I don't get it. Where is the spec? What's the aperture size, focal length of this beast? What about abberations? How does it work in extreme conditions that a normal lens can't take a good image?
So many questions, but the author decided to use it to take portraits in day light ...
I guess different people have different definition for "significant circumstantial evidence". We have two hypotheses: 1. virus come from seafood market, 2. virus come from lab. Let's see the likelihood for each with each piece of data.
Data 1: Index case. There is no evidence that the "index case" is ever found. The earliest patient backtracked to Nov 2019 [1], but there is no report that patient is related to the lab / market.
Data 2: close lab location to seafood market. This alone does not favor one hypothesis to other, since distance is communicative. The virus can start from either location to the other.
Data 3: Bat species. Not sure what aspect of the bat species is related, but one thing to note is the virus sample found in bat is only 96% similar to the virus sample. At 30k genome and an mutation rate of 1bp per two weeks, these samples have at least 20 years worth of evolution time. Unlikely to relate to either lab / seafood market. Some researchers believe there might be intermediate hosts, but there doesn't seem to be evidence what that intermediate host might be.
Data 4. Earlier lab accident. I think the lab accident data is actually not favoring the lab hypothesis. If you think in a Bayesian way, earlier lab leaks are quickly identified and controlled. Given that this time it is not, your belief for lab leak should decrease a bit. Anyway, for the lab data what's important is the likelihood of (the lab, without genetic engineering, get a hold of this virus, and leaked) vs. (an intermediate host). Having worked in Biohazard level 2/3 labs, I think a leak from level 4 lab will be more unlikely compared to intermediate host, but we don't have any good estimate of the two likelihood yet.
So I think people can have strong beliefs about where the virus come from (since everyone's prior is different), but from the data the likelihood really doesn't strongly favor any of the two hypotheses.
Being someone who is still struggling with these concepts, I like this tutorial because at least it doesn't use the common list/maybe/state to illustrate the concepts. Somehow I feel these concepts are so abstract - unless one is well versed in category theory, maybe only a data approach can prevent people from overfitting these concepts to specific examples.
I would really hope to see a tutorial that have a diverse set of examples and just fmap each example with a light explanation of say, what is a monad in this code and what is not, and because it's a monad we can do this.
Essentially the tutorial can just train a classifier in one's head, and with a nice set of examples maybe the brain can learn a general representation of concepts for the classifier ...
So many questions, but the author decided to use it to take portraits in day light ...