trentmkelly/LessWrong-43k huggingface dataset:
Social Calibration
by SimulatedCrow — 2021-05-20T23:22:07.864Z

The concept of “illusory superiority” used to just be a punchline about human nature. Eighty percent of people believe they are above-average drivers, above-average lovers, and above-average vegetable choppers. Ah, humans: so charmingly irrational.

Then recently, independent sociology researcher Spencer Greenberg showed that illusory superiority is complemented by an equal but opposite effect: illusory inferiority. When he asked people to rate themselves on hundreds of competencies, he found that while most people believe they are above-average drivers, most people simultaneously believe they would be worse than average at driving a race car. When the bar is sufficiently raised, it seems that under-confidence becomes the norm.

Rob Reid discussed Spencer’s findings in a recent After On podcast about pandemic detection using search data. Rob and his guest seemed to agree that Spencer’s findings support the idea that maybe we should be more optimistic about human nature – perhaps we’re not just a bunch of overconfident apes!

But this optimism misses the far more interesting question: what should we actually do with this new information? Just like Taleb’s concept of antifragility expanded the definitional range of the spectrum of fragility beyond just “robust” to “delicate,” the empirical evidence for illusory inferiority helps us see more of the range of the calibration scale of self-assessment, from systematically biased overconfidence to systematically biased under-confidence. Understanding it better can help illuminate a path toward good calibration at the center. ( For background reading on calibration, see Probabilistic forecasts, calibration and sharpness by ‎Gneiting.)

Why should we want to be well-calibrated in our judgements about our own abilities?

Being well-calibrated about your own abilities, i.e. neither over- nor under-confident, is not just about increasing self-knowledge. It is also a prerequisite for learning.

If you’re overconfident about your own abilities in a particular area, then there is no pressing need to spend time learning about it. If I believe I’m an amazing driver, why heed the annoyed honks directed at me? It’s clearly their problem. While feedback blindness is harder to pull off in strictly quantitative endeavors, like math and video games, it’s pretty easy to fall into it in subjective fields with slow or ambiguous feedback cycles. (...
Did the Industrial Revolution decrease costs or increase quality?
by jasoncrawford — 2021-05-20T21:10:55.804Z
Link: https://rootsofprogress.org/cost-quality-and-the-efficient-frontier

An oversimplified story of manufacturing progress during the Industrial Revolution is: “we automated manufacturing processes, and that decreased the cost of goods.” This is not wrong, but is not the full picture.

Mechanization—and other progress in manufacturing, such as improved tools, materials, and chemical processes—not only decreases costs, but also improves quality. Making things by hand requires skill and attention: just try making your own clothing or furniture at home; on your first attempt you won’t be able to achieve nearly the quality you can purchase for a modest price. Automation not only improves average quality, but also consistency, by reducing variance.

If we want a fuller picture of how goods were improved through the Industrial Revolution, we should think of cost and quality together. We can visualize this conceptually on a two-dimensional chart. Here, each dot represents one possible manufacturing process for a particular good:

In this chart, you want to be in the upper left: high quality at low cost. So there’s no point in using any given process if there is another process that achieves higher quality and lower cost at the same time. This means the industry tends to move towards the set of processes along the upper-left edge. This set is known as the efficient frontier:

If you’re in the middle of a diagram like this, you can improve both cost and quality together by moving towards the frontier. If you’re on the frontier, you face a cost-quality tradeoff.

The nuanced way to think about new technology is not that it simply decreases cost or increases quality, but that it moves the frontier.

Consider cotton thread, one of the first products whose manufacturing was automated. In the late 1700s, spinning machines were invented that could do the work of dozens of hand spinners. Large factories were set up, driven by water mills (and later, by steam engines). Here’s what happened to the cost of thread as automation took over the industry:

Harley, “Cotton Textile Prices and the Industrial Revolution” (1998)

The chart above shows prices for three different grades of thread. Higher numbers indicate finer thread, which makes for softer fabrics.

