of my undergraduate o-chem series. It's going to be a year of prose like this:

Recall the electron configuration of carbon (Figure

1.18

). This electron configuration cannot satisfactorily describe the bonding structure of methane (CH4), in which the carbon atom has four separate C─H bonds, because the electron configuration shows only two atomic orbitals capable of forming bonds (each of these orbitals has one unpaired electron). This would imply that the carbon atom will form only two bonds, but we know that it forms four bonds. We can solve this problem by imagining an excited state of carbon (Figure

1.19

): a state in which a 2s electron has been promoted to a higher energy 2p orbital.

Scintillating. I genuinely enjoy my subjects, but let's be honest. Right in the moment, there are many other things I would rather be doing than reading my chemistry textbook.

Recently, I explored whether it's possible to feel a sense of playfulness while you're alone. I found that it was. It it possible to feel playfulness while reading a chemistry textbook? Or is it better to optimize for an efficient, if dry, learning experience, and find ways to reward yourself afterward? If it is possible to feel playfulness while reading an o-chem textbook, it would be worth trading off at least some efficiency in favor of positive feeling. The question is how much. Of course, the best case scenario is where it's both possible to feel playfulness while reading, and it makes you learn more efficiently.

In my experiments of feeling playful while alone, I took away a few key insights.

Starting with an open, unfocused, non goal-oriented mindset was crucial.
The feeling of playfulness was based on connecting physical objects with memories, a sense of profound personal meaning, and possibility. There was a poetic, metaphorical, associative quality to the experience. These associations were neither purely spontaneous and involuntary, nor deliberately constructed. It was more a sense of resting my attention and gaze on an object, and then gently asking my mind to remember or imagine a psychological association.
I remembered the act of remembering afterward, and it made those memories more available to me in the future.

Playfulness while reading a chemistry textbook might take a similar form, something quite different, or simply be unavailable.

A particular differenc...
Scheduling Algorithm for a PhD Student
by Tim Liptrot — 2020-09-24T16:10:12.177Z

This post was inspired by Christian and Griffiths Algorithms to live by. Unlike other self-help books, it argues that the optimal scheduling strategy depends on your goals. If you want to minimize lateness of your single latest output, you should use Earliest Due Date. If you want to minimize the number of late items, use Moore's Algorithm. If you want to maximize value for time, select by value-weighted processing time (value/processing time). And so on and so forth. Based on their work, here is my scheduling plan for different items.

Coursework

As a phd student, I cannot afford to submit any coursework late. A high maximum lateness is bad for my reputation. Therefore, for coursework I want to use Earliest Due Date. However, I only want to devote a portion of my time to coursework: my primary goal is to become a producer of research, not a consumer. Therefore I should limit the amount of time I spend on each assignment (this also helps w/ focus). I have devised the following heuristics (and usually set timers for each activity)

Reading an article: 20 min

Reading an article relevant to my interests: 40 min

Reading a book: 2 hrs [1]

Reading a book relevant to my interests: 4 hrs (in two sessions)

A problem set: until completion

A coding problem: until completion

Writing a reading response: 20 min

After coursework is done, I switch immediately to career development.

Career development

These tasks include every behavior that increase my chance of becoming a professor, producing good research, and producing juicey QALY's. The most common tasks are pitching articles, analysing data, writing, editing, writing, communicating w/ coauthors, and writing more.

These tasks should be ordered by value per weight. I have more ideas than I have instrumentalized time to explore them. Any tasks which will take a long time to complete but give only marginal academic value can be thrown out.

The real problem is assigning values to the different tasks. How valuable is planning out my course schedule for the next year? Evaluating a specific research agenda? Exploring the papers in a new field? Unfortunately, I lack good answers for these questions. I'm vaguely aware that I should have >5 articles published when I enter the job market. But the weightings for quality, quantity, prestige and coherence of these publications are unclear. This is a vital area for further research.

T

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Covid 9/24: Until Morale Improves
by Zvi — 2020-09-24T15:40:02.594Z

Last Two Weeks: Covid 9/17: It’s Worse, Covid 9/10: Vitamin D

There is a pandemic. It is not going anywhere.

The news, on the other hand, is ready to move on. There is a supreme court seat open. There is an election coming soon, complete with a first debate. There are protests and presumably riots flaring up once again, this time regarding there no direct charges to any police officers in the death of Breonna Taylor.

Oh, and the President of the United States said yesterday, and I quote:

“Well, we’re going to have to see what happens. You know that I’ve been complaining very strongly about the ballots and the ballots are a disaster,” Trump said, when asked if he could commit to the peaceful transition.

“(G)et rid of the ballots and you’ll have a very … there won’t be a transfer, frankly. There’ll be a continuation.”

If you are going to vote for Biden, vote in person. Make sure your vote counts and reduce the chances of chaos breaking loose before that happens.

If you’re going to vote for Trump despite everything, at least have the decency to vote by mail. That way, to count your vote, we’ll have to… count the votes.

Who cares about the pandemic? No one, it seems. That’s old news. The pandemic is now merely one more prop, albeit a big one, in the political game.

I can’t even argue. I too am more worried about the election than the pandemic, at this point.

We all grow tired of this pandemic. The damage accumulates. Economies and lives and reputations and credibility get destroyed week in and week out, and nothing good seems to come of any of it. Solutions get ignored, and the situation fails to improve. The same scolds keep saying we need to keep holding out, while scolding anyone attempting to solve the problem. Same covid time, same covid channel. If I didn’t have people thanking me every week for doing these, it would be difficult to keep going.

