trentmkelly/LessWrong-43k huggingface dataset:
A Scalable Urban Design and the Single Building City
by Edward Swernofsky — 2020-07-25T19:06:17.206Z

Crossposted from Medium. Initial description of proposed infrastructure.

Epistemic status: I've read a few books on the subject, spent a few months trying different things and working out the details, and am fairly confident this is at least worth a shot; definitely some kinks to work out; would appreciate writing and technical feedback.

-updated 07/26/2020 with more notes on sunlight, windows, and fires-

-transit network section updated 08/02/2020-

This article will be an exploration of some of the main issues people encounter with cities, the causes of those issues, and a comprehensive set of partial solutions and possible infrastructural innovations.

Problems with Existing Cities

One of the main issues people have with cities is the cost of housing. If you try to buy a house in a suburban or urban area, the suburban house will tend to be cheaper. In fact, if you just try to buy land, the suburban land will be cheaper. The land can be most of the property price difference for small buildings, though there may be other things at play like an artificially limited housing supply.

Why is space so expensive in a city? Well, it’s extremely valuable. Everyone wants to live there. Utilities such as water, electricity, trash, internet, and mail are much cheaper. There are more people around. You can buy whatever you want from tons of different stores. There’s a certain economy of scale about it. A lot of people and things in one place reduce a lot of the cost of moving around or bridging distances. There are more job opportunities. Everyone wants to put businesses there. It’s a lot easier to find people to work for you with more people nearby looking for a job.

Actually, why are buildings taller in a city? Higher is more expensive, right? Construction costs tend to depend most on whether the frame is wood, concrete, or steel, and whether there are stairs or elevators. Both of these mean higher numbered floors are more expensive. Despite this, if the land is valuable enough, it’s easier to make a 2nd or 20th floor than it is to buy more land and put a single story building up. In other words, buildings tend to be taller because the space they afford is worth the added expense.

Urban design, infrastructure, and regulations can also be uncoordinated, raising prices or imposing perverse restrictions. There are many different people with differen...
Constraints from naturalized ethics.
by Charlie Steiner — 2020-07-25T14:54:51.783Z

Humans are just bundles of atoms, and so for them to reflect on themselves and their goals, it's plausible that there's some interesting naturalized ethics going on. This informs how we'd think about an AI intended to do human value learning. Let's frame this as a dialogue, so my hypothetical human can explain what they're thinking.

Human: I don't actually have goals in the precise mathematical sense, what we call my 'goals' are good yet imperfect patterns that I or others can use to describe my thoughts and behavior.

Foil: I don't see how an AI is supposed to learn your goals then. Can you at least specify exactly what pattern you mean by your goals?

Human: I don't think so - my thoughts and behavior under-determine those patterns. If we just try to model my thoughts and behavior a priori, what we probably get is some kind of fairly accurate simulation of me that doesn't have any neatly-labeled parts called 'goals.'

Foil: Can't you just tell the AI to model you in terms of goals?

Human: Even if we add a prior to tell the AI to model me in terms of goals, not only do we have to figure out how to pick a good prior, but also there's the problem that the predictvely-best model of me really is the simulation, and so there's a pressure on the AI to encode a simulation of me within whatever model we make it use, which probably will break whatever common-sense properties the things labeled 'goals' are supposed to have.

Foil: How did you start thinking in terms of goals in the first place, then?

Human: Well, I'm not even sure that I think in terms of goals. I care about them, but as you can see I can't precisely pin them down. It feels like I do my actual modeling of humans in terms of a bunch of related heuristics, and then use the label 'goals' to encapsulate planning-related parts of many of these heuristics. I also owe a lot to language - 'goal' is a word after all, and it fits within an entire mode of thinking about the world verbally.

Foil: If goals show up in human heuristics and language, they don't seem that hard to at least get a good approximation of. What's the problem?

Human: I still think of those heuristics as being about goals, not being the goals themselves. This is where I get notions like "even if you could convince me of s...
What it means to optimise
by Neel Nanda — 2020-07-25T09:40:09.616Z
Link: https://www.neelnanda.io/blog/mini-blog-post-9-what-it-means-to-optimise

(This is a post from a daily blogging experiment I did at neelnanda.io, which I thought might also fit the tastes of LessWrong)

If you’ve gotten to my ninth post, it’s probably no surprise to you that I’m a big fan of optimising my life! Optimisation, as I define it, is the process of taking actions that best achieve your goals.

