trentmkelly/LessWrong-43k huggingface dataset:
Lightweight minimal speech recognition?
by jefftk — 2023-07-12T12:00:01.191Z

I currently control my

rhythm stage setup
with a USB keyboard. This works, except that my hands are often busy
playing instruments when I want to change something. I already have a
microphone in front of my mouth running to a computer, which I use for
my whistle-controlled bass synthesizer: could I use speech
recognition?

Another way to look at this is that a lot of my exploration here has
been finding some way to play bass and mandolin at the same time:
whistle bass, bass pedals, bass and drum
pedals. With speech recognition I could call chords to the computer!

I'm currently running on a Raspberry
Pi 3B. I tried OpenAI's Whisper and then
the C++ port
(which should
work on the 4B), and then the much older software Julius but my Pi 3B
couldn't handle real time with any of these. Is there some existing
software that would be a good fit for this? People were running
speech recognition back when this computer would have been top of the
line.

I'm also considering giving up on the idea of using speech: all I
really need is some sound I can make consistently enough that the
computer can recognize. I'm already running a system that can mostly
decode whistling, so maybe I should figure out how to phrase my
commands as simple whistle patterns? I'm a bit nervous about doing
this while playing, though, since unless I choose patterns that can
sound musical in any key it's going to be pretty hard to combine with
playing something else. Maybe alternating low and high notes, but I
can choose which notes in response to the key?

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Aging and the geroscience hypothesis
by DirectedEvolution — 2023-07-12T07:16:04.516Z

Cautionary introduction

I am a biomedical engineer with an interest in the science of aging. The Handbook of the Biology of Aging, 9th ed, is a great 400-page introductory resource, looking at aging on every level from evolutionary and conceptual, to public health, to specific molecular and organ systems, to genetics, to immortal organisms.

My goal is to teach readers key concepts and facts about aging research in short posts with accessible language. I follow the substance of the handbook chapters, though I sometimes reorganize them. I'm also presenting the chapters out of order, because I think it creates a better narrative. I encourage readers to take the facts and concepts tentatively - they are just pointers to a rich and sometimes controversial underpinning literature. My hope is that some readers will go on to read the handbook and explore the literature it cites.

Chapters will be presented out of order in order to improve the overall narrative flow. This post is for chapter 4:

Hornsby, P. J. (2021). The nature of aging and the geroscience hypothesis. In Handbook of the Biology of Aging (pp. 69-76). Academic Press.

Next: Compression of morbidity (available 7/12/2023)

The nature of aging and the geroscience hypothesis

This chapter is about making sense of a seemingly paradoxical thesis:

The fact that aging and injury are inevitable should encourage us about the possibility of advanced medicine that makes frailty and death preventable.

Your body is made of physical stuff - DNA, proteins, sugars, fats, cells, tissues, and organs - and physical stuff, left alone, inevitably breaks down and becomes disordered. The longer you live, the more chances you take with car crashes, disease, and other external causes of death. Aging is entropy - the inevitable process by which the laws of physics guarantee that a system will become disordered over time.

Yet the entropy of aging and injury is something we fight every time we heal a wound (or for that matter, repair a car or clean the house). Physics also tells us that we can repair the damage of aging and injury as long as we have the skill and energy to do so.

The fact that physics guarantees we'll experience aging and injury means we can reject the idea that aging is somehow directly beneficial to us or to our species. By Occam's Razor[1], there's no need to suppose some evolutionary benefit from aging and injury, since they...
Popularizing vibes vs. models
by DirectedEvolution — 2023-07-12T05:44:21.586Z

I'm working on a series of blog posts to popularize The Handbook of the Biology of Aging.

One of the dilemmas I face is between popularizing vibes vs. popularizing models. What do I mean by that?

Popularizing vibes trades off exactitude for getting the right feeling across. If I say "people are living longer free of disability," you feel like you know what I mean, and you know that's an encouraging sign.
Popularizing models makes the opposite trade. If I explain why there's a big difference between the proportion of the population with diabetes, and the length of life spent with diabetes, I have to put you in a state of icy rational focus and compromise on vibes or length.

This is LessWrong, so I expect my audience's gut reaction to be "you should popularize models!" That's my gut reaction, anyway.

But let's think about why popularizing vibes can be good. One reason is that vibes can come from a list of concrete (if slightly ambiguously stated) facts:

Life expectancy at birth is stalled
Life expectancy at 65 is improving
Fewer people have heart attacks and minor disabilities
People live longer free of disabilities, and recover from them more often
White men are benefitting most from getting fewer disabilities
We're doing a better job using medication to manage cholesterol and blood pressure

Facts are good! When you read that list, you're taking in data. It's painting a portrait of the state of American public health.

