trentmkelly/LessWrong-43k huggingface dataset:
Regularization Causes Modularity Causes Generalization
by dkirmani — 2022-01-01T23:34:37.316Z

Epistemic Status: Exploratory

Things That Cause Modularity In Neural Networks

Modularity is when a neural network can be easily split into several modules: groups of neurons that connect strongly with each other, but have weaker connections to outside neurons. What, empirically, makes a network become modular? Several things:

Filan et al.[1]:

Training a model with dropout

Weight pruning

L1/L2 regularization

Kashtan & Alon: Switching between one objective function and a different (but related[2]) objective function every 20 generations

Clune et al.: Adding penalties for connections between neurons

Modularity Improves Generalization

What good is modularity? Both Clune et al. and Kashtan & Alon agree: more modular networks are more adaptable. They make much more rapid progress towards their goals than their non-modular counterparts do:

Modular neural networks, being more adaptable, make faster progress towards their own goals. Not only that, but their adaptability allows them to rapidly advance on related[2:1] goals as well; if their objective function was to suddenly switch to a related goal, they would adapt to it much quicker than their non-modular counterparts.

In fact, modular neural networks are so damn adaptable that they do better on related goals despite never training on them. That's what generalization is: the ability to perform well at tasks with little to no previous exposure to them. That's why we use L1/L2 regularization, dropout, and other similar tricks to make our models generalize from their training data to their validation data. These tricks work because they increase modularity, which, in turn, makes our models better at generalizing to new data.

How Dropout Causes Modularity

What's true for the group is also true for the individual. It's simple: overspecialize, and you breed in weakness. It's slow death.

—Major Kusanagi, Ghost in the Shell

Training with dropout is when you train a neural network, but every neuron has a chance of 'dropping out': outputting zero, regardless of its input. In practice, making 20-50% of your model's neurons spontaneously fail during training usually makes it much better at generalizing to previously unseen data.

Ant colonies have dropout. Ants die all the time; they die to war, to famine, and to kids with magnifying glasses. In response, anthills have a high bus factor. Not only do anthills have sp...
Self-Organised Neural Networks:
A simple, natural and efficient way to intelligence
by D𝜋 — 2022-01-01T23:24:56.856Z

The piece of code bellow is indisputable, reproducible, proof of what we are going to say in this post.

It implements a basic spiking neural network, with a simple learning method that introduces a new paradigm.

The accuracy reaches

state of the art

(98.9%) on PI-MNIST with 750,000 low precision (binarisable) connections (>98.6% with 80,000 connections), one layer, no bias, no back-propagation and only additions. It is yet to be optimised. It works online, and is backed by existing mathematical theorems. It also relates to the real cortical structure and personal experience; and even popular wisdom.

So it cannot be just ignored or dismissed. It requires proper falsification, or further analysis and extension.

I will describe the system and its mechanisms, analyse it, and share the vision, starting from the beginning, in plain language, so everybody can understand, and, yet, it will be essentially complete.

I am setting up a website where I will add more information and code depending on expressed interest, if any. The code at the bottom is there for completeness, immediate reference and, required, formal proof.

The system

A Neuron, in the brain, is connected to multiple other neurons from dendrites. It receives signals carried by nerves. Each connection has a strength (weight) that varies over time. There is positive and negative connections. When a neuron receives a signal from a connection, it ‘adds’ or ‘subtracts’ (chemically) the weight associated with the connection, inside its body, to a ‘total’. If it reaches the limit of what it can hold (its threshold), it spikes: it sends a signal through its axon, its only outgoing nerve, for other neurons to receive. In doing so empties its content. And so on… In our system, there is neurons that operate the same way.

When a neuron spikes, the connections that have contributed to it are reinforced. It is

Hebb’s rule

. In this system, we do the same.

Connection are not permanent. They are created on the fly, and their weight changes over time. Decay reduces weights over time, as well. When the weight goes down too much, the connection is cut. In this system, we do the same.

In the brain, neurons group together, locally, in what is called columns. They tend to spike, or not, in a fairly synchronised manner inside a group. It is a

matter of scientific debate

how or what for, but it is. In this system, we set up neurons in g...
Why don't we just, like, try and build safe AGI?
by Sun — 2022-01-01T23:24:34.114Z

Why not? Currently, the AGI efforts in the world seem to be like, openAI and deepmind. None of them have as their primary aim of building safe AGI.

