trentmkelly/LessWrong-43k huggingface dataset:
Welcome to Rationality Graz [Edit With Your Details]
by Lukas Trötzmüller — 2020-01-19T21:11:57.958Z

(The following are our suggestions for what kind of information is best to include in the welcome post of your group, feel free to replace them with whatever you think is best)

What kind of events does your group usually run? What does it usually do?

How frequently does your group organize events or meet?

Who would be a good fit for you group?

Should they have any particular skills or have done some specific background reading?
Use-cases for computations, other than running them?
by johnswentworth — 2020-01-19T20:52:01.756Z

In imperative programming languages, the main purpose of a program is to specify a computation, which we then run. But it seems a rather... unimaginative use of a computation, simply to run it.

Having specified a computation, what else might one want to do with it?

Some examples:

Differentiate numerical computations (i.e. backprop)
Ask whether any possible inputs could yield a particular output (i.e. NP problems)
Search for data within the computation's output space (i.e. grep)
Given output from the computation, find a data structure which traces its execution (i.e. parsing with context-free grammars)
Parallelize the computation
Interventions & counterfactuals: ask what will happen/what would have happened if some internal variable at some step of the computation were assigned a different value, keeping the rest of the computation the same
Generate a new computation which optimizes the output of the original computation via dynamic programming
Find summary statistics describing the computation (i.e. profiler, maximum forces encountered in a physical simulation, ...?)
Embed one computation within another (i.e. a compiler embeds computation specified in Python into the computation performed by a CPU)

Use knowledge of the internal structure of the two computations to optimize the embedding (i.e. optimizing compiler)

Ask what parameters/outputs of the computation need to be observed in order to reliably deduce other parameters/outputs (i.e. inference, identifiability, & experiment design in causal models)
Ask whether observed data is consistent with the computation (i.e. regexes & CFGs; causal model comparison)
Dependency: is the output (or some set of intermediates) independent of some internal value, or independent of the distinction between two (or more) possible values at some point in the computation?
Can we keep around a small amount of data sufficient to quickly reconstruct anything we might want to know about the full computation (i.e. intermediate values and execution path)?
Was any computation repeated (i.e. something we could optimize out via memoization)?
Are there any unused patterns/symmetries in the computational DAG (i.e. potential for refactoring the code)?
Big-O runtime analysis

I'm also interested in more general meta-use-cases for computations, which generate use-cases that aren't just "run the computation". For instance, so...
UML VII: Meta-Learning
by Rafael Harth — 2020-01-19T18:23:09.689Z

(This is the seventh post in a sequence on Machine Learning based on this book. Click here for part I.)

Meta

A more accurate title for this post would be "Meta, Error bounds, Test Data, Meta-Learning, Error decomposition, and an optional introduction to general probability spaces." The first item is me trying to categorize the stuff we've been doing in this sequence so far, which is meta-learning but nonetheless different from the Meta-Learning item, which is about a Machine Learning technique.

Categorizing Machine Learning Insights

Although the book doesn't do this, I think taking some time to reflect on how exactly we've been making progress is very worthwhile. I would categorize the material we've covered so far as follows:

(1) Carving out relevant and learnable classes out of ML problem space

(1.1) Defining such classes and establishing results about their limits

Examples: Linear Predictors (post 4), Convex (bounded, Lipschitz / smooth) problems (post 5), the negative result on convex problems (post 5)

(1.2) Finding algorithms that learn these classes and prove that they work.

Examples: Perceptron (post 4), Linear Programming (post 4), applied linear algebra (post 4), Stochastic Gradient Descent (post 6)

(2) Extending the usability of such classes

Examples: Primarily surrogate loss functions (post 5); also the mapping of inhomogeneous linear problems to linear problems (post 4), even if it's just a detail. There will be more stuff here in future posts.

(3) General (as in, widely applicable) theoretical work

Examples: The general learning model, work on overfitting, the PAC learning framework, the basic error decomposition, the ERM approach (all post 1), the uniform convergence property, the No-Free-Lunch Theorem, the VC dimension, the fundamental theorem of statistical learning (all post 2), the nonuniform learning framework, the SRM approach, the minimal description length, the basics of computational complexity (all post 3), meta-learning, error bounds via test data, and advanced error decomposition (later in this post)

Not quite covered: coming up with learning algorithms. So far, that's just Stochastic Gradient Descent, which I have listed as a solution to solve convex Lipschitz/smooth bounded problems, but it's not restricted to that. Also, neural networks supposedly have an impressive track record of fitting all sorts... ,
Adjusting Outdoor Reset
by jefftk — 2020-01-19T18:20:02.028Z

Our house has forced hot water heat with three loops, two of
baseboards and one of radiators. The first floor unit has a baseboard
loop and on cold winter days can't keep up. The tenants have been
using electric heat [1] to supplement, but that's annoying for them
and resistive electric is much more expensive than gas.