But I'm telling you these facts not because you're particuarly interested in each data point. You could look them up yourself if you were. And I don't expect you'll remember them.

Instead, I'm curating these facts because I think they'll help you form a holistic impression about whether or not Americans are getting healthier over time. So the reason I'm feeding you these facts for the vibes. And I do expect you'll remember that.

Models demand more focus to take in, and more explanation. When I explain a model, I'm trying to give you a new conceptual distinction, not just write bits into your head. If you're standing at the checkout line, browsing LessWrong on your phone, and I try to teach you a model, you're probably going to say "ugh" and read something else.

But if I can get you to understand a new model, there's a big payoff. Because now, new I can tell you about new types of facts, and therefore introduce you to a whole new vibe. You won't be able to appre...
Announcing the AI Fables Writing Contest! 
by DaystarEld — 2023-07-12T03:04:51.630Z

Winners have been announced!

TL;DR: Writing Contest for AI Fables

Deadline: Sept 1/Oct 1

Prizes: $1500/1000/500, consideration for writing retreat

Word length: <6,000

How: Google Doc in replies

Purpose: Help shape the future by helping people understand relevant issues.

Hey everyone, I’d like to announce a writing competition to follow up on

this post

about AI Fables!

Like Bard, I write fiction and think it has a lot of power not just to expand our imaginations beyond what we believe is possible, but also educate and inspire. For generations the idea of what Artificial General Intelligence could or would look like has been shaped by fiction, for better and for worse, and that will likely continue even as what once seemed to be purely speculative starts to become more and more real in our everyday lives.

But there’s still time for good fiction to help shape the future, and on this particular topic, with the world changing so quickly, I want to help fill the empty spaces waiting for stories that can help people grapple with the relevant issues, and I’d like to help encourage those stories to be as good as possible, meaning both engaging and well-informed.

To that end, I’m calling for submissions of short stories or story outlines that involve one or more of the “nuts and bolts” covered in the above post, as well as some of my own tweaks:

Basics of AI

Neural networks are black boxes (though interpretability might help us to see inside).

AI "Psychology"

AI systems are alien in how they think. Even AGI are unlikely to think like humans or value things we'd take for granted they would.
Orthogonality and instrumental convergence might provide insight into likely AI behaviour.
AGI systems might be agents, in some relatively natural sense. They might also simulate agents, even if they are not agents.

Potential dangers from AI

Outer misalignment is a potential danger, but in the context of neural networks so too is inner misalignment (related: reward misspecification and goal misgeneralisation).
Deceptive alignment might lead to worries about a treacherous turn.
The possibility of recursive improvement might influence views about takeoff speed (which might influence views about safety).

Broader Context of Potential Risks

Different challenges might arise in the case of a singleton, when compared with mu

...
Why it's necessary to shoot yourself in the foot
by Jacob G-W — 2023-07-11T21:17:48.907Z
Link: https://g-w1.github.io/blog/observation/2023/07/08/shoot-yourself-in-the-foot.html

Without shooting yourself in the foot, learning lacks motivation. Complexity without reason is really confusing.

At the Recurse Center Feb 2020: I watch a talk during presentations about someone optimizing a database engine. It was really complicated, and I remeber nothing about the talk itself. What I do remember is thinking to myself "this seems really complicated for no good reason." Keep in mind, I had never made a web application at that time and when I needed to store data, I just used a csv file or a python pickle file on the disk. I thought that a filesystem was sufficient for storing data.

Fast forward a few months and I'm building my first web application. I don't remember what it was for, but I remember using a csv file as the database. I had to load the file into memory every time I wanted to look something up and it was just a big pain. I now understood why using a database is (sometimes) a good idea.

Learning Rust, July 2020: I start learning about the borrow checker. It prevents you from keeping a pointer to an item of a vector (&vec[i]) if you pass the Vec<T> as a &mut. I don't really get why this is necessary. I have never done low-level programming before, never used pointers, and now I am being told that following the borrow checker is 'safe'. It is still very confusing to me.

Fast forward to when I am writing Zig code. I take &array_list.items[i], append to the array_list and then try to write to the stored pointer. I get a segfault. Ahh, now I get the problem Rust solves.

Learning Vue, July 2020: Why are there all of these complicated ways to represent state? Shouldn't developing a web app be simpler than this?

After writing a bunch of vanilla JS, I can see why these frameworks could be useful. I have never made a very big web app, but I could see that keeping track of state and what is or isn't rendered is hard and gets much harder with a bigger web app.

