trentmkelly/LessWrong-43k huggingface dataset:
Potential Ways to Fight Mazes
by Zvi — 2020-01-28T22:50:00.352Z

The previous post listed some of the causes of higher than normal maze present in today’s society.

This post asks what might be done about it, both as an individual and via group coordination.

I believe it is better to first ask “what are the things that would help?” even if one does not know how to implement them or convince others to get them implemented or even if they are good ideas worth implementing. Then figure out if any version of the idea is worth implementing. Then ask how to put the ideas into practice.

All of this assumes that lowering maze levels is both desirable and important. This post takes that assumption as its premise. My case for it has already been made previously.

Some items on this list are political interventions. They require solving the collective action and public choice problems of writing and passing good versions of the appropriate laws and regulations.

Some of these items are cultural shifts that happen one person at a time. This is a different kind of collective action problem. Word would be spread, potentially convincing others to support the cause.

In both cases, pointing out that coordination is hard, and that game theory says any given person by default shouldn’t cooperate or devote any effort, are important things to remember. They are not full justifications for dismissing potential solutions.

Finally, there is one last solution that asks what one determined person or organization with extensive resources might do, and comes back with the answer of quite a bit. That will be the next post.

As a reminder, I am not, except where explicitly saying so, endorsing the political proposals as good ideas or rejecting them as bad ones. Nor am I interested in having political policy debates, or any other political debates, on the internet – any comments along these lines will be ignored at best and deleted at worst. I am only saying these actions might help with the problem of mazes, without being obviously catastrophic.

This post considers nine potential ways to fight mazes in general, out of nine I could come up with. The last method will be the next post in the sequence.

Potential Partial Solutions

Solution 1: Knowledge

Not all solutions start with knowledge of the problem. Most of the solutions are good ideas anyway even if you don’t know about mazes.

Knowledge remains a logical place to start any list of solut

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Draining the swamp
by jasoncrawford — 2020-01-28T21:37:03.542Z
Link: https://rootsofprogress.org/draining-the-swamp

In an earlier post, I outlined our main weapons against infectious disease, including vaccines, antibiotics, antiseptics, pest control, sanitation, and general hygiene. These technologies (in a broad sense, even hand-washing is a technology) have largely eliminated lethal diseases such as smallpox, malaria, cholera, tuberculosis, and polio, at least in the developed world.

But which of these technologies mattered most? Which should we highlight in a history of health and medicine, and which should we hold in our minds as major examples of human progress against disease?

Most histories of medicine give the spotlight to vaccines and antibiotics. They’re the most effective medical treatments; prior to their introduction, there was little a doctor could do for an infected patient.

But to really answer the question, we should look at mortality rates over time, by disease where possible, and correlate reductions in mortality to specific interventions effective against specific classes of disease. Otherwise we run the risk of assuming that just since something is in all the histories, it must be the most important (leading me to then feature it prominently in my history, thereby perpetuating the cycle).

So I started looking into the data and interpretations of it. And the surprising thing I found is that infectious disease mortality rates have been declining steadily since long before vaccines or antibiotics.

First, let’s look at the data that clearly shows that something was going on prior to any effective medical treatment for most diseases.

Here’s a chart of 20th century mortality in the US [1] (the spike around 1918 is a major worldwide influenza epidemic, sometimes known as the Spanish Flu):

US crude mortality rates
Armstrong, Conn & Pinner 1999, Fig. 2

And here’s a similar chart I made for England and Wales:

Mortality rates in England & Wales, age- and sex-standardized to 1901
Data from McKeown, Record & Turner 1975

Mortality rates in both regions fell, and most of the improvement came from the infectious disease mortality rate, which was reduced by more than an order of magnitude.

Extending the analysis further into the past is difficult. Data is available back to the mid-1800s, but disease classification changes over time as scientific and medical knowledge advances, so it’s not always possible to trace the mortality rate from a single disease; a

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Hello, is it you I'm looking for?
by Knuckels McGinty — 2020-01-28T20:56:09.191Z

Hi

Sorry if diving in with my question is a breach of your etiquette, but I have a kind of burning question I was hoping some of you guys could help me with. I've been reading the core texts and clicking around but can't quite figure out if this has been covered before.

Does anyone know of any previous attempts at building a model of ranking the quality of statements? By which I mean ranking things like epistemic claims, claims about causation and that kind of thing. Something that aims to distill the complexity of the degrees of certainty and doubt we should have into something simple like a number? Really importantly, I mean something that would be universally applicable, objective (or something like it) not just based on an estimate of one's own subjective certainty (my understanding of Bayesian reasoning and Alvin Goldman style social epistemology).

