trentmkelly/LessWrong-43k huggingface dataset:
Seven Years of Spaced Repetition Software in the Classroom
by tanagrabeast — 2021-03-04T02:42:01.475Z

Description

This is a reflective essay and report on my experiences using Spaced Repetition Software (SRS) in an American high school classroom. It follows my 2015 and 2016 posts on the same topic.

Because I value concise summaries in non-fiction, I provide one immediately below. However, I also believe in the power of narrative, in carefully unfolding a story so as to maximize reader engagement and impact. As I have applied such narrative considerations in writing this post, I consider the following summary to be a spoiler.

I’ll let you decide what to do with that information.

Summary (spoilers)

My earlier push for classroom SRS solutions was driven by a belief I came to see as fallacious: that forgetting is the undoing of learning. This epistemic shift drove me to abandon designs for a custom app that would have integrated whole-class and individual SRS functions.

While I still see value in classroom use of Spaced Repetition Software, especially in basic language acquisition, I have greatly reduced its use in my own classes.

In my third year of experiments (2016-17), I used a windfall of classroom computers to give students supervised time to independently study using an SRS app with individual profiles. I found longer-term average performance to be slightly worse than under the whole-class group study model, though students of high intelligence and motivation saw slight improvements.

Intro and response to Piotr Woźniak

I have recently received a number of requests to revisit the topic of classroom SRS after years of silence on the subject. Understandably, the term “postmortem” has come up more than once. Did I hit a dead end? Do I still use it?

Also, I was informed that SRS founding father Piotr Woźniak recently added a page to his SuperMemo wiki in which he quoted me at length and claimed that SRS doesn’t belong in the classroom.

Well, I don’t have much in the way of rebuttal, because Woźniak’s main goal with the page seems to be to use my experience as ammunition against the perpetuation of school-as-we-know-it, which seems like a worthy crusade. He introduces my earlier classroom SRS posts by saying, “This teacher could write the same articles with the same conclusions. Only the terminology would differ.” I’ll take that as high praise.

If I were to quibble, it would be with the part shortly after this, where he says:

The entire analysis is made with

...
I'm still mystified by the Born rule
by So8res — 2021-03-04T02:35:32.301Z

(This post was originally intended as a comment on Adele's question, but ballooned to the point where it seems worthy of a top-level post. Note that I'm not trying to answer Adele's (specific fairly-technical) question here. I consider it to be an interesting one, and I have some guesses, but here I'm comentating on how some arguments mentioned within the question relate to the mysteries swirling around the Born rule.)

(Disclaimer: I wrote this post as a kind of intellectual recreation. I may not have the time and enthusiasm to engage with the comments. If you point to a gaping error in my post, I may not reply or fix it. If I think there's a gaping error in your comment, I may not point it out. You have been warned.)

My current take is that the "problem with the Born rule" is actually a handful of different questions. I've listed some below, including some info about my current status wrt each.

Q1. What hypothesis is QM?

In, eg, the theory of Solomonoff induction, a "hypothesis" is some method for generating a stream of sensory data, interpreted as a prediction of what we'll see. Suppose you know for a fact that reality is some particular state vector in some Hilbert space. How do you get out a stream of sensory data? It's easy enough to get a single sensory datum — sample a classical state according to the Born probabilities, sample some coordinates, pretend that there's an eyeball at those coordinates, record what it sees. But once we've done that, how do we get our next sense datum?

Or in other words, how do we "condition" a quantum state on our past observations, so that we can sample repeatedly to generate a sequence of observations suitable for linking our theories of induction with our theories of physics?

To state the obvious, a sensory stream generated by just re-sampling predicts that you're constantly teleporting through the multiverse, and a sensory stream generated by putting a delta spike on the last state you sampled and then evolving that forward for a tick will... not yield good predictions (roughly, it will randomize all momenta).

Current status: I assert that additional machinery is required to turn QM into a hypothesis in the induction-compatible sense — ie, I'd say "the Born rule is not complete (as a rule for generating a hypothesis from a quantum state)". My guess is that the missing machinery involves something roughly like sampling cl...
Limits of Giving
by jefftk — 2021-03-04T02:20:02.903Z

A friend recently asked what my goal was in giving: was there some
amount of donations that would be enough? If someone give me a large
enough amount of money, which I then donated, would I be free of
further altruistic obligations?