To fully understand a "best practice" or why something is necessary, it's important to experience how things go wrong without it. When teaching programming, we should let people make these mistakes, and then show them the tools to correct them. Just giving someone a complicated tool without a salient reason to explain its complexity will just make them really confused.

Some opinions:

You should store stuff in csv files before using a database

You should learn Zig (or

...
How do low level hypotheses constrain high level ones? The mystery of the disappearing diamond.
by Christopher King — 2023-07-11T19:27:48.756Z

Consider two friends, Alice and Bob, trying to figure out what happened to a diamond that disappeared from a museum. They do so in the form of a game that is kind of an approximation to Solomonoff induction: they will work together to come up with the smallest possible explanations that conform to the data, for some intuitive notion of smallness.

This helps to eliminate fake explanations; the hypothesis "a witch caused Henry to fall ill" can be simplified to "Henry fell ill". But "Sally touched Henry and Bob, and Sally is sick and Henry is sick and Bob is sick" is beaten by "Sally touched Henry and Bob and Sally is contagiously sick". An explanation is good if it is smaller than just hard-coding the answer.

Bob knows that there are four diamond thieves in the city, so he comes up with four hypotheses:

The diamond was stolen by thief number 1.
The diamond was stolen by thief number 2.
The diamond was stolen by thief number 3.
The diamond was stolen by thief number 4.

These are all roughly the same complexity (depending on how you encode numbers), so this provides a uniform distribution over the four thieves.

Alice comes up with one hypothesis:

The diamond spontaneously ceased existing.

and declares victory.

Bob: What, that makes no sense? Physical objects can't stop existing.
Alice: We aren't doing physics; we are playing a game.
Bob: ͠° ͟ʖ ͡°
Alice: ¯\_(ツ)_/¯

But there is an additional rule; you can add other data from the real world to the challenge. For example, for "Henry falling ill", you might get better hypotheses if you try to compress info about all the sick people in the village, so that a slightly more complex hypothesis that can explain all of them wins!

Bob: I hereby add all physics experiments to the data set!

Alice then comes up with the following hypothesis: all physical experiments are explained by the standard model of physics, except the diamond spontaneously ceased existing.

"Was it really necessary to write it out? And why do you know this off the top of your head?" said Bob.

Bob: That bit about the diamond ceasing to exist is arbitrary!
Alice: Do you have a better hypothesis?
Bob: Uhm, idk. All physical experiments are explained by the standard model of physics, and the diamond was stolen by thief 3?
Alice: ͠° ͟ʖ ͡°

Bob's new hypothesis is bigger than Alice's new hypothesis.

And thus we run into the problem. Due to Alice's and Bob's bounded rationality, they ca...
Do we automatically accept propositions? 
by Aaron Graifman — 2023-07-11T17:45:41.861Z

Epistemic Status: Exploratory (I'm searching for an expert or someone who has studied and rigorously investigated these claims/essays, as I intend to, but time doesn't permit me now)
16 years ago, Eliezer asked "Do we Believe Everything we're told?"
This was with respect to the Spinozan Model of the mind.

Spinoza suggested that we first passively accept a proposition in the course of comprehending it, and only afterward actively disbelieve propositions which are rejected by consideration.

My question I'd like to pose is, has anyone deeply investigated this? Cognitive Science is of interest to me, but I am not as educated as many in this field.
I'd like to call attention to work done by Eric Mandelbaum of CUNY[1].

Mandelbaum has investigated the Spinozan Model further and written multiple essays on it.

I want to reach out to the lesswrong community to see if anyone has deeply investigated the claim and research behind this paper Thinking is Believing, published in 2014. [2]
This idea of the Spinozan model has been on my mind for months, and upon taking an Intro to Philosophy of Cognitive Science class, I've wanted to dive into it more.
What's stopping me is lack of trust in my ability, and other projects going on.
I've been debating asking, but I figured I would put this out there to the community and hope I hear back.

The importance:
IF we automatically accept propositions AND we are truth seeking, THEN we ought to guard our mind from likely falsehoods.

^

Mandelbaum was a researcher at FHI in the past (date not found)

^

ABSTRACT

The idea that people can entertain propositions without believing them is widespread, intuitive, and most probably false. The main goal of this essay is to argue against the claim that people can entertain a proposition without believing it. Evidence is presented demonstrating that we cannot withhold assent from any proposition we happen to consider. A model of belief fixation is then sketched and used to explain hitherto disparate, recalcitrant and somewhat mysterious psychological phenomena. The proposed model is one where beliefs are the automatic output of a computationally null belief acquisition reflex. In short, the model holds that the mere activation of a mentally represented truth apt proposition leads to immediately believing it. The essay concludes by considering some consequences that the pr

...
fMRI LIKE APPROACH TO AI ALIGNMENT / DECEPTIVE BEHAVIOUR
by Escaque 66 — 2023-07-11T17:17:51.576Z

fMRI (Functional Magnetic Resonance Imaging) is a technique used to investigate the human brain by detecting changes in the blood flow when doing certain tasks. Blood flow is an indicator of energy consumption, and its changes indicate areas of the brain associated with certain activities.