As usual, most of the Covid news falls into our existing patterns. When no one cares enough to do anything, but they do care enough to make sure no one else does anything, each week’s news is going to look like the previous week’s news.

And yet, if we look, there is always something worth talking about. Including one piece of genuinely great news.

Let’s run the numbers.

Positive Test Counts

Date	WEST	MIDWEST	SOUTH	NORTHEAST
July 30-Aug 5	91002	64462	212945	23784
Aug 6-Aug 12	93042	61931	188486	21569
Aug 13-Aug 19	80887	63384	15

...
Spoiler-Free Review: The Stanley Parable
by Zvi — 2020-09-24T14:40:01.655Z

At Ben Pace’s recommendation, I recently played The Stanley Parable. Ben considers the game Tier 1.5. On reflection I consider this Tier 2. If you can spare the $15 for a short game, it’s Worth It to play.

If there’s one game I’ve played that needs to be played blind, this is it, so that’s all I’ll say.
Should it be a research paper or a blog post?
by Sherrinford — 2020-09-24T08:09:08.179Z

The answer to this question may seem obvious to some, but let's see.

My impression is that some people write long blog posts about things that they perceive as innovative new ideas in philosophy, AI research, or whatever. So how do you decide that your idea should be a blogpost (here or on some other site) instead of submitting it to a journal?

Is the university research system just too closed? Or do you think it is superfluous?

Or do you think that you can do it faster for a blog? (This may not be unambiguously good. My impression is that there is less literature research for some blog articles than I would like to see, which often creates noise.)

Or do you want to have feedback for a blogpost first and submit later?

(Note that the question can be generalized to other media and forms, e.g. magazine essay vs. journal article.)
David Deutsch on Universal Explainers and AI
by alanf — 2020-09-24T07:50:23.615Z

In The Beginning of Infinity David Deutch claims that the world is explicable and that human beings can explain anything that can be explained (Chapter 3):

The astrophysicist Martin Rees has speculated that somewhere in the universe ‘there could be life and intelligence out there in forms we can’t conceive. Just as a chimpanzee can’t understand quantum theory, it could be there are aspects of reality that are beyond the capacity of our brains.’ But that cannot be so. For if the ‘capacity’ in question is mere computational speed and amount of memory, then we can understand the aspects in question with the help of computers – just as we have understood the world for centuries with the help of pencil and paper. As Einstein remarked, ‘My pencil and I are more clever than I.’ In terms of computational repertoire, our computers – and brains – are already universal (see Chapter 6). But if the claim is that we may be qualitatively unable to understand what some other forms of intelligence can – if our disability cannot be remedied by mere automation – then this is just another claim that the world is not explicable. Indeed, it is tantamount to an appeal to the supernatural, with all the arbitrariness that is inherent in such appeals, for if we wanted to incorporate into our world view an imaginary realm explicable only to superhumans, we need never have bothered to abandon the myths of Persephone and her fellow deities.

So human reach is essentially the same as the reach of explanatory knowledge itself. An environment is within human reach if it is possible to create an open-ended stream of explanatory knowledge there. That means that if knowledge of a suitable kind were instantiated in such an environment in suitable physical objects, it would cause itself to survive and would then continue to increase indefinitely. Can there really be such an environment? This is essentially the question that I asked at the end of the last chapter – can this creativity continue indefinitely? – and it is the question to which the Spaceship Earth metaphor assumes a negative answer.

Deutsch claims that an AI would be a universal explainer (Chapter 7):

There is a deeper issue too. AI abilities must have some sort of universality: special-purpose thinking would not count as thinking in the sense Turing intended. My guess is that every AI is a person: a general-purpose explainer. It is conc

...
I have discovered a new kind of unemployment.
by Nathan Brooks — 2020-09-24T01:04:18.266Z
Link: https://seekingalpha.com/article/4361570-skill-stalagmites-technology-stalactites

Hello everybody, I have an article that explains why since 2000: business investment has been weak; the fall in the U.S net labor share; the decline in the prime age U.S labor participation rate vs large gains elsewhere; the rise in deaths of despair. The article is called Skill Stalagmites, Technology Stalactites and can be found here. I have split the piece into two parts: a 1500 word article for the general reader and a longer piece for the more sophisticated reader. There is a link to the latter at the end of the first piece.

The punchline to the article is that the 4-5% gap in the lfpr between the U.S and peer economies is a form of disguised unemployment. And this is a novel kind of unemployment, which is not caused by a fall in aggregate demand.

The actual cause is that firms are imposing higher effort levels on workers. I can summarize the argument you will find in the main article; it goes like this:

Firms impose higher effort demands on workers; workers have to complete more tasks (for a higher wage) or be fired.
The higher wage does not compensate workers for their lost work leisure; thus workers look for less demanding job positions (or refuse to move up to more senior roles).
If one imagines a skill ladder, then all workers attempt to drop down a rung. This is easy for higher skilled workers, but what happens to workers at the bottom?
The lowest skilled workers compete for job openings with somewhat more skilled workers. Firms prefer to hire the more skilled worker, resulting in the lowest skilled workers being pushed out of employment altogether.
This assumes that employers can always identify the highest skilled worker from their pool of applicants. This won't always be the case; if the higher skilled worker has a bad interview or the weaker candidate has positive chemistry with the interviewer, then the objectively weaker candidate can win a job offer.
Thus provided the lowest skill workers are willing to keep searching for jobs they will eventually obtain a job offer and regain employment.
This means though that workers on the second lowest skill rung will be unable to drop down to the lowest rung unless they also increase their job search activity. And in turn this forces the workers above them to increase their job search.
Any person wanting a job now has to apply to many more job positions before they can get their first job offer.

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