These questions feel to me like they come from a very different
perspective, so I want to try and explain how I think about it. If I
continue on my current path, perhaps over the
next 40 years I might manage to donate $10M. There's a sense, then,
in which I have a target of $10M. If through some unrealistically
good fortune my 0.34% of Wave stock options turned into $50M, however,
I wouldn't donate $10M and then devote myself to leisure.

The level of need in the world is enormous, far bigger than my
personal efforts can address. The poorest billion people need a
marginal dollar far more than I do; no one should be dying of malaria;
our society's ability to handle a pandemic is terrifyingly bad; we are
putting much less effort than we should be into making sure humanity
doesn't go extinct.

Now I'm not going to sell all my possessions and live as cheaply as
possible, but I am going to be thoughtful about balancing
costs to myself against benefits to others and making good altruistic
tradeoffs. The more money I have, the larger a portion I can give
while continuing to spend money on myself in ways that make me happy.

Considered this way, responding to receiving $50M by decreasing the
percentage I gave would be exactly backwards.

Comment via: facebook
How does bee learning compare with machine learning?
by leni — 2021-03-04T01:59:06.067Z

This is a write-up of work I did as an Open Philanthropy intern. However, the conclusions don't necessarily reflect Open Phil's institutional view.

Abstract

This post investigates the biological anchor framework for thinking about AI timelines, as espoused by Ajeya Cotra in her draft report. The basic claim of this framework is that we should base our estimates of the compute required to run a transformative model on our estimates of the compute used by the human brain (although, of course, defining what this means is complicated). This line of argument also implies that current machine learning models, some of which use amounts of compute comparable to that of bee brains, should have similar task performance as bees.

In this post, I compare the performance and compute usage of both bees and machine learning models at few-shot image classification tasks. I conclude that the evidence broadly supports the biological anchor framework, and I update slightly towards the hypothesis that the compute usage of a transformative model is lower than that of the human brain.

The full post is viewable in a Google Drive folder here.

Introduction

Ajeya Cotra wrote a

draft report on AI timelines

(Cotra, 2020) in which she estimates when

transformative artificial intelligence

might be developed. To do so, she compares the size of a transformative model (defined as the number of

FLOP/s

required to run it) with the computational power of the human brain, as estimated in

this Open Phil report

(Carlsmith, 2020)[1]. She argues that a transformative model would use roughly similar amounts of compute as the human brain. As evidence for this, she claims that computer vision models are about as capable as bees in visual tasks, while using a similar amount of compute.[2]

In this post, I (Eleni Shor) investigate this claim. To do so, I focus on the performance of bees at

few-shot image classification

, one of the most difficult tasks that bees are able to perform. I find that both their task performance and their compute usage are roughly comparable to current machine learning models. This is broadly consistent with Cotra’s claim that transformative models would have compute requirements similar to that of the human brain, as neither bees nor machine learning models are clearly superior at this task.

On the one hand, bees are more sample-efficient than machine learning models, requiring or...
Some recent interviews with AI/math luminaries.
by fowlertm — 2021-03-04T01:26:25.046Z

I've recently started a podcast with renowned futurist Thomas Frey, and when possible I've been scheduling luminaries in AI Safety, AGI, and mathematics.

Most of the content won't be new to regular LWians, but I thought it couldn't hurt to share a few links. Like and subscribe for future interviews, in the months ahead we've got leading experts in economics, one of the founders of the Santa Fe Institute, the brain behind one of the most popular social networks, and a bunch more.

Here is our interview with Dr. Roman Yampolsiy (spoiler: he admits to being Satoshi Nakamoto). Before this interview I hadn't heard of 'intellectology', but it's his proposal for a new field that studies the structure and limitations of different cognitive architectures:

We spoke with the director of the Icelandic Institute for Intelligent Machines about his proposed design of a fully generally intelligent system. I don't know if he's cracked that nut, but he's definitely given it deep, serious thought:

My good friend Erik Istre is an expert in nonclassical foundations for mathematics. In our interview with him we really get into the weeds on paraconsistent logic and what it does/doesn't mean, plus its potential applications to AI Safety and metaphysics:

Finally, David Jilk is well-known in AI Safety circles, and in this interview we talk about different approaches to the topic and whether there's any connection to quantum computing:
How To Think About Overparameterized Models
by johnswentworth — 2021-03-03T22:29:13.126Z

So, you’ve heard that modern neural networks have vastly more parameters than they need to perfectly fit all of the data. They’re operating way out in the regime where, traditionally, we would have expected drastic overfit, yet they seem to basically work. Clearly, our stats-101 mental models no longer apply here. What’s going on, and how should we picture it?

