Quantified Self is a community site about tracking personal data such as health, food, and productivity, with talks, meetups, and member experiments.
HN commenters link it as the hub of the quantified self movement and point people to its conferences and meetups.
That informational abundance or disinformational abundance create a scarcity of attention is hardly novel.
Unfortunately most advice tends toward conscious and deliberate rejection of such sources, which continue that assault on attention.
As anyone who's implemented an adblocker or similar online annoyance filters is aware, automating the process of distraction rejection is a vastly more effective and less-attention-costly approach. Rather than individually reject cookies, or cookie notices, or trackers, or ads, or various interstitials / "recommendations" / nags / pop-ups / fly-overs, and the like, I've applied and created sets of tools which remove those without my further conscious awareness. Users of PiHole may occasionally check the dashboard blocking statistics and be amazed at how much not only useless but actively counterproductive crud has been avoided.
Information overload requires cheap, fast, regret-free rejection tools.[1] I've come to suspect that worldviews and models specifically function in this manner, identifying key information which we should focus on, and costlessly discarding the rest.[2] The article does nod to this briefly, particularly in the note referencing Gigerenzer & Gaissmaier, 2011, but on balance misplaces its emphasis, most especially in its suggestions.
Another surprisingly effective tool is randomness, whether in sampling or in decisionmaking directly. Random sampling is a statistical tool for choosing a very small set of…
Personally I found it can help to write down or talk about any emotions which may be present yet currently outside of your scope of perception/awareness. E.g., "based on what happened today, any random person could be excused for feeling ____ or ____."
I also found it helpful to reorient my diet toward sweets while processing emotion, foods which my body seems to "request" or crave in order to deal with situations that may pierce the lower portions of Maslow's pyramid. Like you just lost your footing on an important work project and people will be angry, or your stocks took a dive and you panic-sold, etc.
By cutting out normal / healthy foods and focusing on indulgent crisis foods first, the overall caloric expenditure can be reduced insofar as crisis foods were what you really wanted to eat, and this direct approach helps to blunt the weight-gain effect of binge eating extra sweets combined with any normal foods outside of that scope. It felt weird at first, but it worked out well, in its strangely logical way.
These are just my experiences though, and I really recommend that you keep a log and an open mind and see what you can figure out as you look into this further. I found some helpful mental models related to [0]. Good luck and good on you for raising the question.
0. https://quantifiedself.com/
This is something I think about often when it comes to tools. The idea that Google had all of my location history for the past 8 years means I can see everywhere I've ever gone. I can lookup any email I've ever sent/received. I can look through my browser history for any particular day. With Spotify + Last.fm I can see stats about my listening history, and with Tract.tv I can see my TV/movie watching habits. Strava tracks exercise, and YNAB tracks budgeting/spending. There's an incredible amount of health data from my iPhone + Apple Watch.
It's really cool to think that all of this data exists, but I'm not really sure that it's useful. Occasionally I've gone back through my old search history for that one old post that I wanted to re-read, or go through my Spotify listening history to find a song I really enjoyed but forgot the name of, but 99% of that data goes to waste.
Quantified Self [0] is an interesting project to me since it seems to make use of data from all of these disparate sources, but I'm still not sure what the outcome of tracking all of this data is. It seems cool but not useful.
[0]: https://quantifiedself.com/
It's one of the nicest things to do if you love computers, and great for your health compared with staying indoors.
> Could one actually work like this, typing and everything? After my “heart-rate discovery” I decided I had to try it. I thought I’d have to build something myself, but actually one can just buy “walking desks”, and so I did. And after minor modifications, I discovered that I could walk and type perfectly well with it, even for a couple of hours. I was embarrassed I hadn’t figured out such a simple solution 20 years ago. But starting last fall—whenever the weather’s been good—I’ve tried to spend a couple of hours of each day walking outside like this
https://writings.stephenwolfram.com/2019/02/seeking-the-productive-life-some-details-of-my-personal-infrastructure/#:~:text=Could%20one,this,-%3A
https://quantifiedself.com/blog/stephen-wolfram-finds-working-outside-lowers-his-resting-heart-rate/
It's a great post, but to me there's another message that Dustin - understandably - hasn't focused on: "perfect health" is often (usually?) an illusion.
Given what I've learned about health in recent years, I can't accept that this guy really was in perfect health; if he was, he wouldn't have suffered a major cardiac arrest while simply jogging on a treadmill.
I think it's mostly a failure of modern medicine and modern attitudes towards health that most of us walk around feeling and looking like we're in "perfect" health, only to find all too late that a severe condition was lurking undetected.
The answers may lie in fields like Quantified Self [1], or PG's suggestion of Ongoing diagnosis [2].
But whatever the case, we're only just starting to scratch the surface of an area that I think this post demonstrates is hugely important.
[1] http://quantifiedself.com/
[2] http://paulgraham.com/ambitious.html
To be fair, microsoft did kinect, a computer vision product that is in millions of hoees. It wouldn't be hard for google or apple to this but they haven't for some reason.
The problem with microsoft has been that is ran it's research wing separate from the business, while google runs research like a business
http://cacm.acm.org/magazines/2012/7/151226-googles-hybrid-approach-to-research/fulltext
A full shift to the realization that machine learning isn't research and should actually be the majority of what everyone does hasn't sunk in it. The question not be "where can we apply machine learning" but instead "where should machine learning not be applied". This type of thinking should occur even down to the individual person (quantified self) http://quantifiedself.com/ .