Alex K. Chen

Obsessed with longevity. Unlike anyone you know. Search for me on Twitter and Quora.


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Effects of Castration on the Life Expectancy of Contemporary Men


Among invertebrates, birds and mammals, experimental paradigms that limit reproductive investment also cause lifespan extension [232]. Hypothetically, the need for repairing and preventing damage to the germline dominates resource allocation strategies, while the somatic tissues age and deteriorate [112]. In support of such theories, modulations of reproduction that eliminate germ cells in C. elegans and D. melanogaster provide effective mechanisms for extending lifespan [232-234], phenotypes that may be caused by heightened resource availability and proteome stability within the post-mitotic soma [17, 235]. Inhibiting germline proliferation delays the onset of PolyQ-dependent aggregation and toxicity [235]. Proteasome activity and RPN-6 protein levels are increased in germline-lacking worms [17]. In these long-lived animals, increased proteasome activity, rpn-6 expression and longevity are modulated by DAF-16 [17]. Similar to these long-lived worms, FOXO4 is necessary for increased proteasome activity and PSMD11/Rpn6 levels in immortal hESCs [28, 236]. Interestingly, it has been recently reported that DNA damage in germ cells of C. elegans induce a systemic response that protects somatic tissues by increasing their proteasome activity [237].

Possibly slightly relevant

Core Pathways of Aging

Have you thought of to reduce labor costs?

How often do you check this forum?

You know, I barely checked LW from 2015 to 2020, and now I check it like, every time I feel like I need some novelty refresh, almost as much as I do Twitter... It has definitely improved since a few years ago

Are index funds still a good investment?

The dot-com crash was also preceded by an extremely obvious and unique bubble that has not been seen since - diversifying/rebalancing during a massive/obvious bubble doesn't take that much special skill or awareness, and we're more aware of bubble dynamics now than 2000.

Are index funds still a good investment?

Throughout the last decade (or last 15 years, really), FAANG stocks (and QQQ) have consistently overperformed the market/index funds, with roughly comparable maximum drawdowns relative to even the S&P. It was clear to many of us technophilic early adopters even in the late 2000s that Amazon/Google were going to take over the world (though I'd replace Netflix with NVIDIA as NVIDIA is just more innovative), and their returns have massively outperformed the market, with much smaller drawdowns. COVID only accelerated the returns from FAANG - however - with their monopolization (and penetration into all markets, reducing what upside risk there is left), I'm not sure if FAANG has as much market capture, going forward, as there was 5-10 years ago. I know some have said that it is safe to invest in "singularity stocks" like the cloud - ones that have non-zero chance of precipitating the singularity (or feeding into the data-heavy thesis that accelerationism happens when you have more data/compute power/better algorithms, and only tech-heavy companies have really embraced this trend - some even liken Tesla's valuation to one that you can only understand if it were a "tech stock"). 

This year has pretty much accelerated the growth of all "new technology" stocks too (eg everything and anything to do with "new tech" exploded in value => to be fair ALL the "meme stocks" performed well), but many are now  that they're overvalued and the upside risk to them is not as high as they used to be. Ark Invest is the closest thing there is to a "hedge fund" that tries to understand "new technology" (even traditional hedge fund people like Bill Ackman and Ray Dalio aren't technophiles or well-versed in "technology" => it's known that hedge fund people tend not to outperform the market on long timescales, but a surprisingly small percent of them are like, technophilic), and it has had amazing market-beating returns over the last few years (where you don't have to spend that much time paying attention to it). Also, despite technophilia, the ARKK funds haven't really beat index funds pre-COVID (similarly to solar ETFs, which somehow exploded post-COVID for who-knows-what reason)

You have to look at companies with managers who constantly keep up with new technology/trends (rather than dig into what has always worked for them) and who can be expected to never stagnate.

I'm a little concerned about post-COVID overvaluations across the "tech sector" (especially given the "stagnation hypothesis" that many, particularly the Thielosphere, is concerned about), but I would still put some into QQQ, as QQQ has vastly outperformed index funds (but QQQ may be in a bubble itself). John Hussman has sounded the alarm for years, but if you were paying attention to him, you would have lost out on the returns over the last 5 years. I've observed that most of the high-profile companies that have recently IPO'd (especially in the tech sector) and which have rapidly-growing userbases have had much higher returns than most other companies - just look at how far up Twilio and Slack and Spotify and Cloudflare have gone up. Many recent biotech companies that have targeted CRISPR have also gone way up but they're more at risk of a sudden catastrophic drop if a clinical trial doesn't pan out.

Many I know are bullish in cryptocurrency [particularly BTC\ethereum] again, esp given the prevalence of money printing/devaluation as a response to the COVID crisis (and perhaps as an easier/more politically feasible way to "get money into the economy" than is higher taxation ), and since BTC is near ATH and still nowhere at risk of being at a bubble.