Conceptually (not to scale), we can visualize what happened like this:

Note that the price for the highest-quality thread, 100 twist, came down most dramatically. It’s possible for humans to spin thread this fine, b...
It's like poetry, they rhyme: Lanier and Yudkowsky, Glen and Scott
by CraigMichael — 2021-05-20T18:48:53.537Z

[EDIT: I've edited this post based on feedback. The original is on archive.org.]

I've gazed long into the exchanges between Jaron Lanier and Eliezer Yudkowsky, then longer in to those between Glen Weyl and Scott Alexander. The two exchanges rhyme in a real but catawampus kind of way. It's reminiscent of an E.E. Cummings poem. If you puzzle over it long enough, you'll see it. Or, your English teacher explains it to you.

Rpophessagr?

(Understanding "r-p-o-p-h-e-s-s-a-g-r" is not required for the rest of this post, or even really worth your time. But a hint if you want to try: the line preceding the date is something like a pre-ASCII attempt at ASCII art.)

Anyway, for the duration of this post, I will be your hip substitute English teacher. Like hip substitutes, instead of following the boring lesson plan your real teacher left, we're going to talk about Freud, bicycles and a computer demo from 1968.

Who are Jaron Lanier and Glen Weyl?

Jaron Lanier has written several books, most recently Ten Arguments for Deleting Your Social Media Accounts Right Now, which distills many of his more salient points from his previous books You Are Not a Gadget, Who Owns the Future and Dawn of the New Everything. Eric Weinstein, says there's "four stages of Jaron Lanier."

The first one, you think he's a crazy person. Then you're told that he's a visionary. Then you realize no no no, he's probably just faking it. And then, when you get to the final stage you realize he's pretty much usually always spot on and never cowed by his employer or the group think of Silicon Valley.

If this is your first encounter with Jaron and he sounds crazy, you're not alone, but maybe give him and second, third and fourth look. Glen works with Jaron closely, and I'm familiar with Glen's work where it overlaps with Jaron's.

(Unrelated but perhaps interesting to other neurodivergents who share personality traits with me—I strongly suspect that if I saw Jaron's Big Five test results, they would look a lot like mine. Part of my interest in him is to see how someone high in openness and low in conscientiousness—he may of had the most overdue book contract ever in American publishing, for example—succeeds in doing things. FWIW two strategies he uses are cross-procrastination and the hamster wheel of pain. I wish he would share more of these.)

The easy rhyme: mentor-protégé lineage

In 2008 Eliezer Yudkowsky and J...
Sabien on "work-life" balance
by Rob Bensinger — 2021-05-20T18:33:37.981Z

From Duncan Sabien:

This morning, a friend of mine referred to his "work-life balance" and then grimaced at himself, and noted that he doesn't really like that term.

(As far as I can tell, he doesn't like it because his work is important to him, and is part of being alive, and his non-work life isn't some fundamentally different kind of thing.)

This led me to the mental distinction between one's directly valuable life experiences, and one's indirectly/instrumentally valuable ones.

Like, there are many fewer layers involved when you, say, snuggle up to someone you love, or pop a delicious food in your mouth, or bounce on a trampoline. There's a very short, direct path between the action and the reward; it's just straightforwardly close to your values and the things which bring you joy and fulfillment.

Whereas if you're good at your work and you think that your job is important, there's an intervening layer or three—I'm doing X because it unblocks Y, and that will lead to Z, and Z is good for the world in ways I care about, and also it earns me $ and I can spend $ on stuff...

I think it's less about "work-life balance" and more about the ratio of direct vs. indirect value. "How many of the things that I'm doing pay off directly, versus how many of them are knocking over dominos that eventually lead to the payoffs I'm seeking?"

(And how close is that ratio to one which I will actually find sustainable and enjoyable and healthy.)

