[1/n] Debido al post de @gummibear737 no va a quedar otra que explicar nuestra hipótesis de cómo las mitigaciones universales (por ej. cuarentena) extendidas en el tiempo causan más muerte que lo que hizo Suecia.
[2/n] Lo que voy a comentar está todavía en desarrollo y ni siquiera es el tema central del paper que estamos escribiendo con @LDjaparidze, así que no intenten leer entre líneas o en detalle.
[3/n] La idea general es: Como muchos fenómenos en física y biología, la curva de muerte es dependiente del camino. De una forma u otra se llega a la inmunidad comunitaria, y habrá muertes durante ese camino. La pregunta es siempre COMO llegamos ahí.
[4/n] Ya sea por infección, por vacunación (al menos por un tiempo) o por muerte, parte de la población no puede contagiar más. Mientras tanto, en el tiempo, vamos a seguir contabilizando muertes (triste, pero un hecho).
[5/n] El argumento central es el siguiente. Asumimos un sistema con 2 clases (como el COVID): Vulnerables y Sanos. Hasta llegar a la inmunidad comunitaria (el virus se vuelve endémico) se espera que una clase tenga N muertes cada 100000 habitantes y la otra N'.
[6/n] Como las muertes esperadas de los Vulnerables es mucho mayor que de los Sanos, cualquier camino que aumente el riesgo de infección de los Vulnerables tendrá a lo largo del tiempo mayor cantidad de muertes.
[7/n] Y cuanto más largo sea ese camino, mayor será la cantidad.
[8/n] Entonces la estrategia óptima es que los Sanos se infecten primero porque su riesgo de muerte es varios órdenes de magnitud menor (10x a 100x menor), Llegar a inmunidad comunitaria de la forma más rápida y ética posible siempre va a minimizar las muertes.
[9/n] Las cuarentenas son mitigaciones universales. Esto significa: Si tú eres el virus, como no pudimos extinguirte, cuando debas infectar (para reproducirte) sólo podrás hacerlo con quien haya cometido un error al protegerse.
[10/n] En probabilidad llamamos a esto "Camino Aleatorio". Se le asigna la misma probabilidad a elegir un Vulnerable o un Sano. La probabilidad de elegir un Vulnerable es p=0.5
[11/n] Las mitigaciones segmentadas como cuidar más a los Vulnerables, hacen más difícil que tú como virus puedas infectarlos y en contraposición más fácil para tí elegir un Sano. Este último contribuye a la inmunidad comunitaria con riesgos mínimo (p tiende a 0).
[12/n] En la práctica, p no es exactamente p=0.5 ni p=0, diferencias demográficas, o que la gente trabaje y las políticas públicas influencian ese número. La pregunta es: "¿Será posible estimar ese p?". Bueno, de eso se trata nuestro paper
[13/n] En resumen: Si no es posible extinguir rápidamente, a medida que la epidemia se extiende, las mitigaciones universales fuerzan a que la probabilidad de infectar Vulnerables y Sanos se equiparen. Por lo tanto, de aplicarlas tendremos más muertos.
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1/ Alright, nerds buckle up. My read on todays news is @elonmusk playing 8D chess as usual. You have to hand it to him, he is smart as fuck. This isn’t just joking around trying to buy OpenAI. This is the AI industry’s version of Game of Thrones, and everyone’s got knives out.
Let’s break it down. 🧵👇
2/ First, OpenAI’s structure is a financial booby trap.
- There’s a nonprofit (OpenAI Inc.) that controls the for-profit OpenAI LP.
- That means you can’t just buy OpenAI outright—first, you gotta deal with the nonprofit board.
- It’s like saying, "I wanna buy Twitter," but the deal has to be approved by a secret society of monks.
3/ Musk’s move? Drop a $97.4 billion bid.
Now, did he actually wanna buy it? Probably not. This is a game theory 101 class onto adversarial games.
- If OpenAI rejects it, they must explain why, revealing their real motives.
- If they accept, Musk gets control and can shut down the Microsoft-aligned vision.
- If they try a legal loophole? He sues their pants off.
1/ After almost 1.5 years of studying cancer research for personal reasons, I arrived at a realization that prompted me to write this tweet. I will lay out the hypothesis in this thread.
2/ Disclaimer: I am not a formally trained health researcher. More like a very curious and tenacious guy with a 15+ year background in research, development, & reproducibility in computer science (computer science).
3/ I am putting the hypothesis out there because it may make sense to others doing field work. Feel free to dissect this hypothesis, find holes in it, and play devil's advocate. We will all come out smarter from it.
1/ There is a very perverse dynamic on how Chavism (aka "the communist socialism") works. Let's use Argentina as the example. Over the first 20 years they initiate a process that we could call "Earnings Substitution" that will seal your fate over time.
2/ Your earnings/salary is going down and at the same time "subsidies" start to go up in order to fool people into think that nothing has changed. This works because the dirty job is done by inflation which is a much slower process.
3/ By the time people starts to realize that something is wrong, because some critical goods are not available (medicine, food, you name it) or inflation enters a death spiral; most people already depend on subsidies for spending.
1/ Recently some interesting papers have been doing the rounds in the health community. To me the most interesting ones have been the GlyNAC paper and the more recent Taurine deficiency as a driver of aging papers.
2/ Disclaimer: While I have been researching this for a year and even executed an experimental protocol tailored for myself based on the GlyNAC paper, I am NOT a health professional, and I am just taking my health into my own hands. This is not advice of any kind.
3/ Disclaimers aside, why do I think these 2 papers are interesting? First because the claim (if true) is a game changer. And second because they may be related but I haven’t seen this relationship spotlighted by anyone.
This just confirmed the weaponization of block lists. If enough people/bots block and mute you, they are essentially cancelling you. I find lots of people with I have never interacted with that has me blocked. Assuming there are third party block lists and block networks.
Normally that is an issue in general. Anyone that has done reinforcement learning had figure out (usually in the worst way) that you have to be incredible cautious with penalties. They are very prone to be gamed.
2/ Since the general problem that practitioners find (in the worst way) is always training set tainting (guilty-as-charged). Habits die hard, the first thing I did is asking to do a review of the paper without any extra knowledge about what the paper says
3/ From the response alone I learned 2 things. First, our paper title was deadly accurate. I also learned that it has no information whatsoever on it, as the entire response can be generated from understanding the title itself.