The boiler is a modern efficient one with an outdoor
reset. You have a temperature sensor outside, and on warm days
the system won't heat the water it's circulating to as high a
temperature as on cold days. For example, if it's 55F outside it's
wasteful to be circulating 180F water when you could instead have,
say, 130F water.

Here's how ours was configured, which I think is the factory default:

This says to circulate 180F water at temperatures of 0F and below,
110F at temperatures of 70F and above, and linearly interpolate
between them. Except also never go below 130F.

Since the first floor unit gets too cold in weather well above 0F, it
seemed to me that it should be willing to circulate 180F water in
somewhat warmer weather. I increased the lower limit from 0F to 15F:

At the current temperature of 27F, this means it's targeting 176F
instead of 164F.

I expect this will help the first floor unit, though since I did it
yesterday evening and it's much warmer today I don't know if it
worked yet. I expect this is set higher than would be ideal for the
radiator loop, since those are so massive, and it may increase the
cycle time for those rooms. Perhaps instead of going 63F-66F they'll
be more like 63F-68F, we'll see.

This will probably increase our gas bill, but if it works it should
decrease our electric bill by enough that it saves money. And even if
it just saves hassle for our first floor tenants that's reason enough
to do it.

One thing that surprises me is that the system isn't designed to be
smarter about handling cases it finds itself running constantly. When
the loop for unit 1 isn't keeping up, the thermostat never turns off
and it runs all day. That seems like a good indication that hotter
water is called for. The heating system can determine with the
sensors it already has, so why doesn't it?

[1] We got them a big oil-filled
radiator, since that looked like the safest kind.
Book review: Human Compatible
by PeterMcCluskey — 2020-01-19T03:32:04.989Z
Link: http://www.bayesianinvestor.com/blog/index.php/2020/01/17/human-compatible/

Book review: Human Compatible, by Stuart Russell.

Human Compatible provides an analysis of the long-term risks from
artificial intelligence, by someone with a good deal more of the
relevant prestige than any prior author on this subject.

What should I make of Russell? I skimmed his best-known book,
Artificial Intelligence: A Modern
Approach,
and got the impression that it taught a bunch of ideas that were popular
among academics, but which weren't the focus of the people who were
getting interesting AI results. So I guessed that people would be better
off reading Deep Learning by Goodfellow, Bengio, and Courville instead.
Human Compatible neither confirms nor dispels the impression that
Russell is a bit too academic.

However, I now see that he was one of the pioneers of inverse
reinforcement
learning,
which looks like a fairly significant advance that will likely become
important someday (if it hasn't already). So I'm inclined to treat him
as a moderately good authority on AI.

The first half of the book is a somewhat historical view of AI, intended
for readers who don't know much about AI. It's ok.

Key proposals

Russell focuses a moderate amount on criticizing what he calls the
standard model of AI, in which someone creates an intelligent agent,
and then feeds it a goal or utility function.

I'm not too clear how standard that model is. It's not like there's a
consensus of experts who are promoting it as the primary way to think of
AI. It's more like people find the model to be a simple way to think
about goals when they're being fairly abstract. Few people seem to be
defending the standard model against Russell's criticism (and it's
unclear whether Russell is claiming they are doing so). Most of the
disagreements in this area are more about what questions we should be
asking, rather than on how to answer the questions that Russell asks.

Russell gives a fairly cautious overview of why AI might create risks
that are as serious as the risks gorillas face from humans. Then he
outlines an approach that might avoid those risks, using these three
rules:

The machine's only objective is to maximize the realization of
human preferences.

The machine is initially uncertain about what those
preferences are.

The ultimate source of information about human preferences is
human behavior.

Note that these are high-level guidelines for researchers; he's not at
all claiming they'r

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Is NYC Building Much Housing?
by jefftk — 2020-01-18T20:50:01.676Z

One common response [1] to the claim that we can bring down the cost
of housing

by building
more of it is something like:

New York is has been putting up new housing at an
insane rate, and it's one of the most expensive places in the country.
If building that much can't solve the problem, this approach clearly
doesn't work.

The idea that NYC has been building a lot of housing, however, isn't
right. In 2017 NYC housing grew from 3,445,123 units to 3,469,240 (pdf),
an increase of 0.7%. The same report gives this graph of newly
permitted units:

If it feels like current construction levels are a lot, they are high
compared to after the 2008
crash and the ~1960 to ~1990 period with low
housing demand.
But 0.7% annual growth is much lower than the city needs to keep up
with demand, and it's much lower than historical
growth rates.

