trentmkelly/LessWrong-43k huggingface dataset:
On Building Theories of History
by Samo Burja — 2018-02-23T23:40:55.722Z

This is an excerpt from the draft of

my upcoming book

on great founder theory. It was originally published on SamoBurja.com. You can

access the original here.

Why was Barack Obama elected president in 2008? Was it because he ran a smart and successful campaign? Was it because new social media sites allowed young people to get interested in politics? Or was it because American culture was generally shifting away from George W. Bush’s brand of conservatism?

If you read the news articles published on November 4th, 2008, you’ll notice something interesting: journalists explain this historic event in many different ways. Some journalists attribute the campaign’s success primarily to the individual leading the campaign. Others focus more on the influence of new technologies on campaigning. Still others explain it in terms of a general cultural or political shift.

These explanations are revealing—not necessarily of what actually landed President Obama in office, but rather of how each individual journalist conceives of the way things happen in the world. Through their explanations for the outcome of the election, we can glean a bit of their implicit theories of history.

Concept & importance

A theory of history is an explanation of how things generally happen in the world, both in the past and in the future. If, for example, you subscribe to the great man theory of history, then you might explain events by looking at the influential individuals who shaped them. If you subscribe to a

technological determinist

theory, on the other hand, you might explain events in terms of the technologies that allowed them to occur. Or, you might subscribe to a

social determinis

t theory, explaining both influential individuals and new technologies as the makings of greater societal forces.

Someone operating under the great man paradigm might explain Obama’s election as a product of his and his staff’s exacting efforts in the day-to-day of campaign work. A believer in technological determinism might attribute the win to the unprecedented use of social media, which mobilized previously uninterested voters. Someone adhering to a social determinist view might draw a straight line from the Civil Rights era to the election of Barack Obama, pointing out the inexorable cultural shift towards empowering African-Americans.

Everyone has a theory of history...
Mythic Mode
by Valentine — 2018-02-23T22:45:06.709Z

Follow-up to:

The Intelligent Social Web

Related to:

Fake Frameworks

Yesterday

I described a framework for viewing culture as a kind of distributed intelligence, and ourselves as nodes in this distributed network.

Today I’d like to share a way of using this framework intentionally that doesn’t require

Looking

. My main intent here is concreteness: I’d like to illustrate what an application of accounting for the Omega-web can look like. But I also hope this is something some of y’all can benefit from.

I’ll warn up front: this is playing with epistemic fire. I think the skill of clearly labeling when you’re entering and leaving a

fake framework

is especially important here for retaining epistemic integrity. If you aren’t sure how to do that, or if the prospect of needing to unnerves you too much, then it might be right for you not to try using this at least for now.

Scott Alexander created a fascinating impact through his essay

Meditations on Moloch

. A few excerpts:

What’s always impressed me about this poem is its conception of civilization as an individual entity. You can almost see him, with his fingers of armies and his skyscraper-window eyes.

[…]

The Universe is a dark and foreboding place, suspended between alien deities. Cthulhu, Gnon, Moloch, call them what you will.

Somewhere in this darkness is another god. He has also had many names. In the

Kushiel books

, his name was Elua. He is the god of flowers and free love and all soft and fragile things. Of art and science and philosophy and love. Of

niceness, community, and civilization

. He is a god of humans.

The other gods sit on their dark thrones and think “Ha ha, a god who doesn’t even control any hell-monsters or command his worshippers to become killing machines. What a weakling! This is going to be so easy!”

But somehow Elua is still here. No one knows exactly how. And the gods who oppose Him tend to find Themselves meeting with a surprising number of unfortunate accidents.

There are many gods, but this one is ours.

There was basically nothing in that essay that was conceptually new, at least in the social circles I’m in around CFAR. But this essay still had a huge cultural impact. Suddenly it became real how to literally see the demon-god so many of us are fighting, and now we

know

its name: Moloch. This mattered to the web. Now we can actually feel a sense in which we’re battling eldritch horrors in an epic war ...
Two types of mathematician
by drossbucket — 2018-02-23T19:26:19.551Z

This is an expansion of a linkdump I made a while ago with examples of mathematicians splitting other mathematicians into two groups, which may be of wider interest in the context of the recent elephant/rider discussion. (Though probably not especially wide interest, so I'm posting this to my personal page.)

The two clusters vary a bit, but there's some pattern to what goes in each - it tends to be roughly ‘algebra/problem-solving/analysis/logic/step-by-step/precision/explicit’ vs. 'geometry/theorising/synthesis/intuition/all-at-once/hand-waving/implicit’.