Why don't we just get a few billion dollars together as a community, put everyone really smart somewhere, and just, go for it? Before openAI or Deepmind does. Sure, lots to research happening in AI safety, but it doesn't seem like any of this research will find it's way to openAI or Deepmind, anyway. Seems unlikely with incentives as misaligned as they are.
So, creating an organization with its primary aim to build safe AGI, and no other aims, at all, fully and only funded by EA money seems at least worth the attempt given how much money this community should have soon.
We Choose To Align AI
by johnswentworth — 2022-01-01T20:06:23.307Z

Epistemic status: poetry

"We choose to go to the moon! We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard. Because that goal will serve to organize the best of our skills and energies. Because that challenge is one we are willing to accept, one we are unwilling to postpone, and one we intend to win." -

John F Kennedy

WE CHOOSE TO ALIGN AI IN THIS DECADE AND DO THE OTHER THINGS

JFK gave his “We choose to go to the moon!” speech in 1962. And when he said “in this decade”, he did not mean that we’d go to the moon before 1972. He meant we’d go to the moon before 1970.

Happy 2022! When I say we choose to align AI in this decade, I don’t mean before 2032. I mean before 2030. Maybe sooner if things go well. Do I think that’s actually doable?

Yes

. Also fuck you.

… and some other things! As long as we’re shooting for the metaphorical moon, might as well throw

aging

in the mix too. That seems doable by 2030.

NOT BECAUSE THEY ARE EASY, BUT BECAUSE THEY ARE HARD

Effective altruists

talk a lot about “importance, neglectedness, and tractability”. The more important, neglected, and tractable a problem is, the more we should expect a high impact per unit of effort invested in it. The alignment problem scores through the roof on importance, and is still relatively neglected, but tractability is… um… not.

I’m not really an EA, at heart. When there’s low hanging fruit, I might pick it quickly and move on, or these days I might point it out to someone else and move on. Point is, the low hanging fruit is not what I’m here for. I’m here for the challenge. I study alignment and agency and the other things not because they are easy, but because they are hard.

The more EAs I meet, the more I realize that wanting the challenge is a load-bearing pillar of sanity when working on alignment.

When people first seriously think about alignment, a majority freak out. Existential threats are terrifying. And when people first seriously look at their own capabilities, or the capabilities of the world, to deal with the problem, a majority despair. This is not one of those things where someone says “terrible things will happen, but we have a solution ready to go, all we need is your help!”. Terrible things will happen, we don’t have a solution ready to go, and even figuring out how to help is a nontrivial problem. When people really come...
What are sane reasons that Covid data is treated as reliable?
by Valentine — 2022-01-01T18:47:38.919Z

I feel like this is one of those questions that's somehow too basic to ask. Or maybe too political. Like wondering this stuff implies I'm siding with something.

I'd really like to just set all the politics aside, simply name my ignorance, and hear some truthful answers. Because seeing this not even discussed is part of what's giving me a sense of "Fuck it, no one actually knows anything, everyone is just making random shit up that fits their social identities."

People keep talking confidently about incidence rates for different Covid variants, about their death rates and likelihood of hospitalization, how we have such clear data on vaccine efficacy and safety, etc.

But all the info streams I see look extremely dubious. I don't just mean one could doubt them. I mean, I've witnessed powerful evidence of blatant bias, and aside from brief mentions of the existence of those biases no one seems to care.

I'll give some examples below that inspire my confusion. I want to emphasize that I'm honestly asking here. Less Wrong is one of the few places (the only place?) where I feel like I can seek epistemic cleaning and clarity here.

So, example confusion sources:

I've personally known many people who have had serious medical problems that sure looked clearly like vaccine reactions. On par with "Well now I can't get out of bed and can't think anymore" or "Oh shit, heart attack" kinds of reactions. But all these people I've known who tried to report their reactions were told "No, your reaction can't have been due to the vaccine, because the vaccine is safe and effective." I've heard lots of similar reports. Because this is about rejecting data collection, I don't see how anyone could possibly know how common/rare this is.
I've never known anyone who has been tested for a variant of any kind. I don't know anything about how variant tests look different from a generic Covid test. So where are these numbers for variant spread coming from? Maybe hospitals do have special genetic tests and reliably do those? But then isn't there going to be a pretty strong bias based on the fact that these are only for people who are getting hospitalized?
There was, best as I could tell, active (if unintentional) data destruction quite early on in the pandemic. Maybe it's still going on? Hospitals had financial incentives to find reasons why people who died had died of Covid. Lots of bodies got crema

...
How does one learn (music) more efficiently?
by Nicole Dieker — 2022-01-01T17:55:08.885Z

Crossposted (with some audience-based edits) from my Substack.

The best way to share this is by showing it.