The problem is worse than you'd guess by comparing NYC's 0.7% to, say,
Beijing's ~2.5%
because decades of restricted construction have left us with much
lower supply. For example, compare two hypothetical cities starting
at 3M units in 2020 and adding more units at 0.7% vs 2% per year:

After twenty years the 2% city has 1M (30%) more units than the 0.7%
one.

An approach of heavily
restricting supply for decades, not just in NYC but in many
high-demand cities, has caused the remaining housing to be bid up to
levels far above the cost of construction. We should fix this and we can fix
this: let people build housing!

[1] Prompted by my getting into arguments with two different
random people giving this response within hours of each other.
The Road to Mazedom
by Zvi — 2020-01-18T14:10:00.846Z

Previous post: How Escape From Immoral Mazes

Sequence begins here: Moloch Hasn’t Won

The previous posts mostly took mazes as given.

As an individual, one’s ability to fight any large system is limited.

That does not mean our individual decisions do not matter. They do matter. They add up.

Mostly our choice is a basic one. Lend our strength to that which we wish to be free from. Or not do so.

Even that is difficult. The methods of doing so are unclear. Mazes are ubiquitous. Not lending our strength to mazes, together with the goal of keeping one’s metaphorical soul intact and still putting food on the table, is already an ambitious set of goals for an individual in a world of mazes.

We now shift perspective from the individual to the system as a whole. We stop taking mazes as given.

It is time to ask why and how all of this happens, and what if anything we can do, individually or collectively, about it.

In particular, this post presents an explicit model of the first question, which is:

How did mazes come to be, both individually and overall?

This is partly a summary of the model developed so far. It is partly making the model more explicit, and partly the fleshing out of that model with more gears.

If there are points here for which you believe the previous posts failed to lay the groundwork they should have laid, please note that in the comments by number, so I can consider fixing that. Keep this distinct from any disagreements or other notes on these claims, which are also welcome.

Every organization has an organizational culture. That culture can and does change.

Those who focus on their own advancement at the expense of other considerations will, by default, advance further, faster and more often. Those who do not do this will not advance. Increasing amounts of focus make this effect increasingly large.

Focus on one’s own advancement inside hierarchies causes individuals to self-modify in order to be the type of person who automatically engages in maze-creating and maze-supporting behaviors. They will also see such behavior as natural and virtuous.

Middle management performance is inherently difficult to assess. Maze behaviors systematically compound this problem. They strip away points of differentiation beyond loyalty to the maze and willingness to sacrifice one’s self on its behalf, plus politics. Information and records are destroyed. Belief in the po

...
What types of compute/processing could we distinguish?
by MoritzG — 2020-01-18T10:04:03.380Z

I am a bit confused and thought I'd rather ask and discuss here before thinking about it for long. As usual I am trying to compartmentalize, structure, make distinctions.

My confusion was triggered thinking about the evaluation function (heuristic to rate the certainty to win/loose) in chess. Clearly what it takes is all there on the board, actually the game is already decided based on that state and assuming both player play to force the best possible outcome.

Why do we need to process data when the information is obviously already in the input? (Yes, I know one can make wordy the distinction between data, information, knowledge, wisdom, understanding.)

Here a few cases, as examples:

Chess: The game state takes few bit to store, yet it takes a lot of processing to decide on a move.

Search: I know what I want but still have to go through a much bigger data set.

Statistical inference: I came up with an hypotheses and now I have to go through tons of data to see if it is consistent.

Automation: I am trying to control something and must derive actions from measurements.

I suppose any compute is just a replacement for a huge LUT, proving that the information is already in the input, but then searching that LUT would still take compute.

There are transformations that just change the perspective, but do not discard or compress.

There is compression were I either only care for decisive aspects (filter / lossy) or there is redundancy in the input (lossless).

In the end it is always about deriving efficient actions to reach goals. For that, I suppose, we can make a one fits all scheme: Make and maintain model. Measure current state. Filter measurement. Compute possible outcomes. Evaluate possible outcomes impact and likelihood. Decide on / search for best action.

But what I find so confusing is the contrast of starting out with little information or a lot but that does not explain alone how much there is to compute. Why can I have little information but still have to search a huge state space, why can't I go straight to the conclusion/action?

Obviously I am thinking about it wrong, there might not be an antagonism but a confusion of stages.
Political Roko's basilisk
by Abhimanyu Pallavi Sudhir — 2020-01-18T09:34:19.981Z

Why has there never been a "political Roko's basilisk", i.e. a bill or law that promises to punish any member of parliament who voted against it (or more generally any individual with government power, e.g. judge or bureaucrat, who did not do everything in their capacity to make it law)?