This, to me, is a distinction that cuts closer to the true joints of reality than the arbitrary categories of "work" and "everything else." As a single easy example, this also applies to the balance, in one's relationships, between straightforwardly valuable interactions with people you enjoy, and meta interactions/maintenance/laying the groundwork for the future.
Response to "What does the universal prior actually look like?"
by michaelcohen — 2021-05-20T16:12:30.354Z

These are my thoughts on this post of Paul Christiano. I claim "malign" models do not form the bulk of the Solomonoff prior.

Suppose that we use the universal prior for sequence prediction, without regard for computational complexity. I think that the result is going to be really weird, and that most people don’t appreciate quite how weird it will be.

...

The setup

What are we predicting and how natural is it?

Suppose that it’s the year 2020 and that we build a camera for our AI to use, collect a sequence of bits from the camera, and then condition the universal prior on that sequence. Moreover, suppose that we are going to use those predictions to make economically significant decisions.

We aren’t predicting an especially natural sequence from the perspective of fundamental physics: to generate the sequence you really have to understand about how the camera works, about how it is embedded in the physical universe, about how it is moving through space, etc.

On top of that, there are lots of “spots” in the universe, and we are picking out a very precise spot. Even if the sensor was perfectly physically natural, then it would still be quite complicated to pick out which physically natural thing it was. Even picking out Earth from amongst planets is kind of complicated, picking out this particular sensor is way more complicated.

So the complexity of a “natural” description of our sequence is actually reasonably high. Much smaller than the complexity of existing compression algorithms, but high enough that there is room for improvement.

I agree that the natural description of the sequence is reasonably high, so we can't rule out room for improvement immediately.

Consequentialism

Specifying a consequentialist probably requires very very few bits. (Here I mean “consequentialist” in the sense of “agent with preferences,” not in the sense that a philosopher might be a consequentialist.)

Suppose I specify a huge simple lawful universe (like our own), and run it for a very long time. It seems quite likely that consequentialist life will appear somewhere in it, and (if the universe is hospitable) that it will gradually expand its influence. So at late enough times, most of the universe will be controlled by consequentialists.

We can concisely specify a procedure for reading a string out of this universe, e.g. somehow we p

...
Time & Memory
by abramdemski — 2021-05-20T15:16:49.042Z

Epistemic status: fake psychology, no attempt to be correct in details, but maybe something broadly like this happens

Ever had the experience of laughing at something, and then a few minutes later it sort of hits you again and you start laughing again?

Or forget someone's name you just used, and say "it'll come to me in a minute" (and it does)?

Humans have very limited working memory. But you can sort of simulate a larger working memory by looping over things, "rotating" them in and out.

Computer systems tend to have multiple layers of caches -- SRAM caches DRAM, DRAM caches stuff on the disk. It creates this pyramid of memories, on a spectrum from fast&small to slow&large.

Computers have their own sophisticated ways of managing these memories, but for humans it might make sense to randomly/cyclically rotate stuff in and out (weighted by some measure of importance and relevance) in order to give you a good chance of spotting any important connections between, say, 20 things even though you can only keep 7 things in working memory at once.

I mean, it's very sophisticated. The next time you are making a complicated argument, if you can, try and watch yourself recalling bits and pieces at a time. To me, it feels viscerally like I have the whole argument in mind, but when I look closely, it's obviously not the case. I'm just boldly going on and putting faith in my memory system to provide the next pieces when I need them. And usually it works out.

(When it doesn't, there's this whole failure mode where people continue viscerally feeling like they can make the argument, even though they don't have the pieces; and I think this is where a lot of bad reasoning comes from.)

But there's also this random/cyclical element to it, with things popping in and out for no good reason other than that I haven't thought about it in a bit.

There's also a moderately similar-feeling thing that happens on a longer time scale, which might be a different mechanism. It feels like I have pieces of me which "wake up" periodically and look around. There's the piece that wakes up every few minutes to check whether I'm doing roughly what I want to be doing. There's a piece that wakes up every few hours to really check. There's a piece that wakes up every few days to see whether things are looking ok on that scale. And so on, for a lot of time scales. I've noticed that I have a "mini mid-life...
How I Learned to Stop Worrying and Love MUM
by Waddington — 2021-05-20T07:57:25.645Z

Epistemic status: Speculative

The title is a tongue-in-cheek reference to Google AI's latest showcase: Multitask Unified Model, or MUM for short. Further details can be found in their arXiv paper; Rethinking Search: Making Experts out of Dilettantes.