Very interested in your thoughts, especially from people who have experience with Ericsson/Pool deliberate practice either from a memory-precision perspective (strings of numbers, Shakespearean soliloquies, chess openings, etc.) or from a physical-precision perspective (guitar, piano, free throws, etc.).

If you want me to share/show more, I'll post another video tomorrow...
Working through D&D.Sci, problem 2 (preface)
by Pablo Repetto — 2022-01-01T17:40:45.862Z
Link: https://pabloernesto.github.io/2022/01/01/dndsci-2-0.html

This is part three of my series on working though D&D.Sci. This post is about choice of tools, installation, and setup.
Can each event in the world be attributed conditions under which it occurs?
by Maciej Jałocha — 2022-01-01T16:02:22.463Z

Can each event in the world be

attributed conditions under which it occurs (actually under coditions there's possibility of occuring)

and

attributed this possibility/probability that this event will happen under those conditions?

When event is caused by another it's for sure ok. I think also that no matter how random and "strange" (e.g. quantum physics) the event looked like we'd still be able to assign this two things. For example even though there were completely random and "strange" events like some truly self-causing (just) flashes in part of galaxy it would still be possible to determine conditions (e.g. location range, temperature range, maybe frequency) under which these occur and attribute probability that this event will happen under those conditions (e.g. 85%).

Can't image any event that could not be put into those criteria since the borderline case would be 0 conditions and complete unpredictability (but above 0%).

What do you think?

(For me it sounds obvious and have tried to refute it and had read about it but I have still felt I needed some sort of social proof from LW community. Strange feeling.)
Careful With Trailing Averages
by jefftk — 2022-01-01T16:00:04.699Z

With covid, it is common for people to look at seven day trailing
averages. For example, the

MA
Covid Dashboard has "the seven day average of percent positivity
is 18.42%". While this isn't exactly wrong, it's not a good fit for a
rapidly rising number. Here's how it's usually presented:

This gives the impression that 18.4% is the number for 12/30, but it's
clearer to think of it as a number for 12/27. That way it represents
our best guess of how things were on the 27th by looking three days
into the future and three days into the past to average out
fluctuations:

On the other hand, these variations are clearly not noise; it's a
weekly cycle. People are much more likely to test positive on the
weekend than during the week:

Monday
89%

Tuesday
85%

Wednesday
96%

Thursday
93%

Friday
113%

Saturday
172%

Sunday
145%

(Average ratio by day of week comparing daily test positivity to the
seven day average of test positivity centered on that day.)

This makes sense: weekend testing will include a larger proportion of
people who are getting tested because they are sick.

Instead of trying to average this pattern away, we can use it, scaling
each day by the inverse of its average departure from the 7d average:

This gives us a metric that is as responsive as the daily numbers
with less of the day-by-day variation from from measuring different
populations on different days. It's not perfect, you can see it
getting things wrong by treating Thanksgiving and Christmas like
normal days, but it's pretty good.

When case numbers are changing quickly, this responsiveness is
helpful. Using the most recent seven days we'd get a test positivity
of 18%, but the most
recent weekday-adjusted daily number is 23% (+25%).

Comment via: facebook
Genetics: It sometimes skips a generation is a terrible explanation!
by Jan Christian Refsgaard — 2022-01-01T15:12:03.277Z

Question: "How come your son Jimmie isn't colorblind when his father is"

Wrong answer: "It some times skips a generations, maybe his kids will inherent it"

Right Answer: "it's on the X chromosome, he got his fathers Y chromosome, so his kids will not inherent colorblindness from his side of the family!"

Question: "How come your daughter Susie isn't colorblind when her farther is"

Confused answer: "It some times skips a generations, maybe her kids will inherent it"

Right Answer: "The confused answer is less wrong than with the son, it's on the X chromosome, she got her fathers X chromosome, so half heir sons will be color blind"

Simple genetics, your Mother has XX your father has XY, let's use little x to denote colorblindness, you get a random chromosome from each parent.

Colorblind father -> sons have normal vision, daughters are carriers:

xY + XX - > 50% XY + 50% xX

Colorblind mother -> sons are colorblind, daughters are carriers

XY + xx - > 50% xY + 50% xX

Carrier mother -> half sons are colorblind, half of daughters are carriers

XY + xX - > 25% xY + 25% XY + 25% xX + 25% XX

Finally if colorblindness runs in both families:

xY + xX -> 25% xY + 25% XY + 25% xx + 25% xX

both parents colorblind -> all children colorblind

Question: "Have you ever wondered, how does the xX females know not to use the color blind gene?"