(Edit to add: 'analysis' in the first cluster is meant to be analysis as opposed to 'synthesis' in the second cluster, i.e. 'breaking down' as opposed to 'building up'. It's not referring to the mathematical subject of analysis, which is hard to place!)

These seem to have a family resemblance to the S2/S1 division, but there's a lot lumped under each one that could helpfully be split out, which is where some of the confusion in the comments to the elephant/rider post is probably coming in. (I haven't read The Elephant in the Brain yet, but from the sound of it that is using something of a different distinction again, which is also adding to the confusion). Sarah Constantin and Owen Shen have both split out some of these distinctions in a more useful way.

I wanted to chuck these into the discussion because: a) it's a pet topic of mine that I'll happily shoehorn into anything; b) it shows that a similar split has been present in mathematical folk wisdom for at least a century; c) these are all really good essays by some of the most impressive mathematicians and physicists of the 20th century, and are well worth reading on their own account.

The earliest one I know (and one of the best) is Poincare’s ‘Intuition and Logic in Mathematics’ from 1905, which starts:

“It is impossible to study the works of the great mathematicians, or even those of the lesser, without noticing and distinguishing two opposite tendencies, or rather two entirely different kinds of minds. The one sort are above all preoccupied with logic; to read their works, one is tempted to believe they have advanced only step by step, after the manner of a Vauban who pushes on his trenches against the place besieged, leaving nothing to chance.

The other sort are guided by

...
June 2012: 0/33 Turing Award winners predict computers beating humans at go within next 10 years.
by betterthanwell — 2018-02-23T11:25:12.092Z

In June 2012, the Association for Computing Machinery—a professional society of computer scientists, best known for hosting the prestigious ACM Turing Award, commonly referred to as the "Nobel Prize of Computer Science"—celebrated the 100th birthday of Alan Turing.

The event was attended by luminaries like, oh, in no particular order: Donald Knuth, Vint Cerf, Bob Kahn, Marvin Minsky, Judea Pearl, Ron Rivest, Adi Shamir, Leonard Adleman, and of extra relevance here; Ken Thompson, inventor of the UNIX operating system, co-inventor of the C programming language, and computer chess pioneer.

In all, some 33 Turing Award winners were scheduled to be in attendance. There were no parallel tracks or simultaneous panels going on.

Image credit: Joshua Lock

So today, randomly watching one of the panel debates on Youtube, I was amazed by the amusing / horrifying example of failure of foresight and predictive accuracy, by these world leading computer scientists, regarding the advancement of the state of the art in artificial intelligence.

In this case the as it pertains to the ancient board game "go".

https://www.youtube.com/watch?v=dsMKJKTOte0&t=54m

"When does a computer crack go?"

"And I will start by a 100 years, and then count down by ten year intervals."

And [by] 90 years? I count about 4% of the audience. (...)

Perhaps my internet searching skills are weak, but as best as I can tell, the incident has not been noted other than a few bemused Youtube comments in the video linked above.

Given ten options, ten buckets in which to place their bet, world-leading experts in computer science, as a group, managed to perform much worse than one would expect given their vast and wide-ranging expertise.

Worse even, than one would expect of a group of complete ignorants.

Given ten options, one would expect one out of every ten to land on the right answer, if nobody knew anything and everyone made a blind guess.

Three years and three months later, three-time European champion Fan Hui, was defeated. Half a year after that, world champion Lee Sedol was defeated by AlphaGo.

Interestingly, Ken Thompson, to whom the query was first directed, starts his answer by sharing an experience, from a World Computer Chess Championship, around 1980. Participants were asked if and when computers would beat the world champion at the game of chess. And, he explain...
Design 2
by alkjash — 2018-02-23T06:20:00.656Z

This is part 14 of 30 of Hammertime. Click here for the intro.

I am a finger pointing to the moon. Don’t look at me; look at the moon.

Rationalists drone on and on about how our fake our models are, how we gesture at and point to deep inarticulate truths, and – to shoulder some of the blame – the importance of circumambulating the truth rather than honing in on it directly. We spend all too much time insisting we’re fingers pointing at the moon.

Hammertime says: Fuck the moon.

There are trillions of indistinguishable giant space rocks floating around in the universe. But a human finger contains a trillion copies of the source code for the most power intelligence to walk the known universe. If I had to choose, I’d rather spend my days studying fingers than moons, and it’s not even close.

Hammertime is a set of fingers pointing at the moon. Occasionally, it may prove useful to sit back, cross your eyes, and look for the moon: that grand overarching cognitive strategy behind these techniques. But if you miss the moon, fingers are awesome too. So don’t worry. Relax. Just do exactly as I say.

Day 14: Design

Previously: Day 4.

Design is the practice of seeing all the tiny incentive gradients in the environment, and shifting them in your favor. Last time, we took environment to mean physical space, but Design principles apply across domains.