Let's say hi to MUM.

Multitask Unified Model (MUM)

Google presents its new piece of AI technology as a search tool. That is, indeed, one potential application of MUM. But that's just scratching the surface.

The transformer architecture was introduced in 2017 with the paper Attention is All You Need. Language models, it turns out, need only simple algorithms and a ton of data. The bigger they get, the better they perform. Nobody expected the latter part. Generative Pre-trained transformers, GPTs, were introduced by OpenAI. GPTs generate text based on their transformer models. GPT-2 raised quite a few eyebrows. GPT-3 performed so well that it has alarmed the general public. GPT-4 isn't out yet, but we all know it's coming.

A simple idea transformed the transformer: multimodality. Let the model find connections between the same patterns via different modalities. 'Book', for instance, is a word. It refers to a cluster of patterns that can be made sense of via vision, audition, or even olfaction (for all you weirdos sniffing old books at the library). When you can compare and contrast patterns across different modalities, you understand them better. Helen Keller's tutor, Anne, wrote W-A-T-E-R in Keller's palm as she let water run across her hand. Keller later described this moment in these terms:

“As the cool stream gushed over one hand she spelled into the other the word water, first slowly, then rapidly. I stood still, my whole attention fixed upon the motions of her fingers. Suddenly I felt a misty consciousness as of something forgotten–-a thrill of returning thought; and somehow the mystery of language was revealed to me. I knew then that ‘w-a-t-e-r’ meant the wonderful cool something that was flowing over my hand. That living word awakened my soul, gave it light, hope, joy, set it free! There were barriers still, it is true, but barriers that could in time be swept away.”

Are you scared of MUM yet? Don't worry!

While they haven't released details, Google describes MUM as a multimodal transformer. It can understand your search queries. Even complicated ones. It can then provide you with answers. You are at point A....
Abstractions and translation
by Amir Bolous — 2021-05-20T02:45:10.485Z

This is a linkpost for https://amirbolous.com/posts/abstraction

Introduction
Languages and Meaning
Communication and Translation
Closing Thoughts

Introduction

For the past three weeks, I've been building an interpreter for Lisp (more specifically, for a Lisp-dialect I created inspired by Clojure and Scheme). Building a language from the ground up is incredibly fascinating because you get to see in real-time how different abstractions come together.

In the beginning my language was just a simple calculator that handled single expressions.

Pretty straightforward, nothing fancy. Then it was a calculator that could handle multiple, nested expressions

Then it could take on relational and logical operators.

Ok becoming cooler but still more reminiscent of a calculator than a programming language, what about state? That is, saving, calling, and using previous values. Enter variables and functions.

Level by level, I climbed up the ladder of abstraction and watched how the language unfolded. First I was writing functions in Lispy (the name I gave the language inspired by this) then I was writing functions which used other functions then I was writing functions which used other functions which used other functions, and it was off to the races.

Languages and Meaning

3 weeks ago before the interpreter, the rules and syntax governing my language had no meaning (in my little domain). In fact, the rules and grammars on their own were just gibberish, worthless to an outsider who did not know what they represented. They were just letters guided by certain design choices. But the interpreter gave them meaning.

This is pretty profound because we use language so often that I sometimes forget how high up the abstraction ladder we are. Language is powerful because it carries meaning but the syntax of a language itself does not. The syntax becomes a language when it encodes the underlying abstractions it intended to. For example, my interpreter at the start could not recognize that (+ 2 2) was a binary operation that produced some result. Until I gave it the tools to encode this abstraction.

