Unfinished Projects Thread

At some point I had the harebrained idea of writing a LW sequence on health, despite lacking all formal qualifications for doing so, because I've been confused about / disappointed with most of my experiences at doctors' offices throughout my life. Eventually I figured I won't understand why until I do a deep dive into the topic myself.

Questions I'd like to help (myself and others) answer include "how can I think about this potential illness I have" or "how could I tell whether I have a sleep problem when I'm unconscious while asleep" or "how good is healthcare, how do the incentives work, in which situations are doctors likely to give good / poor advice", etc.

By now I have a huge bunch of disorganized notes, but no drafts or essays written up; maybe I'll manage to write one this week, but most likely not.

Rough intended outline:

• Preamble: What does it mean to take care of your health? And then, how to actually act on health concerns (wrt motivation etc.)?
• Epistemics: Thinking about health is hard and involves a lot of biases. Disambiguating disease (-> Scott's LW essay on Diseased Thinking). Epistemic learned helplessness. How to think about and compare health risks. Etc.
• How good is Healthcare? Civilizational inadequacy and Gell-Mann Amnesia. Why are real doctors not Detectives of Illnesses à la Dr. House? Principal-agent problems. Which concerns by patients are not taken seriously? How does the healthcare system deal with chronic illnesses? Covid Policy. Zvi's FDA Delenda Est stuff. Etc. pp.
• Unknown Unknowns: If you've always been healthy / ill, how would you even know? Society-wide failings, blind spots, and delusions (e.g. the manioc story from The Secret Of Our Success). Etc.
• Operating Conditions of the Human Body (a particularly Gears-y essay): What temperature, humidity, nutrients, noise levels, oxygen levels, CO2 levels, etc. etc. does a human body need to thrive? How can you tell if something is off here? (-> e.g. buy a CO2 sensor, a thermometer, etc.)
• A Rough Overview of Physiology: [Whatever I as a layperson can cobble together to provide very-rough-but-not-misleadingly-incorrect summaries of all bodily systems (like the gastrointestinal system). And how and to which extent a layperson can roughly tell whether the system is healthy.]
• Personal Idiosyncrasies: In a world of ~8 billion people, you're probably not a unique snowflake with unique health issues... unless maybe everyone is one? On stuff like hereditary disease, birth defects, but also the notions of e.g. Typical Mind Fallacy and Universal Human Experiences mentioned frequently on SSC (e.g. aphantasia, tinnitus, visual snow), etc.
• Things That (Might) (Actually) Help: Synthesizing hopefully-actually-useful-advice from the above essays. I expect this to result in advice like "buy an ergonomic chair", not "here's a magical way to solve all your health issues forever".
• [Plus a few other miscellaneous essays.]

Does that sound interesting / useful / valuable?

Regarding concrete blockers for this project (besides procrastination, of course):

• I'm interested in pointers to resources on these topics (like physiology or "how does the healthcare system work") which are considered to be very accurate. E.g. "X is the authoritative textbook on Intro to Physiology" or something.
• Are you aware of any great resources (search engines, websites, etc.) for asking general health questions? Resources used by professionals would be fine, too, but I suspect those would be paywalled.
• Once I have a draft to show, I could use feedback from beta readers with backgrounds in healthcare or medicine. For this I'll also request assistance from the LW feedback service.

Hi, I'm a lurker. I work on CPUs. This also motivated me to post!

This is a rather niche topic, but I want to express it, because I greatly enjoy seeing other ramble about their deep-work domain expertise, so maybe someone will find this interesting too? This is relatively similar to the concept behind the podcast [What's your problem?], in which engineers talk about ridiculously niche problems that are integral to their field.

Anyways-- here's my problem.

Fuzzing (maybe known as mutation based testing, or coverage directed verification, or 10 other different names) has, in my opinion, been revolutionary for the software security industry. [AFL] is probably the best and most successful example, and I think most people would agree with me that this tool has saved millions of manhours in finding security vulnerabilities.

Why don't we have such tools in hardware? Well, my personal opinion is that EDA tools are rather monopolistic and cumbersome relative to e.g. GCC (e.g. imagine paying millions of dollars for a GCC release!), and a partial side-effect of that is that the language hardware codes in (verilog, systemverilog) is so ingrained we can't get out of it.

This is just barely starting to change.

[Here] is my personal top favorite of a contender. What makes this cool is not entirely revolutionary new ideas, but rather the sheer amount of effort to make things just work is truly truly commendable. 