Maybe you’ve heard about some papers on the topic, but didn’t look into it in much depth, and you still don’t really have an intuition for what’s going on. This post is for you. We’ll go over my current mental models for what’s-going-on in overparameterized models (i.e. modern neural nets).

Disclaimer: I am much more an expert in probability (and applied math more generally) than in deep learning specifically. If there are mistakes in here, hopefully someone will bring it up in the comments.

Assumed background knowledge: multi-dimensional Taylor expansions, linear algebra.

Ridges, Not Peaks

First things first: when optimizing ML models, we usually have some objective function where perfectly predicting every point in the training set yields the best possible score. In overparameterized models, we have enough parameters that training indeed converges to zero error, i.e. all data points in the training set are matched perfectly.

Let’s pick one particular prediction setup to think about, so we can stick some equations on this. We have a bunch of (x,y) data points, and we want to predict y given x. Our ML model has some parameters θ, and its prediction on a point x(n) is f(x(n),θ). In order to perfectly predict every data point in the training set, θ must satisfy the equations

∀n:y(n)=f(x(n),θ)

Assuming y(n) is one-dimensional (i.e. just a number), and we have N data points, this gives us N equations. If θ is k-dimensional, then we have N equations with k variables. If the number of variables is much larger than the number of equations (i.e. k>>N, parameter-dimension much greater than number of data points), then this system of equations will typically have many solutions.

In fact, assuming there are any solutions at all, we can prove there are infinitely many - an entire high-dimensional surface of solutions in θ-space. Proof: let θ∗ be a solution. If we make a small ...
Thoughts On Computronium
by Darklight — 2021-03-03T21:52:35.496Z

While it's widely accepted common knowledge that computers are considerably faster and more powerful than the human brain, it's arguable that evolution didn't optimize the human brain for raw speed, but rather, energy efficiency. The reason why this is important is that in the limit, the theoretical computronium needs to be not only powerful, but an efficient use of resources. In the limit, assuming reasonably that entropy cannot be reversed, then energy is our main practical confining factor for a universe filled with computronium or particularly utilitronium.

Furthermore, if natural selection has already provided a sufficiently close to optimal solution, it may make sense for Omega to fill the universe with human brain matter, perhaps even human beings enjoying lives worth living, as an optimal solution, rather than simply taking humanity's atoms and reconfiguring them into some other as yet unknown form of computronium. This idea of the human form already being optimal, has some obviously desirable characteristics for humans looking to imagine possible futures where the universe is filled with trillions upon trillions of happy humans.

So, practically speaking, how realistic is it to assume that the human brain is anywhere close to optimal, given that the theoretical limits of physics seem to imply that there is considerable leeway for a high upperbound on the efficiency of computronium? As an interesting exercise, let's look at real world supercomputers.

As of this writing the world's fastest supercomputer is the Fugaku, which achieves an impressive 1000 PetaFlops in single precision mode. In comparison, the human brain is estimated to be 20 PetaFlops. However, in terms of energy efficiency, the human brain achieves that with 20 watts of power, for an effective 1 PetaFlops/watt. On the other hand, Fugaku is listed as having an efficiency of about 0.000015 PetaFlops/watt, or six orders of magnitude less.

Due to mass-energy equivalence, the fact is that even if we close the gap on energy efficiency in terms of wattage, another possibly dominant term in the equation is the amount of energy in the mass of the matter that makes up the computronium. Here, the distance is similar. The human brain takes up about 1.5 kg of matter, while Fugaku is 700 tons or over 600,000 kg. The human brain thus has an effect...
Texas Freeze Retrospective: meetup notes
by jchan — 2021-03-03T14:48:18.965Z

This article is a writeup of the conversation at a meetup hosted by Austin Less Wrong on Saturday, February 27, 2021. The topic was the winter weather and infrastructure crisis that took place the previous week. There were a total of 13 participants, including 8 people who were in Texas at the time and 5 who weren't.

I was the note-taker but I was not in Texas myself, so replies to any comments will probably come from people other than me. Below the section break, "I" refers to whoever was speaking at the time. Thanks to everyone who contributed and helped compile these notes.

Disclaimer: I took pains to make it clear before, during, and after the meetup that I was taking notes for posting on LessWrong later. I do not endorse posting meetup write-ups without the knowledge and consent of those present!