I've been working on something like that for a couple of years as a kind of hobby . I've read a lot of things on subjects that are adjacent (probability, epistemology, social psychology) but never found anything that seems like an attempt to do that.

I think that means I'm either a unique genius, a crazy person or bad at describing/ searching for what I'm looking for. Option 1 seems unlikely, option 2 is definitely possible but I suspect that option 3 is the real one. Does anyone know of any work in this area they can point me towards?

Cheers - M
Using vector fields to visualise preferences and make them consistent
by MichaelA — 2020-01-28T19:44:43.042Z

This post was written for Convergence Analysis by Michael Aird, based on ideas from Justin Shovelain and with ongoing guidance from him. Throughout the post, “I” will refer to Michael, while “we” will refer to Michael and Justin or to Convergence as an organisation.

Epistemic status: High confidence in the core ideas on an abstract level. Claims about the usefulness of those ideas, their practical implications, and how best to concretely/mathematically implement them are more speculative; one goal in writing this post is to receive feedback on those things. I’m quite new to many of the concepts covered in this post, but Justin is more familiar with them.

Overview

This post outlines:

What vector fields are

How they can be used to visualise preferences

How utility functions can be generated from “preference vector fields” (PVFs)

How PVFs can be extrapolated from limited data on preferences

How to visualise inconsistent preferences (as “curl”)

A rough idea for how to “remove curl” to generate consistent utility functions

Possible areas for future research

We expect this to provide useful tools and insights for various purposes, most notably AI alignment, existential risk strategy, and rationality.

This post is structured modularly; different sections may be of interest to different readers, and should be useful in isolation from the rest of the post. The post also includes links to articles and videos introducing relevant concepts, to make the post accessible to readers without relevant technical backgrounds.

Vector fields and preferences

A vector represents both magnitude and direction; for example, velocity is a vector that represents not just the speed at which one is travelling but also the direction of travel. A vector field essentially associates a vector to each point in a region of space. For example, the following image (source) shows the strength (represented by arrow lengths) and direction of the magnetic field at various points around a bar magnet:

Figure 1.

Another common usage of vector fields is to represent the direction in which fluid would flow, for example the downhill flow of water on uneven terrain (this short video shows and discusses that visualisation).

We believe that vector fields over “state spaces” (possible states of the world, represented by positions along each dimension) can be a useful tool for analysis and communication of various

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If Van der Waals was a neural network
by George3d6 — 2020-01-28T18:38:31.561Z
Link: https://blog.cerebralab.com/If_Van_der_Waals_was_a_neural_network

If Van der Waals was a neural network

At some point in history a lot of thought was put into obtaining the equation:

R*T = P*V/n

The ideal gas equation we learn in kindergarten, which uses the magic number R
in order to make predictions about how n moles of an “ideal gas” will change in pressure, volume or temperature given that we can control two of those factors.

This law approximates the behavior of many gases with a small error and it was certainly useful for many o' medieval soap volcano party tricks and Victorian steam engine designs.

But, as is often the case in science, a bloke called Van der Waals decided to ruin everyone’s fun in the later 19th century by focusing on a bunch of edge cases where the ideal gas equation was utter rubbish when applied to any real gas.

He then proceeded to collect a bunch of data points for the behavior of various gases and came up with two other magic numbers to add to the equation, a
and b, individually determined for each of the gases, which can be used in the equation:

RT = (P + a*n^2/V^2)(V – n*b)/n

Once again the world could rest securely having been given a new equation, which approximates reality better.

But obviously this equation is not enough; indeed it was not enough in the time of Van der Waals, when people working on “real” problems had their own more specific equations.

But the point, humans gather data and construct equations for the behavior of gases exist, the less error prone they have to be, the more niche and hard to construct (read: require more data and a more sophisticated mathematical apparatus) they become.

But let’s assume that Van der Waals (or any other equation maker) does all the data collection which he hopes will have a good coverage of standard behavior and edge cases… and feeds it into a neural network (or any other entity which is a good universal function estimator), and gets a function W.

This function W is as good, if not better than the original human-brain-made equation at predicting the behavior of gasses and fitting the collected data.