A heuristic I might use: What products/technologies are the smartest/most innovative people (eg those at DeepMind) using? [eg note how viciously smart people in AI have massive salaries and actually, like, use their salaries on smg] Their resourcefulness + financial resources will only improve with time, and them having higher ability to have frictionless workflows (minimizing the amount of time they spend on unnecessary logistical things such as upgrading their PC/changing homes/buying a new car/backing up data/preventative medicine/etc)+ collect data + store energy for data centers + make use of these massive datasets depends on them having access to certain resources (be it energy, speed, technology, SSDs, advanced materials). Think of what they will be like 10 years in the future, and of the materials they will use to maximize their ability to make money-time (or money-time-energy) tradeoffs. Sufficiently resourceful companies will never saturate - they will figure out how to create demand in areas where previous demand was not thought of as existing (kind of like how if you create enough products and advertise them to people, you may convince them that they have a "need" they might not have thought out themselves). If you use this valuation in itself, you wouldn't be surprised at the massive increases in NVIDIA/AMD/GOOGLE/LRCX/GLW/cloud stocks/whatever..

[given current tech valuations, I feel that materials science is the sector that has the most potential to improve/advance, and I am somewhat invested in LRCX/MU/AMAT, but I feel like there still isn't, like, an equivalent to "big tech" for the materials science sector - there isn't a huge market [yet] for photonic or neuromorphic computing, for instance. This is overdue, and it's possible that "AI" can catalyze a massive shift in materials science innovation that could lead to fast AI takeoff]. Also, with quantitative easing and other novel financial instruments (such as potentially cryptocurrency fintech), we may be able to more quickly "manufacture ourselves" out of recessions/crises than before, without being overly dependent on politics or which party wins the white house and dictates tax policy => this flexibility is also why a great depression a la 1929 is unlikely to happen ever again). 

[Linkpost] AlphaFold: a solution to a 50-year-old grand challenge in biology

Does knowing the structure of a protein help with simulating how it responds to any arbitrary/unknown protein/molecule/agonist/antagonist/superagonist? [it seems that even with all the protein structures that we do know well, that finding appropriate agonists of the protein with the desired action is still a huge unsolved problem]. Is simulation a much more difficult problem than "folding"?

This allows us to design "efficient" proteins (proteins designed "intelligently" often do tend to be smaller, less "messy" and "bulky" than naturally-evolved proteins [which also cross over at the most pedagogically unhelpful sites ever], and with protein folding solved, it may be easier for us to design proteins that are less complicated/more amenable to simulation than the natural set of proteins that exist => not to mention that it may be possible to find a specific transferase protein that is able to precisely add a methyl or carboxyl group to any molecule at any location, or a ligase that is able to split a molecule at any arbitrary location). We may also be able to design them based on properties like how easy it is to introduce them into the cell via mRNA (the genes for many natural proteins are not easy to introduce into the cell via CRISPR or AAV, but as protein design-space is so large, you can probably design another protein that carries out the same function that can be delivered into cells via mRNA or CMV-based vectors, without needing to force the corresponding gene at the right location at the cell's nucleus). 

Anyhow, designing proteins for industrial chemistry (eg properly degrade polyethylene plastics in the ocean) [and also those with a specific physical property rather than those that perform a very specific function] is a much easier problem than, say, figuring out how to make an extremely particular histone acetyltransferase or DNA methyltransferase or chaperone enzyme [often those at the center of hub networks and whose evolved messiness naturally evolves due to the necessity of needing to have other extremely precise interactions with other proteins that have also evolved to become messy bloated behemoths] localize/diffuse at the locations where it can precisely do the right things at {X} sites and not do the wrong things at the {Y} other sites. 

Also, this helps us develop a "periodic table of protein function" where you can design proteins that can carry out X function if you change certain motifs to it, and it will turn out as much cleaner/more organizeable/more predictable than the natural super-messy [and hard to organize] set of protein motifs we find in the wild. I think this is especially relevant for manufacturing and industrial chemistry - proteins that broadly carry out functions sort of similar to zymogen. 

The whole field of structural biology was 95% useless anyway.

As long as it produces machine-interpretable output, it's useful for training new algorithms, even if the vast majority of humans are unable to properly interpret protein structure.

^Anyhow, this post was replying to the idealized version. Protein folding is still far from solved, as explains. It's an exciting advance to be sure. I think this allows us to better figure out what a stable system of ultrastructural scaffolds is first before figuring out what precise things can be built USING those ultrastructural scaffolds.

How do you assess the quality / reliability of a scientific study?

Is there an online way to better tag which studies are suspect and which ones aren't - for the sake of everyone else who reads after?

When Money Is Abundant, Knowledge Is The Real Wealth

So, two years ago I quit my monetarily-lucrative job as a data scientist and have mostly focused on acquiring knowledge since then. I can worry about money if and when I know what to do with it.

Also this knowledge only matters if you do something useful with that knowledge, which I'm convinced that you are, for instance. many other people are not able to create useful knowledge and thus may be better suited for earning2give.

Gears-Level Models are Capital Investments

Do you think that applying black box models can result in "progress"? Say, molecular modeling/docking or climate modeling or whole-cell modeling or certain finite-element models? [climate models kind of work with finite element analysis but most people who run them don't understand all the precise elements used in the finite element analysis or COMSOL]? It always seems that there are many many more people who run the models than there are people who develop the models, and the many people who run the models (some of whom are students) are often not as knowledgeable about the internals as those who develop them - yet they still can produce unexpected leads/insights  [or stories - which CAN be deceiving, but which in an optimal world helps others understand the system better even if they aren't super-familiar with the GFD equations of motions that run inside climate models or COMSOL] that might be better than chance.

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