These abstractions are powerful but they're also just small building blocks. How you construct them and what you do with them is only limited by the imagination of the user who understands these abstractions. This is why we can code, why we write, why we create music, why we talk, why we do any form of...
Solar Production Data
by jefftk — 2021-05-20T01:30:02.098Z

We have solar panels on our house, configured so that we can have

backup power in a blackout. In
talking about

generators
and

power outages I wanted to
figure out how much power we might expect to be able to draw.

Our system is a bit weird. We have 14 panels, rated for 360W
each. Most of them (11 of 14) are mounted on our 11:12 slope roof,
facing WNW (~293°). Since we're in the Northern hemisphere this
is a pretty silly direction to point panels, but our neighbors to the
ESE have an enormous tree so this is the only direction sun hits our
roof. They are considering cutting their tree down, since it's is
interfering with their foundation, at which point I might want to put
panels on the opposite side of our roof. This means this post likely
generalizes even less than my usual ones, but it is at least likely a
worst case.

Our system is connected to a monitoring service. While their UI is
good for getting rough understanding of performance, it isn't set up
to make the charts I'm interested in. It can export data, but at the
highest resolution you can only do one day at a time. I automated
this, and now I have data
for 2018-11-13 to 2021-05-15 at one minute resolution.

Looking it over, there were twelve periods when our system was shut
down due to arc faults. Since these
have now been fixed by a firmware
update I've excluded those periods.

The very highest value in the data is 5273W, 113% of nominal, at 16:04
on 2020-05-09:

I think this is probably not an error; this was a historically
cold day (ideal for production). My guess is we were seeing an edge-of-cloud
effect, where the "silver lining" of a cloud can magnify sunlight
above what you'd get in direct sun.

Let's take all the minutes in the data, and sort them by production,
lowest to highest:

This is a mildly tricky chart, because the raw data does not include
times when the system is producing no power at all, such as the night.
So it is saying, "if the system is producing power, how much power is
it producing."

Perhaps a more useful way to look at this, is by day:

This is the same chart, but instead of aggregating at the "minute" level
we are aggregating at the "day" level. You could read this and
conclude that a median amount of power is 7.8kW/day, but sunlight
varies a lot over the year, and so does production:

The sunniest days of the sum...
Is Ray Kurzweil's prediction accuracy still being tracked?
by CraigMichael — 2021-05-20T00:06:45.690Z

This is from Ray's 2001 response to an essay by Jaron Lanier. I came across it while researching another post.

To correct a few of Jaron’s statements regarding (my) time frames, it’s not my position that the “singularity” will “arrive a quarter of the way into the new century” or that a “new criticality” will be “achieved in the about the year 2020.” Just so that the record is straight, my view is that we will have the requisite hardware capability to emulate the human brain in a $1,000 of a computation (which won’t be organized in the rectangular forms we see today such as notebooks and palmtops, but rather embedded in our environment) by 2020. The software will take longer, to around 2030. The “singularity” has divergent definitions, but for our purposes here we can consider this to be a time when nonbiological forms of intelligence dominate purely biological forms, albeit being derivative of them. This takes us beyond 2030, to perhaps 2040 or 2050.

So it's 2021. Do we think for ~$1000 dollars we could buy the hardware to emulate the human brain? Will we not know until we have the software?

There's a list on Wikipedia of his predictions and there's a link from that article to Long Bets, but it looks like Ray only has one bet there. Would be interesting to see an up-to-date, batting average.
How refined is your art of note-taking?
by DirectedEvolution — 2021-05-19T22:12:06.424Z

Over the last few months of studying "effective scholarship," I've concluded that a key skill is note-taking. In the near future, I plan to write fairly extensively on the subject. For now, I'm curious to hear how the LessWrong community takes notes. Some particular questions:
1) How consistently do you take notes when you're reading up on a new skill or subject?

2) Do you regularly refer back to old notes?

3) Do you approach note-taking differently for different subjects or purposes?

4) Have you adopted a specific note-taking method and used it consistently for more than a few months?

5) What role does note-taking play for you? Is it a way to focus your attention? To make extracts from the text for easier reference later? To comprehend the material better through the act of making notes?