Answer: "They do not, half the relevant cells in their eyes will have a bad photo receptor... but the brain is pretty smart, it quickly learns to boost the signal from the other half of the cells"

Warning: The above is strictly only true for X-linked dominant diseases such as colorblindness, a similar approach can be used to elucidate the inheritance pattern of Classical Mendelian traits, but does not work for complex trait such as height which is influenced by multiple genes.
$1000 USD prize - Circular Dependency of Counterfactuals
by Chris_Leong — 2022-01-01T09:43:25.748Z

Congrats to the winner TailCalled with their post Some thoughts on "The Nature of Counterfactuals". See the winner announcement post.

I've previously argued that the concept of counterfactuals can only be understood from within the counterfactual perspective.

I will be awarding a $1000 prize for the best post that engages with the idea that counterfactuals may be circular in this sense. The winning entry may be one of the following (these categories aren't intended to be exclusive):

a) A post that attempts to draw out the consequences of this principle for decision theory

b) A post that attempts to evaluate the arguments for and against adopting the principle that counterfactuals only make sense from within the counterfactual perspective

c) A review of relevant literature in philosophy or decision theory

d) A post that restates already existing ideas in a clearer or more accessible manner (I don't think this topic has been explored much on LW, but it may have in explored in the literature on decision theory or philosophy)

Feel free to ask me for clarification about what would be on or off-topic. Probably the main thing I'd like to see is substantial engagement with this principle. The bounty is for posts that engage with the notion that counterfactuals might only make sense from within a counterfactual perspective. I have written on this topic, but the competition isn't limited to posts that engage with my views on this topic. It's perfectly fine to engage with other arguments for this proposition if, for example, you find someone arguing in favour of this in the philosophical/mathematical literature or Less Wrong.
If someone submits a high-quality post that only touches on this issue tangentially, but someone else submits an only okayish post that tries to deeply engage with this issue, then I would likely award it to the latter as I'm trying to incentivise more engagement with this issue rather than just high-quality posts generally. If the bounty is awarded to an unexpected submission, I expect this to be the main cause.

I will be awarding an additional $100 for the best short-form post on this topic. This may be a LW Shortform post, a public Facebook post, a Twitter thread, ect (I'm not going to include Discord/Slack messages as they aren't accessible).

Why do I believe in this principle?
Roughly, my reasons are as follows:

Rejecting David Lewis' Counterfactual Real

...
Mailing Binax kits
by NormanPerlmutter — 2022-01-01T06:17:36.444Z

Is it safe with regard to test efficacy to mail Binax rapid covid test kits (i.e. to a friend) in the winter when the weather is below freezing? The official instructions say to store at above 2 degrees C, but they are on sale on Amazon. Going to visit a friend in another city, being able to mail them a test for their use would be easier logistically than having them pick one up. I've tried googling this and found nothing, maybe somebody here knows.
This Year I Tried To Teach Myself Math. How Did It Go?
by Borasko — 2021-12-31T17:55:31.384Z

Cross-posted from my substack

Summary:

This goes over a year of self teaching, the topics of math I was able to learn, how I view the best way to learn math and what my general view of math as a whole is with my limited current knowledge on the subject. At the end is a guide I made for anybody else that wishes to teach themselves math, no matter the skill level.

The Wild World of Online Learning:

When you first start out to do any project it's usually best to have instructions and a goal of what a ‘finished’ state looks like in mind. Most consumer bought appliances that require assembly come with a very hand holdy instruction sheet and all the parts required, the same can not be said for trying to learn an area of knowledge. I would argue a good chuck, >60% of the year, was just spent on figuring out what I needed to learn and where the next logical step from that was.

I will fully admit that GPT-3 and other AI advances got me interested in joining the industry. I will also fully admit that I immediately jumped into Coursera’s Intro to Machine Learning and crashed hard when I realized I wasn't getting anything out of it because I barely knew high school algebra and couldn't understand what I was looking at, because I wasn't interested in anything math related until now. Even reading about what partial derivatives were, there was too much scaffolding of previous calculus math needed to really set me up for understanding. The kicker was I could still pass the coding assignments and it made my “grade” look good, but that’s mostly because of how the course was set up. I complained about online learning because of it here, but I’m going to be a bit more dispassionate in this review.

This cycle repeated in a few more online courses. I would spend time going through the course, get by doing the assignments, start to get frustrated because I didn’t know why what I was learning was going to be important down the road, then eventually drop the course and start looking for another one that I felt could actually help me. It was about half way through the year that I decided to drop online learning due to the resentment of feeling like I wasted my time on the courses. The problem was I could do all the practice problems of the online courses fine, but I still felt like I had only islands of unconnected knowledge and I hadn't learned anything actually useful. It ...