AI alignment is an open area of investigation trying to solve the problem of ensuring that an AI system's goals and behaviours are aligned with human values. Among other problems that may arise in an advance AI system (specification, robustness, interpretability…), one of the main concerns is the possibility that it may attempt to hide its real intentions. This is generally referred to as "concealed intent" or "deceptive behaviour".

We propose to use an approach similar to fMRI to investigate the possibility of a deceptive behaviour in an advance AI system. In our case, the activation of neurons will play the role of energy consumption in fMRI, with the advantage of having a much higher granularity.

First, we hypothesize that:

A complex AI system will have certain internal areas dedicated to specific functions and ideas, as it happens in the human brain. In our case, certain areas of the system will be associated to ideas of deception, fraud, deceitfulness, betrayal, … (in short, we will call these areas Deceptive Areas).
It won’t be possible for a complex system to avoid the use of those Deceptive Areas if it is acting with a deceptive behaviour.

To use this approach, we propose these general steps:

Resting State. Establishment of a “Resting State”. Determining the average activation state of the neurons when doing varied tasks.
Deceptive Areas. Presentation to the system of tasks and stimulus that involve ideas related to deception. Use of statistical analysis to compare the results of steps 1 and 2 to determine the areas with a significant deviation from the resting state; those will be identified as the Deceptive Areas.
Continuous tests of the AI system in operation, to detect anomalous activation of the Deceptive Areas.

Technical feasibility

Complex AI systems have a huge number of neurons and parameters, and work as a “black box” with no clear explanation of the results it produces. This complexity makes difficult to determine the state and evolution of the activated cells when the system is in training or operation.

Our approach u...
Introducing Fatebook: the fastest way to make and track predictions
by Adam B — 2023-07-11T15:28:13.798Z
Link: https://fatebook.io

Fatebook is a website that makes it extremely low friction to make and track predictions.

It's designed to be very fast - just open a new tab, go to fatebook.io, type your prediction, and hit enter. Later, you'll get an email reminding you to resolve your question as YES, NO, or AMBIGUOUS.

It's private by default, so you can track personal questions and give forecasts that you don't want to share publicly. You can also share questions with specific people, or publicly.

Fatebook syncs with

Fatebook for Slack

- if you log in with the email you use for Slack, you’ll see all of your questions on the website.

As you resolve your forecasts, you'll build a track record - Brier score, Relative Brier score, and see your calibration chart. You can use this to track the development of your forecasting skills.

Some stories of outcomes I hope Fatebook will enable

During conversations at meetups, in coworking spaces, or amongst friends, it’s common to pull out your phone and jot down predictions on Fatebook about cruxes of disagreement
Before you start projects, you and your team make your underlying assumptions explicit and put probabilities on them - then, as your plans make contact with reality, you update your estimates
As part of your monthly review process, you might make forecasts about your goals and wellbeing
If you’re exploring career options and doing cheap tests like reading or interning, you first make predictions about what you’ll learn. Then you return to these periodically to reflect on how valuable more exploration might be
Intro programs to rationality (e.g. ESPR, Atlas, Leaf) and to EA (e.g. university reading groups, AGISF) use Fatebook to make both on- and off-topic predictions. Participants get a chance to try forecasting on questions that are relevant to their interests and lives

As a result, I hope that we’ll reap some of the benefits of tracking predictions, e.g.:

Truth-seeking incentives that reduce motivated reasoning => better decisions
Probabilities and concrete questions reduce talking past each other => clearer communication
Track records help people improve their forecasting skills, and help identify people with excellent abilities (not just restricted to the domains that are typically covered on public platforms like Metaculus and Manifold like tech and geopolitics) => forecasting skill development and tale

...
My Weirdest Experience
by Bridgett Kay — 2023-07-11T14:44:55.469Z
Link: https://dxmrevealed.wordpress.com/2020/12/09/the-weirdest-thing-that-has-ever-happened-to-me/

I wrote about this a long time ago in some comments here- I'm not sure where or when- but there seemed to be a lot of interest in it. Because of this, I thought I would share the post I've written about it here.