Have you seen Metacademy? Here is a "tech-tree" for recurrent neural networks [0]. In the top-left corner you can switch between "graph" and "list" mode. Unfortunately, site hasn't been updated for some time. However, they have open sourced their code and I was thinking maybe we (volunteers) could pick up where they left off and continue this initiative. Previous discussion [2].
This seems like an amazing concept and I have been thinking of it ever since I saw KhanAcademy knowledge tree.
I can see this being combined with Arbital's Lens (Same thing explained in multiple ways - from a simple explanation for a 10-year-old to a rigorous explanation for a mathematician)
I always considered this the best way to actually progress in my learning and I still see it as the next big thing after Wikipedia, once it's done correctly.
No I have not seen this, but it looks awesome and like a great place to start. This structure is pretty much exactly what I envisioned at a high level. It would be even more useful if there were a finer degree of granularity between concepts, and each vertex actually directly linked to a video. I think the killer feature for a partially ordered learning resource is going to be keeping people within the ecosystem (i.e. watching embedded videos or reading embedded articles/guides/etc.) so they can grind out on your website, not repeatedly have to go to other websites where they may get distracted or become reluctant to return to the original.
Happy if it helped. IMO Dr. Rhonda Patrick is good, even if she is a biochemist. Her nutrition advice is mostly based on others' work and is well researched and solid. I'm skeptical about the whole cryo thing, and we do not support smoothie movement, so we mostly ignore that part. She's certainly worth to listen/read.
Thanks for the links! Reading from a source and weeding out "low-quality stuff" is a valuable skill to have. Not so long ago a study on telomere length was lambasted by HN community for having low sample size [0]
How do you choose which papers to follow? Any rules of thumb/red flags for assessing a paper?
First, I start with a clear goal/question in mind: e.g. which lifestyle changes could help me live longer? Then there are several things I look for when assessing a specific paper:
a) Sample size is key. Small samples are not very reliable as you can reach almost any conclusion you want. I also prefer looking at meta-analysis (this is when scientists look at all the studies that analysed a given topic). This means more data (larger samples) and less risk of biases.
b) Low P-value (below 5% is what you're looking for). This is the probability that the effect found was due to randomness. If this is too high, then you cannot rely on your conclusions.
c) Adjusting for other variables that could explain the effect that was found in the study. E.g. if you want to find if eating more vegetables is healthy, you should adjust for exercise, so you can compare people that do the same amount of exercise but eat different amounts of vegetables. Otherwise the effect found could be explained by other variables not adjusted for.
d) No funding bias. Is this a study stating that eating X is good but funded by the Association of X Producers? Avoid those :)
e) No publication bias (this only applies to meta-analysis). This is when scientists don't publish their findings (e.g. because they did a small study, or found an insignificant effect, or found an effect they or their funders "didn't like"). Most good meta-analysis will comment on this (it's also called Funnel test/analysis).
f) Human studies. I tend to ignore studies that aren't done with humans as the probability the conclusions will hold in humans is actually very low (probably less than 10%). It's also preferable to have intervention studies (when you get two groups of people that receive two different interventions - e.g. eating beets juice and placebo - and compare outcomes; they generally don't know which intervention they're getting) vs. observational studies (when you study a group of people and try to assess if differences in what they say they do lead to different health outcomes). But in this field, most studies are actually observational, hence the importance of all the other things I've mentioned.
Why would you pay somebody to administrate your own money and give you random results while you can do it yourself?
Corporate bonds. I don't know the legislation in the US, but if there's any chance of you losing your money in case of default or anything. Don't do it. Don't look at how much things yield, but also evaluate how much risk there's behind those things. Nevertheless, if you think it's a good idea, as long as you diversify your portifolio and is investing at most 10% on it. The worst that can happen is that you lose 10% of your assets.
In my opinion, just invest with good diversification in businesses you think are good. Read a book or two in value investing and only invest initially in companies which have not too much debt and have had profits in the last 5 years, for instance. As you progress and get more comfortable, do more complex analysis and invest in companies with a more free criteria.
It might be boring at the first place, but that will prevent you from losing money. If you aren't working for a fund and isn't absolutely rich, just getting a paycheck at the end of the month, the yield doesn't matter too much. Just do the math for the case below and think for yourself:
You save 1k a month for 30 years. Every year you get a liquid 3% yield(I'm making it simple by excluding inflation). Compare this to with a 6% yield(DOUBLE!). Just check how the difference is small.
Now, compare that you saved instead 1.1k a month for 30 years. Only 100 more. There you go. Do the maths, all the yield doesn't matter. 100 dolars in that case is 10% of the total income your assets will have monthly, it's a lot.
The stock market won't make you rich, saving and having intentions of growing your assets will.
I don't have a full list, but some that I thought of when I was trying out this website were Foucault's Pendulum; East of Eden; Sometimes a Great Notion; A Man in Full; I, Claudius; and Anathem.
* Discourse forum: https://www.discourse.org/
* Slack channel
* Google Docs spreadsheet