As stated, I consider it pretty tautologically obvious that this is a good idea. You can take actions that bring you closer to your goals, or you can take other actions. But I get into pretty frequent arguments about whether this is a good idea. And it’s easy to get defensive, and fall back to this definition, and argue that optimisation is by definition a good idea. But I think it’s valuable to engage with criticism and take it seriously, and to see what you can learn from it.

My best explanation is that this criticism is really directed at what I’ll call naive optimisation. Like a robot, who’s told to clean the dust in your room, but never told “priceless Ming vases are important”, and smashes the vase to clear the dust beneath it. An approach that’s focused on being cold, calculating, and driving as hard as you can towards a specific utility function, with no regard for restraint or common sense. And I completely agree that it’s dumb to be a naive optimiser - but I think it’s dumb because naive optimisation does not best achieve its goals. And if someone can explain how an approach to optimisation doesn’t best achieve your goals, that’s not a problem with the concept of optimising, it’s a problem with how you are trying to optimise. You shouldn’t aim to “try as hard as you can” or “do the cold, calculated action”, you should aim to do whatever action best achieves your goals. And this includes asking whether what you think your goals are, are truly your goals.

But this is all easier said than done. And especially for the kind of person who enjoys thinking about optimisation, I think there are a lot of ways to accidentally fall into the trap of being a naive optimiser. So the rest of this post will be a list of the most common criticisms, and how best to account for them:

Objection 1: Optimisation is bad for you. Eg, someone who optimises for their studies, works 12 hour days with no breaks, and

...
Access to AI: a human right?
by dmtea — 2020-07-25T09:38:35.271Z

Authors, Lawyers, Teachers, Researchers, Doctors, Coders. These are just some people who've found that their work can be performed dramatically better with the right implementation of GPT-3/ Large Transformer models. Due to this, I poist that access to the best AI models will become as fundamental to productivity of most jobs, as access to a computer and internet is today.

Unlike computers, large transformers cost millions to train and run- thus there is a far greater risk of centralisation, control and surveillance. OpenAI has announced that they will be closely monitoring access and cancelling access to anyone indulging in harmful usage. Yet this kind of power to exclude somebody from a basic tool of productivity, or censor what they use the tool for, is far too risky for any private organisation to have. Specifically, when any human has such power, even with good intentions, they are susceptible to causing harm/denying access to legitimate users due to pressure, threats, and deception.

If transformer-based AI is going to be a fundamental tool in most high value jobs in the next couple of years, that will likely cause unprecedented concentration of power in the hands of big tech companies: especially if they continue to enforce censorship rules. Imagine if Amazon, Google & OpenAI could decide that you don't get access their transformer models anymore, because they don't like what you tweeted. In many ways, this would be much worse than losing your job - without access to the AI, you might never be competitive in the job market again.

---xxx---

Imagine it is 2021 in a parallel universe, where Apple is the only company manufacturing personal computers- let's say they have exclusive control over mines of a rare earth mineral essential for chip manufacturing. Their latest (mandatory) software update comes with an AI tool that can detect who is using the computer by monitoring their mouse movement and typing styles (let's assume these are very hard to fake). Now, Apple has suddenly gained the power to effectively exclude anyone from the entire tech ecosystem.

You're a journalist. The morning after writing a hard hitting piece criticising one of the world governments, you try to log on and see people's reactions. Instead, you're greeted with a message : "Apple has suspended your computer access due to violation of term...
How to learn from conversations
by Neel Nanda — 2020-07-25T09:36:16.105Z
Link: https://www.neelnanda.io/blog/mini-blog-post-5-how-to-learn-from-conversations

(This is a post from a daily blogging experiment I did at neelnanda.io, which I thought might also fit the tastes of LessWrong)

I’m currently in the middle of a virtual conference, so one thing heavily on my mind is how to get the most out of talking to people who know more than me. How to get as much information as I can, how to get it efficiently, and how to ensure I actually understand it and retain it! I think this is a very learnable skill, and one that I’ve cultivated a lot over the past 3 years. Having fascinating conversations with interesting people is one of my great joys in life, and I care a lot about being able to do this well! In this article I’ll try to convey some of the key tools I’ve developed!

Health warning: I get the feedback that I come across as fairly intense in conversations, and sometimes give the vibe that I’m judging whether the other person is worth my time. I try to minimise these, and to pay attention to the other person’s comfort level, but this seems like a pretty inescapable cost of this approach!