Even if unconstitutionality is an issue, it seems like the "more general" condition would prevent judges from overturning it, etc. And surely there are countries with all-powerful parliaments.
Risk and uncertainty: A false dichotomy?
by MichaelA — 2020-01-18T03:09:18.947Z

Overview

We’re often forced to make decisions under conditions of uncertainty. This may be empirical uncertainty (e.g., what is the likelihood that nuclear war would cause human extinction?), moral uncertainty (e.g., does the wellbeing of future generations matter morally?), or one of a number of other types of uncertainty.

But what do we really mean by “uncertainty”?

According to [one] view, certainty has two opposites: risk and uncertainty. In the case of risk, we lack certainty but we have probabilities. In the case of uncertainty, we do not even have probabilities. (Dominic Roser [who argues against this view])

So what are we really talking about - risk, or (Knightian) uncertainty? What is such a distinction meant to mean? Does such a distinction make sense? What significance might this distinction have for how we resolve and make decisions given a lack of certainty? And what about unknown unknowns, black swans, and incomplete models?

These are the questions I discuss in this post, arriving at the following claims:

The risk-uncertainty distinction is usually not adequately specified; it lets a lot of the “work” be done by ambiguous phrases such as whether probabilities are “known” or “exact”.[1]

Proponents of the risk-uncertainty distinction usually seems to discuss it as if it’s an absolute, binary distinction, or fundamental dichotomy; as if in some cases we really do “know” (or “really can estimate”, or whatever) the probabilities of interest, while in other cases we really can’t at all. To be clear, the alternative to this view is the idea that:

sometimes we have extremely little basis for estimating probabilities

other we times have extremely good basis for estimating probabilities

but these situations aren’t fundamentally different

there’s a continuum of possibilities in between those extremes

(I would obviously agree with the above four points, and thus with the idea of distinguishing between more “risk”-like situations and more “(Knightian) uncertainty”-like situations, along a continuum. This post collects some terms and concepts relevant to this more continuous view.)

That’s a false dichotomy; no absolute, binary distinction can be made.

One quick quote to indicate that this view isn’t unique to me: “Bayesian approaches to probability treat it as a degree of belief and thus they do not draw a distinction between risk and a wider concep

...
Remote AI alignment writing group seeking new members
by rmoehn — 2020-01-18T02:10:38.618Z

If you're interested, then email me (<given name of President
Nixon>.moehn@posteo.de) or PM me on LessWrong.

We meet weekly for 15 min ⨉ number of members. At the moment the meetings consist of explaining aspects of our
research to each other. This helps us get better at explaining (sometimes off
the cuff), as well following technical explanations and coming up with questions
about them. Also, questions from others are a good way to find holes in one’s
understanding and patch them. – Better find them among friends and patch them
early than getting flustered in public later.

The research topics of the two current members are:

impact measures/instrumental convergence à la Alex Turner

iterated distillation and amplification à la Paul Christiano

Even if your topic is far afield, we'll try and make it work. Explanations and questions are universal.

There used to be four members. This means:

The meetings are useful for some and not for others. If you're unsure, I
suggest you join. If you don't get enough value for your time, you can leave again. We
respect that.

Two spots are free!

See also the original
announcement.
"How quickly can you get this done?" (estimating workload)
by kerspoon — 2020-01-18T00:10:55.671Z

Epistemic status: based partly upon what I learnt as a certified ScrumMaster and mainly from practical experience running a software development team. Some of the later ideas come from Gower Handbook of Project Management, Judgement in Managerial Decision Making, and How to Measure Anything.

When asked how long something will take, you have hidden ambiguity in the question and multiple sources of error. Here we will attempt to clarify both and start to address some of these issues.

What is the question?

Let's start with the ambiguity of the question. Lets ignore ambiguity over the definition of 'finished' and any errors in your estimation for now. Lets also assume that you already have a prioritised list of projects/work-packages, some of which will be in-progress, some wont have been started yet (I will take the terminology from SCRUM and call this the 'backlog' but it can apply to anything, not just projects as we traditionally think of them). Here are a few things that I think people actually mean when that ask "how long will this [project] take?":

If you started this now and worked on nothing else (during your usual work hours) when would this task be completed?

If you started this project now and worked on no other projects (during your usual work hours), but still had to deal with the usual non-development work (such as meetings, firefighting and emails) when would this task be completed?

If you worked on this project as the absolute highest priority work. How many hours would you have spent on this project by the time it is finished?

Given that there are other projects that are in progress or even higher priority than this, and that some of your time is spent in non-development work. When will this project be finished?

As with 4 but realising that you are likely to add other work into the backlog, some of which will be higher priority than the project in discussion.

There is an important subtlety to the phrasing of these questions which greatly changes their answers. This is not a problem if the questioner and the estimator agree on the definition but if you wanted an answer to variant 5 but got the answer to variant 1 then there is a problem.

Lets go through an example to show how these variants differ and which is the best version in different scenarios.

Example

A customer requested a feature to be added to some software and that you are responsibl

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