The way this function would work, assuming it was build using a neural network, would be something like:

W(V,T,n,gas) => P

Or maybe another function:

W(P,V,n,gas) => T

We got rid of R, a and b and replaced them with a simple categorical variable, gas (the name of the gas which...
Assortative Mating And Autism
by Scott Alexander — 2020-01-28T18:20:02.223Z

Introduction

Assortative mating is when similar people marry and have children. Some people worry about assortative mating in Silicon Valley: highly analytical tech workers marry other highly analytical tech workers. If highly analytical tech workers have more autism risk genes than the general population, assortative mating could put their children at very high risk of autism. How concerned should this make us?

Methods / Sample Characteristics

I used the 2020 Slate Star Codex survey to investigate this question. It had 8,043 respondents selected for being interested in a highly analytical blog about topics like science and economics. The blog is associated with – and draws many of its readers from – the rationalist and effective altruist movements, both highly analytical. More than half of respondents worked in programming, engineering, math, or physics. 79% described themselves as atheist or agnostic. 65% described themselves as more interested in STEM than the humanities; only 15% said the opposite.

According to Kogan et al (2018), about 2.5% of US children are currently diagnosed with autism spectrum disorders. The difference between “autism” and “autism spectrum disorder” is complicated, shifts frequently, and is not very well-known to the public; this piece will treat them interchangeably from here on. There are no surveys of what percent of adults are diagnosed with autism; it is probably lower since most diagnoses happen during childhood and the condition was less appreciated in past decades. These numbers may be affected by parents’ education level and social class; one study shows that children in wealthy neighborhoods were up to twice as likely to get diagnosed as poorer children.

Given that respondents are likely wealthier than average, we might expect a rate of 2.5% – 5%. Instead the rate is noticeably higher than that, consistent with the hypothesis that this sample will be more autistic than average. About 4% of the SSC survey sample had a formal diagnosis of autism, but this rose to 6% when the sample was limited to people below 30, and to 8% below 20. This sample is plausibly about 2-3x more autistic than the US population. Childhood social class was not found to have a significant effect on autism status in this sample.

Results

I tried to get information on how many children respondents have, but I forgot to ask important questions about age until...
Algorithms vs Compute
by johnswentworth — 2020-01-28T17:34:31.795Z

Two scenarios:

I take a vision or language model which was cutting edge in 2000, and run it with a similar amount of compute/data to what's typically used today.
I take a modern vision or language model, calculate how much money it costs to train, estimate the amount of compute I could have bought for that much money in 2000, then train it with that much compute

In both cases, assume that the number of parameters is scaled to available compute as needed (if possible), and we generally adjust the code to reflect scalability requirements (while keeping the algorithm itself the same).

Which of the two would perform better?

CLARIFICATION: my goal here is to compare the relative importance of insights vs compute. "More compute is actually really important" is itself an insight, which is why the modern-algorithm scenario talks about compute cost, rather than amount of compute actually used in 2000. Likewise, for the 2000-algorithm scenario, it's important that the model only leverage insights which were already known in 2000.
Appendix: how a subagent could get powerful
by Stuart_Armstrong — 2020-01-28T15:28:56.434Z

tl;dr: There are ways of ensuring an agent doesn't have a large impact, by giving an impact penalty. One such penalty is "attainable utility", which measures its "power" by how much it could optimise certain reward functions. But in many circumstances, the agent can build a subagent, without triggering the impact penalty, and then that subagent can become very powerful and optimise the world, still without triggering the impact penalty.

Attainable utility definitions

There's been a long history of trying to penalise an AI for having a large impact on the world. To do that, you need an impact measure. I've designed some myself, back in the day, but they only worked in narrow circumstances and required tricks to get anything useful at all out from them.

A more promising general method is attainable utility. The idea is that, as an agent accumulates power in the world, they increase their ability to affect a lot of different things, and could therefore achieve a lot of different goals.

So, if an agent starts off unable to achieve many goals, but suddenly it can achieve a lot, that's a strong hint that its power has greatly increased.

Thus the impact measure is how much difference an agent's action does to its ability to achieve any of a large class of reward functions. Turner et al defined this using the Q-values of various rewards in a set R; for s a state and a an action, the penalty is:

PENALTY(s,a)=∑R∈R|QR(s,a)−QR(s,∅)|.

Here ∅ is the default noop action.

Krakovna et al's basic formula was similar; they defined the distance between two states, st and s′t, as

dAU(st;s′t)=1|R|∑R∈R∣∣VR(st)−VR(s′t)∣∣.

Here VR(s) is the expected value of R, if the agent follows the optimal R-maximising policy from state s onwards.