Ask questions

This one is pretty self-explanatory - ask a lot of questions! You want to make life as easy for the other person as possible. Shaping a conversation, and figuring out what to talk about takes a lot of cognitive work, and asking question makes life a lot easier for the other person
Technique: Notice curiosity

I find that often conversations, especially small talk, end up being about stuff that just isn’t that interesting. And this is terrible! If you’re talking about things you both find exciting, that’s a mutual win!
My main solution to this is to notice the hint of curiosity, or excitement, at what the other person is saying. Often someone’s statements have a lot of different points and information in them, some of which is much more exciting than others. I’ve found it valuable to practice introspecting on how excited I feel about each part, and asking questions about the bit that feels coolest

This often needs several layers of iteration, but is a pretty efficient way to move from small talk to something new and cool!

Introspection is a difficult skill, and generally Noticing emotions is something that I could talk at length about. But it’s something you can get better at with practice

Main points: You want to get as much surface area on the

...
Taking the first step
by Neel Nanda — 2020-07-25T09:33:45.111Z
Link: https://www.neelnanda.io/blog/mini-blog-post-21-taking-the-first-step

(This is a post from a daily blogging experiment I did at neelnanda.io, which I thought might also fit the tastes of LessWrong. This is about my toolkit for getting started on hard things)

Introduction

A lot of my previous posts have been about prioritisation and planning - it’s easy to fall into the trap of taking actions that don’t lead to your goals, and extremely important to predict and avoid this. But another key skill is implementation - going from knowing the actions to take, to actually doing anything. It’s easy to feel inspired, and to feel as though you have direction, but for this to ultimately go nowhere. So it’s vital to cultivate the ability to get shit done.

I’m a massive procrastinator, and a perfectionist, so by default I’m pretty bad at this. I find it very easy to perpetually put things off, and feeling overwhelmed at the thought of starting. Or, feeling guilt centred around not getting started, but it never quite feels urgent enough to do anything about it. And to then either miss deadlines, or leave everything until the last minute, where the pressure builds up, and then I end up doing a half-assed job. This is an incredibly sub-optimal, but is essentially the default state of the world for me. In this post, I will attempt to outline some of my tools for getting past these failure modes, and especially focusing on getting started - the point where I most often fail.

My underlying model is outlined in my previous post on systems: willpower is a limited resource. When we have an unpleasant task to do, there’s a barrier to starting, an activation energy we need to spend. And we have a bunch of mental pressure from deadlines and obligations that eventually get us to spend the required willpower to get past this barrier. This motivates two obvious approaches which I’ll outline in the first half - increasing the mental pressure, and lowering the activation energy. And explains why I focus on getting started - activation energy isn’t the same thing as what it costs me to continue working on a task, there are significant switching costs, while if I’m already in the flow of a task I have inertia behind me.

I think it’s also easy to fall into the trap of just pushing the Try Harder button - trying harder and putting more effort in, but in the most obvious and accessible way...
Become a person who Actually Does Things
by Neel Nanda — 2020-07-25T09:29:21.314Z
Link: https://www.neelnanda.io/blog/become-a-person-who-actually-does-things

(This is a post from a daily blogging experiment I did at neelnanda.io, which I thought might also fit the tastes of LessWrong)

There are two kinds of people in the world: People who actually do things, and people who don’t.

Now I’ve got the unnecessarily provocative opening line out of the way, what do I actually mean by this? To achieve a goal, this breaks down into two parts: figure out what needs to be done, and actually do it.

As stated, this sounds completely trivial. But I think there’s a really important and non-obvious point in here. Some part of is convinced that the main bottleneck for achieving my goals is being smart, coming up with the best plan, getting all of the necessarily information. And this is true! It doesn’t matter how good you are at doing things, if what you’re doing is crap. But it also doesn’t matter how good my plan is if I never act upon it.

And it’s really easy to miss this point. It’s easy to always think “I can do this tomorrow” or “this is not a priority” or “this would never work”. And it’s hard, because sometimes those thoughts are correct. But I find that, unless I put in active effort, I’m the kind of person who’ll always have those thoughts. I’ll never actually act upon my goals, change things about my life.

One of the main symptoms of this is that I’m always aware of problems in my life, and will often be aware of them for a very long. I’ll notice that I don’t exercise enough, that I’m not enjoying the courses I’m studying, that I really dislike being single, that I don’t have enough time to do what I find most fulfilling. But there won’t be a connection in my mind from “this is a problem” to “what am I going to do about it”. It’s not that I think through the problem, and conclude that I can’t do anything, it’s that it never feels like there’s a question to be asked in the first place! And I think this is a problem that extends far beyond me - I notice some amount of this in most of my friends, and I think it’s incredibly widespread. There seems to be a mental block between “things are not as I want them to be” and “I can actually do something about this”.