The 2021 Texas Freeze

Personal anecdotes

I lost power Monday through Thursday. The inside temperature dropped from 68°F to 47°F on Monday alone; over the course of the week the thermostat hit a minimum of 40°F. (Either the thermostat couldn't read any lower or the kitchen was even colder, since my olive oil solidified, which happens at 37°F). My breath was visible indoors. I had to keep my phone off most of the time, so most of the day was spent reading books under several blankets. I had a carbon monoxide scare on Tuesday after using the fireplace. I started boiling water on Wednesday, when the order was declared in some areas of Austin but not yet mine, because it seemed likely the order would soon be extended city-wide, which indeed occurred a day later. Even after getting power back, I still couldn't get groceries—stores had long lines, and H-E-B was closed after 5pm. Gas stations were out-of-order.

I lost power Monday through Friday—there was some damage to a local power line. I teamed up with my neighbors. We had a fire going out back that people could warm themselves and cook things at. We didn't have much in the way of preparation supplies, but we did have candles and water bottles. We had advance notice that we might lose water, so we filled up the tub and every container we could find. (We didn't lose water, but we got the boil-water notice.) A tree branch fell and blocked our alleyway; we worked together to remove it, yielding a bunch of firewood as a side benefit. The house was well-insulated (≈50°F), but some of our warm clothing got wet, so it would...
Open & Welcome Thread – March 2021
by Yoav Ravid — 2021-03-03T12:27:10.378Z

If it’s worth saying, but not worth its own post, here's a place to put it.

If you are new to LessWrong, here's the place to introduce yourself. Personal stories, anecdotes, or just general comments on how you found us and what you hope to get from the site and community are invited. This is also the place to discuss feature requests and other ideas you have for the site, if you don't want to write a full top-level post.

If you want to explore the community more, I recommend reading the Library, checking recent Curated posts, seeing if there are any meetups in your area, and checking out the Getting Started section of the LessWrong FAQ. If you want to orient to the content on the site, you can also check out the new Concepts section.

The Open Thread tag is here. The Open Thread sequence is here.
What other peptide vaccines might it be useful to make?
by Bird Concept — 2021-03-03T06:25:40.130Z

Related: Making Vaccine

In my limited understanding of the workings of the open-source radvac vaccine, it is surprisingly basic. In particular, if the peptide design works, then simply ordering and snorting it is sufficient. Most of the rest of the process, which is about creating nanoparticles to which the peptides can attach, just serves to boost effectiveness.

When I say this is "basic", I still want to commend the radvac team and johnswentworth for the combined ingenuity and agency required for making it happen. Sometimes it takes true skill to find a simple solution.

Especially when most people aren't even looking.

I spent most of last year not even considering the hypothesis that it might be possible to make safe, cheap and effective vaccines at home. For a year I allowed my life to be governed by a constraint that a more competent version of me could have relaxed in a few months.

It feels a bit like going to Hogwarts and seeing someone do transfiguration for the first time. The thoughts that should be racing through my mind are: how do I learn such magic? And: if I could master such magic... what else could I use it for?

I'm curious for others' input as I think through that.

What else could the general method of peptides, administered intranasally, be used for?

Other diseases, things other than vaccinations, or even enhancement rather than prevention?

My knowledge of this domain is very limited, and these questions might not have interesting answers. But they certainly seems worth at least posing, and I am curious about the result.
Curiosity about Aligning Values
by esweet — 2021-03-03T00:22:07.883Z

Hi there, I've been thinking a lot about AI Alignment and values, the latter for longer than the former, admittedly. I'm in graduate school and study values through ethics. I would love to start a conversation about a thought that shot through my mind just last night. In thinking about values, we often focus on the principles, the concepts such as "good" and "bad" -- most simply, the nouns and adjectives. These are challenging to bridge consensus even in the same language, let alone across cultural, linguistic, and geographic boundaries. In my past experience as an English teacher, conveying verbs was always easier than trying to explain things like integrity.

Here's my question: what if instead of fixed concepts and rules, AI alignment focused on actions as the underlying reward function? In other words, might programming AI to focus on the means rather than the ends facilitate an environment in which humans are freer to act and reach their own ends, prioritizing activated potential over predetermined outcome? Can the action, instead of the outcome, become the parameter, rendering AI a facilitator rather than a determiner?

There's a lot more to these questions, with details and explanations that I would be happy to dive into with anyone interested in discussing further (I didn't think it appropriate to make my first post too lengthy). Either way, I'm happy to have found this group and look forward to connecting with likeminded and unlikeminded folks. Thank you for reading! ~Elisabeth