The main perk of fuzzing is, frankly, finding low-ish hanging fruit. Just like how buffer overflows are, in some sense, a "known problem", there's a plethora of hardware vulnerabilities I've found that you wouldn't believe are insanely easy to find. And I firmly think this can be done by simple fuzzing.

My project plan? Convert the detected vulnerabilities into generated exploitable software vulnerabilities. And I think the above project can fit into the "detection" aspect-- honestly still a WIP for me to evaluate how good it is, or how complicated the fuzzer is (most of the time it's just a wrapper around SMT solvers), but it's something I'm excited about.

(On the "exploitable vulnerabilities" end, there is similar work [here], but I've done some experimentation with this and still find it rather cumbersome for a variety of details I won't get in to.)

What's the purpose? To have people encourage you to finish them? To look cool for having started something awesome?

Big Motivation: Biological systems are thought to stochastically sample from probability distributions. For instance, an optimal prey who is in the act of evading a predator might want to act randomly, at least relative to the predator's model of the prey. Is it possible for such a system to actually generate a random output without explicitly stochastic mechanisms?

Actual Project Question: How can deterministic recurrent neural networks with fixed weights be trained to create random outputs?

Project Plan: Train a recurrent neural network to output a binary digit at random, with specified entropy. For instance, say I want an RNN that can output a 0 or a 1 at every timestep, and I'd like the bit to be (effectively) chosen at random from a uniform distribution. 

Some initial work/thoughts on solutions: Training an RNN on any set of random outputs will not work, it will just memorize the random strings. Can I train directly on the entropy of the output? One way to get a working system is to have the RNN implement chaotic dynamics, and make sure the timescales work out such that the dynamics have evolved enough to randomly sample the ergodic distribution associated with the chaotic attractor. How exactly I can use this to generate a string with e.g. 0.7 bit entropy indead of 1 bit of entropy, I'm not totally sure. I've implemented a Lorenz attractor, and chosen different planes to seperate the state space into two partitions. I asign one partition the symbol 0 and the other symbol 1. Then I can run the system for N timesteps, and then see if I output a 0 or a 1. Thus I get a symbol string. I can then plot the block length entropy diagram to quantify the generation of structure/entropy in that system. The trick would be to get training working with this system somehow.

Further Goals: How about outputing a string that has different amounts of structure and entropy? For instance, a string that goes ...01R01R01R01R..., where R is a bit with 1 bit of entropy?

For a time, I was interested in a postulated obscure relativistic effect called Transverse Gravitational Redshift. I tried to check the calculations and, in 2019, wrote a simulation of relativistic motion. The simulator works in 3+1D special relativity, uses adaptive time steps, and can reproduce the twin paradox. The project is on Github here: 

https://github.com/GunnarZarncke/timewarp

But to simulate the Transverse Redshift requires general relativity, and extending the simulator to support that would be a huge effort, so I stopped working on it. Still, I think it is a useful software for teaching special relativity.

I don't recommend looking into the effect because it was suggested by a quack as the cause of (perceived) accelerated cosmic expansion. Only if you are still interested in this hobby horse of mine, read on. The effect has been discussed on physics fora here and here. A charitable explanation is here. If the calculations are correct the effect is very small (10^-16 at 1g; and if the calculations are wrong, it is zero). The problem is that general relativity is hard; nobody does calculations except for the guy claiming it - and he doesn't do it in small steps. Everybody who checks makes errors, even physics profs. There are a lot of irrelevant tangents ("doesn't hold for high velocities") or ad-hominems. The last reported error in the debunks went unanswered. Based on what you find online, nobody can know if the thing holds or not. The problem is that the guy is an asocial arrogant quack. He does make testable predictions and provides detailed instructions to reproduce with the SDSS dataset (but please start reading at page 10, or you will fall back over). If he is onto something, we will never know because nobody will take the social risk to associate with him. 

Neural style transfer algorithms that rely on optimizing an image by gradient descent are painfully slow. You can make them faster by training a network to map the original image to a stylized one that minimizes the loss. I made them even faster by stacking a few Perlin noise channels to the input, using a very fast hand-written edge detection algorithm and stacking that too and then performing only local processing. Unfortunately this gives very poor results and is only not uselessly slow for low-res inputs.

So I gave up on that and started reading about non-ML style transfer and texture synthesis algorithms, it's a fascinating topic. In the end I managed to create an algorithm that can perform real time poor high resolution style transfer, by using numba to compile python code. Sadly when I tried to turn that into an Android app, despite trying really hard, I found that there was no way to run numba on Android, and using numpy directly isn't fast enough, so I gave up on that too.