For some of the card games I am familiar with (Netrunner, MTG) there are resources online (wikis, dbs, deck builders) that have all the graphic assets. Alternatively, you can scan or photograph each card in your deck, but that may be time consuming.
As for software, there are engines out there that do content-based image retrieval (CBIR) out of the box [1]. It should be possible to build something quickly in OpenCV. You may be able to get away using simple image template matching by putting a few constraints on how the camera sees each card. Something more robust can be also be build using image descriptors and approximate nearest neighbors, as in [2].
I think it is very doable for card games with thousands of different cards like MTG. I remember there was an old school SIFT/SURF image retrieval system that was able to deal with tens of thousands of album covers super fast. That was more than 10 years ago IIRC.
Nassim Taleb wrote a whole book (Skin in the Game) under a similar premise: “Never trust anyone who doesn’t have skin in the game. Without it, fools and crooks will benefit, and their mistakes will never come back to haunt them.”
I agree completely. It is very easy to dismiss the problem if you are young and you only look at your individual risk. But if you look at society as a whole, the problem is significantly more complicated.
You said there was no data available, and I provided a reference.
The data you cite has a large age group as well (0-69), which has the same problem you describe. See the comment by user kmm below for a reference with a better breakdown of estimated IFR by age groups. The reference also shows how the IFR increases exponentially by age.
If you want to see what number applies to you in particular, then you need an specific breakdown. But if you need to see what is the risk for the population in general, then the estimated total IFR, sampled from that same population, is valuable. Think of individual risk vs systemic risk.
Your reference is very good. It is still a pre-print, but the breakdown is very informative. It estimates the IFR (Table 4)
for ages between 0-34 as 0.01% (1 in 10,000), increasing exponentially from there. The estimated IFR for the next age group, people between 34-54, is between 0.04% and 0.2% (1 in 2,500 to 1 in 500), one to two orders of magnitude greater. For people over 85, the IFR is 36.8% (~1 in 3).
Note that the population over 34 is about 50% of the total in the US.
Right, good point. It is a quality of uniqueness for searchability and marketability. On top of my head I can only think of a couple of counterexamples: Windows and Messenger, but your point is still very valid.
I guess in practice, for search purposes, we will see some kind of suffix attached to the name, Element Chat or similar.
I was trying to address the point of the parent comment, that using common words is detrimental to searchability. I think that is true, but as shown by the examples I gave, it is not catastrophic and can be worked around.