Today I will apply Design principles to the design of Schedules, Social Groups, and Screen Space. As budding self-help guru I dub these (together with Space) the Four S’s of Design.

Keep in mind the three principles of Design:

Intentionality: notice all the knobs you can turn. Turn them the way you intend.
Amortization: pay up-front costs to save attention in the long run.
Reflexive Towel Theory: the aesthetics of your environment determine your self-image.

Schedules

I am no expert on using calendars; this section is about the basics.

What’s the single most important incentive gradient to fix about a calendar? The incentive to use it at all.

Knowing where you’ll be, what you’ll be doing, how much of your project will be done days, weeks, and months in advance is great. Unbelievably great. It would seem as if the incentives are already there. So why don’t people plan everything all the time?

Everyone has different aversions, but I think the biggest one against calendars is categorizing them as productivity tools. My emotions when I first started ...
AI Alignment and Phenomenal Consciousness
by Gordon Seidoh Worley — 2018-02-23T01:21:36.808Z
Link: https://mapandterritory.org/ai-alignment-and-phenomenal-consciousness-2ca23de6aebd

NB: Originally posted on Map and Territory on Medium, so some of the internal series links go there.

In the initial feedback I’ve received on my attempt to formally state the AI alignment problem, the primary objection I’ve heard is that I assume any AI worth aligning will experience qualia. In particular, some think we have to worry about aligning AI that is cybernetic but not phenomenally conscious. I think they are mistaken in thinking cybernetic-only AGI is possible, but in all fairness this is probably due to a failure on my part to adequately explain phenomenal consciousness, so I’ll try here to make clearer the case for phenomenally conscious AGI and how this relates to AI alignment.

To begin, though, let’s step back and consider what we mean by “artificial intelligence”? The artificial part is easy: we mean something constructed through a deliberate design effort rather than arising naturally, i.e. it is the handiwork of conscious things rather than the outcome of an unconscious process. Thus artificial intelligence stands in contrast to natural intelligence, like the intelligence of animals that arose through evolution. The intelligence part is harder because by intelligence we mean multiple things: possession of goal directed behavior (telos), ability to model the world (ontology), and an ability to combine telos and ontology to come up with new solutions to problems (find undiscovered algorithms). Generally we might say intelligence is something like an ability to systematically increase local informational complexity — optimize the nearby parts of the world — by increasing global entropy. Taken together then, artificial intelligence and artificially intelligent agents can be said to be designed optimization processes.

This means, of course, that things as simple as steam engines are a kind of artificial intelligence even if they aren’t especially intelligent since a steam engine is increasing global entropy in the form of waste heat in order to produce mechanical power. And even if we put a governor on our steam engine it is still only cybernetic — a thing that experiences itself — and not phenomenally conscious — a thing that experiences itself experiencing itself — but, as we...
Explanation vs Rationalization
by abramdemski — 2018-02-22T23:46:48.377Z

Follow-up to: Toward a New Technical Explanation of Technical Explanation, The Bottom Line.

In The Bottom Line, Eliezer argues that arguments should only provide evidence to the extent that their conclusions were determined in a way which correlated them with reality. If you write down your conclusion at the bottom of the page, and then construct your argument, your argument does nothing to make the conclusion more entangled with reality.

This isn't precisely true. If you know that someone tried really hard to put together all the evidence for their side, and you still find the argument underwhelming you should probably update against what they're arguing. Similarly, if a motivated arguer finds a surprisingly compelling argument with much less effort than you expected, this should update you toward what they claim. So, you can still get evidence from the arguments of motivated reasoners, if you adjust for base rates of the argument quality you expected from them.

Still, motivated reasoning is bad for discourse, and aspiring rationalists seek to minimize it.

Yet, I think everyone has had the experience of trying to explain something and looking for arguments which will help the other person to get it. This is different than trying to convince / win an argument, right? I have been uneasy about this for a long time. Trying to find a good explanation is a lot like motivated cognition. Yet, trying to explain something to someone doesn't seem like it is wrong in the same way, does it?

A possible view which occurred to me is that you should only give the line of reasoning which originally convinced you. That way, you're sure you aren't selecting evidence; the evidence is selecting what you argue.

I think this captures some of the right attitude, but is certainly too strict. Teachers couldn't use this rule, since it is prudent to select good explanations rather than whichever explanation you heard first. I think the rule would also be bad for math research: looking for a proof is, mostly, a better use of your time than trying to articulate the mathematical intuitions which lead to a conjecture.