These measures have problems with delayed effects; putting a vase on a conveyor belt that will smash it in five turns, for example. To combat this, the paper defined an inaction roll-out: seeing what happened do the dAU measure from st and s′t in future turns, if the agent did noop for a specific period. I won't define the formula here, since the example I'm giving is mostly static: if the agent does noop, nothing happens.

The state st was always the agent's current state; the state s′t was either the state the agent would have been in had it never done anything but noop (inaction baseline), or the state the agent would have been in, had its previous act

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Rationalist prepper thread
by avturchin — 2020-01-28T13:42:05.628Z

There are different estimates of the possible severity of the current coronavirus outbreak. One estimation is based on the straight extrapolation of the exponential growth of the infected people number, which doubles every two days. This implies that the whole population of Earth will be ill in March. Another view takes into account that many mild cases are not included in the stat, so lethality is small and probably not everybody will be ill at all. We just don't know yet.

How we should act personally in this situation?

Firstly, we should act in the way, which should be good if everybody will act in the same way. For example, panic evacuation is not good, as if everybody starts running away, this will only increase the dissemination of the virus. Thus the best approach is home self-isolation, lowering of the outside contacts, wearing protecting gear (mask, goggles, gloves), hand cleaning. UV-lamps may help in the disinfection of things.

It is also good to invest in improving ones’ immune system by health food, vitamins, light therapy, as it is our best protection of the virus. Evacuation into a cold county house would weaken the immune system.

It is probably reasonable to collect one month supply of food, water, and other essentials, as the possible pandemic may affect food manufacturing, and also wholesale buying minimise future need to go outside.
An Empistemically Rational Superbowl
by NoSignalNoNoise — 2020-01-28T04:05:40.095Z

American football has some properties that make it useful for practicing making predictions:

A game consists of a sequence of discrete plays with well-defined outcomes

The plays are fast enough that you get quick feedback on each prediction, and there are enough plays in a game that you can make enough predictions to see how well calibrated you are

You go into each prediction with at least some information about what you're predicting

I've created a forecasting practice exercise that you can do while watching a game, either on paper or using this spreadsheet.

For those with no idea how football works, there's a decent basic intro here.

Instructions

Before each down, write down the probability that the current frame will succeed (result in a first down or a touchdown). Once the frame finishes, write the outcome next to your prediction. Repeat.

Optionally, on the special plays (kickoff, punt, field goal attempt, and 2 point attempt), predict whether that play will succeed, and then write down the outcome.

For a field goal or 2 point attempt, success or failure is clear: the team attempting to score either does or doesn't.

For punts and kickoffs, there isn't a sharp dividing line between success and failure, but one definition you can use is that the kick is "successful" if the result is better for the kicking team than a touchback.
AI Alignment 2018-19 Review
by Rohin Shah — 2020-01-28T02:19:52.782Z

Preamble

What this post is

This is a review post of public work in AI alignment over 2019, with some inclusions from 2018. It has this preamble (~700 words), a short version / summary (~1.6k words), and a long version (~8.3k words). It is available as a Google Doc here.

There are many areas of work that are relevant to AI alignment that I have barely touched on, such as interpretability, uncertainty estimation, adversarial examples, and assured autonomy, primarily because I have not been following these fields and wouldn’t be able to write a good summary of what has happened in them. I have also mostly focused on articles that provide some conceptual insight, and excluded or briefly linked to papers that primarily make quantitative improvements on important metrics. While such papers are obviously important (ultimately, our techniques need to work well), there isn’t much to say about them in a yearly review other than that the quantitative metric was improved.

Despite these exclusions, there was still a ton of work to select from, perhaps around ~500 articles, of which over 300 have been linked to in this post. There are many interesting articles that I really enjoyed that get only a sentence of description, in which I ignore many of the points that the article makes. Most have been summarized in the Alignment Newsletter, so if you’d like to learn more about any particular link, but don’t want to read the entire thing, just search for its title in the database.

What you should know about the structure of this post

I am not speaking for myself; by default I am trying to explain what has been said, in a way that the authors of the articles would agree with. Any extra opinion that I add will be in italics.

As a post, this is meant to be read sequentially, but the underlying structure is a graph (nodes are posts, edges connect posts that are very related). I arranged it in a sequence that highlights the most salient-to-me connections. This means that the order in which I present subtopics is very much not a reflection of what I think is most important in AI safety: in my presentation order, I focused on edges (connections) rather than nodes (subtopics).

Other minor details:

Any links from earlier than 2018 will have their year of publication right after the link (except for articles that were reposted as part of Alignment Forum sequences).
I ty

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