I th...
Construct a portfolio to profit from AI progress.
by sapphire — 2020-07-25T08:18:05.633Z

It is probably impossible to hedge against a FOOM scenario. But it seems like there are plausible 'slow' but still transformative AI scenarios we might want to hedge against.

Here is an example of a portfolio you could construct:

40% VUG (covers most larger us tech companies, has some stuff you don't specifically want but gives you some hedging)

40% IGV (smaller us tech companies)

20% CQQQ (china, really high expenses but oh well)

A related question is how you could add exposure to an existing more balanced portfolio. (example answer: Buy CQQQ and IGV)

I am fully aware that trying to get lots of exposure to AI progress is the opposite of diversification for most lesswrong users. But if you are expecting rapid progress soon it might still be worth doing.

Some sub-questions that might be useful:

-- Which US Tech ETfs are most useful

-- Should people buy specific stocks instead of ETFs? Which ones?

-- How should we get Exposure to China? Do we want/need China tech exposure?

-- How do we get exposure to areas besides the USA and China? Do we need to?

-- Should we invest in land if we expect AI progress?

-- Should we directly invest in hardware (Nvidia has already gone up a lot)?
Using books to prime behavior
by ryan wong — 2020-07-25T05:59:54.274Z

My purpose for reading has shifted from "I want to learn this" to "I want to use this to prime my behavior".

After years of consuming psychology, business management and generally "how-to" books(as opposed to "what" books), I've learnt that I will only have gotten something tangible out of the book if it changes my behavior. If some of my mental framework(with regards to eg social interaction, persevering in hard workouts or challenging problem sets etc) shifts and I'm acting in a different way than before.

This works especially for biographies. When reading about someone I emulate, I tend to step into his/her shoes and adopt his/her mental disposition for a trait I'm weak in, or a similar problem I'm trying to solve in my daily life.

Problem is, this priming doesn't last for long. After a week or two the effects tend to be diminished. All the accumulated knowledge of "stand up confidently straight as if you were being pulled at the crown of your head by a string from the ceiling", or "the mind is like a muscle too, and can be trained by effortful focus" tend to fall prey to the forgetting curve.

This might be a sign that the knowledge has become unconscious, implicit. It would however be nice if I can consciously feel that I'm using it that so as to motivate further efforts.

One can used spaced repetition techniques to shift hard facts to long-term memory. My question is, how can a similar thing be done for soft skills and mental frameworks?
Reveal Culture
by MalcolmOcean — 2020-07-25T03:36:28.525Z

(crossposted from malcolmocean.com; originally published June 2015)

I have things to say about the Ask/Guess/Tell Cultures model, and an addition/amendment to propose: Reveal Culture. Shifting cultures is hard, so what you’re about to read is not going to have a quality of “let’s all go do this!” I do think it’s worth talking about a lot more, and working on gradually and creatively with others who are game to experiment with culture-crafting.

This post is going to assume that you’re familiar with the Ask/Guess Culture model at the very least. I don’t want to have to explain the whole concept from scratch. The post is written with a Tell Culture familiar audience in mind, although I think it would be worth reading without it. I will talk about each in turn and my understanding of how they work, so you understanding them well is not a prerequisite for this post.

I do want to note that I think it makes more sense to talk about “ask cultures” or even “Guess-based cultures” though, rather than in the singular. This is helpful for keeping salient the fact that there are many very different cultures built upon the platform of Ask or of Guess.

So I’m going to use Majuscule Singular to talk about the platforms and lowercase (usually plural) to talk about the cultures themselves. I think this is just good thinking practice.

Why am I using a new term?

I want to talk about a new cultural platform: Reveal Culture.

It has similarities to Tell Culture, but I’m choosing a new name for three reasons:

because I think that people read a lot into the names (for example assuming that if you ask a question then it must not be Guess Culture) (more on this as a general issue)
I think the name “Reveal Culture” suits this particular thing better than “Tell” (For what it’s worth, I think that “Infer” probably suits Guess a lot better than “Guess” does.)
I don’t want people to associate what I’m putting out with those who are trying to do Tell Culture with everybody just based on reading Brienne’s post. A culture doesn’t shift overnight: the Reveal-based culture that I have experience with has been working at this for over a decade (I’ve been involved for 3 years) and it’s still very much a work in progress.

I’ll talk later about why I’ve chosen the name “Reveal”. Right now I want to talk about the structure of the models.

Why are they called “cultures” and not just “styles” or “strategies”?

In in...