A second attempt to resolve the conflict: you must adopt different conversational modes for efficiently conveying information vs collaboratively exploring the truth. It's fine to make motivated arguments when you're trying to explain things we...
The map has gears. They don't always turn.
by abramdemski — 2018-02-22T20:16:13.095Z

Follow up to Toward a New Technical Explanation of Technical Explanation.

Confusion is in the map, not the territory. The same goes for predictability, surprise, mysteriousness, weirdness, and so on. Yet, sometimes, we say "no, actually, that's quite surprising" after initially not being surprised -- and it has a meaning. Or, sometimes we say "ah, right, it is obvious" after being confused and needing to think for a while -- and it has meaning.

Though they may be phrased as map/territory errors, statements like this can refer to the amount of surprise or confusion etc which a specific gears-level model has. A step in a proof may be "obvious" because it is an elementary application of a common method, even if it takes time for a fallible human to see it. A physicist may one day come to realize that a commonplace event is "surprising" after being not-surprised by the event dozens of times, because suddenly they've compared it to a model from physics, and found the event difficult to account for.

Our brain has a big messy model of reality, but it is often trying to simulate smaller, cleaner, more justified, more objective, and hopefully better models. When we do things like this, we are speaking from those models.
The Intelligent Social Web
by Valentine — 2018-02-22T18:55:36.414Z

Epistemic status:

Fake Framework

When you walk into an

improv

scene, you usually have no idea what role you’re playing. All you have is some initial prompt — something like:

“You three are in a garden. The scene has to involve a stuffed bear somehow. Go!”

So now you’re looking to the other people there. Then someone jumps forward and adds to the scene: “Oh, there it is! I’m glad we finally found it!” Now you know a little bit about your character, and about the character of the person who spoke, but not enough to fully define anyone’s role.

You can then expand the scene by adding something: “It’s about time! We’re almost late now.” Now you’ve specified more about what’s going on, who you are, and who the other players are. But it’s still the case that none of you knows what’s going on.

In fact, if you think you know, you’ll often quickly be proven wrong. Maybe you imagine in that scene you’re an uptight punctual person. And then the third person in the scene says to you, “What do you care, Alex? You’re always late to everything anyway!” Surprise! Now you need to flush who you thought you were from your mind, accept the new frame, and run with it as part of your newly evolving identity. Otherwise the scene sort of crashes.

It would go more smoothly if you didn’t hold any preconceptions about who you are or what’s going on. The scene tends to work better if you stay in the present moment and just jump in with the first thing that comes to mind (as long as

it’s shaped by what has happened so far

). Then the collection of interactions and emerging roles spontaneously guides your behavior, which in turn help guide others’ behavior, all of which recursively defines the “who” and “what” of the scene. Your job as a player isn’t to play a character; it’s to co-create a scene.

We can sort of pretend that there’s a “director”: it’s the intelligence that emerges between the players via their interactions. It’s a

distributed system

that computes relationships and context by guiding each node in its network to

act freely within constraints

. From this vantage point, the network guides players, and the job of each player is to ...
The Three Stages Of Model Development
by katerinjo — 2018-02-22T14:33:19.081Z

TLDR: First you go with your gut, then you get a logical model, then you improve that model. Trusting your logical model over your gut before it gets good enough is a very common way to believe wrong things.

[epistemic status: probably approximately true, with possible pathological cases around the edges]

The process of getting better at describing and predicting things seems to usually go something like this:

First, you start out with an intuitive model which nature gives you automatically without any effort on your part. This model uses the language of system 1, and is a black box whose contents are unknown to you.

Then, you develop a weak analytical model in the language of your system 2. Your first try at making an analytical model is generally worse on average at describing and predicting things than your intuitive model, which is why I'm calling it "weak".

Finally, after incrementally improving your analytical model over some period of time, you end up with a strong analytical model, a system that surpasses your intuition.

Advantages

Analytical models are good because they can be easily improved relative to intuitive ones. For example, it is hard to convince your system 1 that getting a vaccine shot is a good idea, but your system 2 can improve its understanding of the world to a point where it understands that getting the shot is worth it.

Analytical models are also nice because you can see how their parts work, which makes it easier to apply lessons learned in one area to another problem.

A model will do better in some situations than others, so whether you should use your intuitive one or your analytical one can be dependent on your situation. The process of figuring out a model's better and worse subjects is beyond the scope of this post.

Composition

All analytical models are ultimately composed out of intuitive models. Maybe you start with an intuitive understanding of what bleggs and rubes are, but then quickly come up with the analytical model that says that bleggs are "objects that are round and blue", while rubes are "objects that are cubes and are red". This model doesn't analytically define what "round", "cube", "red", and "blue" mean yet! Those are defined intuitively to start. But when you go back and define, say, what "blue" means in terms of light and hum...