"weight is not really a great measure" followed by saying you measure yourself by looking in the mirror, on an article about weight control is a pretty confusing way to communicate that you measure your health by looking in the mirror. you've made a few other confusing statements, like "Long term, higher intensity cardio builds muscle", so take this entire thread as another data point that you are not communicating your thoughts clearly or precisely.
>In any case, scales can lie too.
Can you stop with low effort bait please? Is it really worth our combined time to draw people in with silly statements about miscalibrated scales?
You're changing the subject now. The article wasn't about measuring health in general, it was about weight control. And the line of discussion that we're on is a good measure of weight. A good measure of weight is different from a good measure of health. I'm not interested in discussing the latter.
Also, you seem to have missed the point of me mentioning measuring hair, which wasn't to say it was a good idea, but to say how you could do it if you wanted to. You're getting hung up on why anyone would want to do that, which misses the point.
You're right, it isn't complicated. The beauty of a true good measure (ie, a scale or other measuring device) is that you can track actual discrete progress over a very short time span. If you didn't know your hair grew, could you track the amount that it grows in 24 hours by looking in a mirror? With a measuring tool, you can. That's a good measure. Compared to it, looking in the mirror is a poor measure. That's why scientists use measuring devices when trying to track quantities, instead of "eyeballing" it and claiming its good enough because they noticed some change.
It sounds like you want someone to agree with you that your memory plus your subjective view of yourself is a "good measure" of progress, despite the existence of scales and measuring tapes, which dwarf your subjective memories in terms of reliability and accuracy. We'll just have to agree to disagree.
Does anyone know the history of noncompetes? It seems like a case of Tacit Collusion[1]. But if there is no competitive advantage to the noncompete, how did it catch on?
Yes, it's called evolution. Software evolves as well as scales. Scaling is architected growth, evolution is unarchitected growth. (Sometimes scaling results in unarchitected growth, if the architecture was not reasonable.) There are many patterns for handling evolution, but they almost always involve a pattern outside of the existing architecture (a super architecture) to lean on for support. In my opinion, codifying and optimizing these super architecture patterns is one of the highest goals in software engineering because they allow for less error prone evolutions.
Honestly I think that you are trolling me, but I am trying to assume good faith. In your first reply to me, you said "[an explanation is that] systemic racism in education in the past has left black applicants less well prepared than other applicants." Now you are saying this is a shaky hypothesis and has minimal impact on the applications?
It's not crazy to assume that people with less resources and support will do worse. The logic is sound and its in fact the standard explanation (along with discrimination) as to why marginalized groups do worse.
If they knew that blacks have less resources and are therefore on average worse applicants as a result, then they knew that a bad application statistically meant a black applicant. So by not picking the worse application, they were in effect being statistically racist.
>>The study adds to a growing body of knowledge about what anonymizing funding applications can—and can’t—do. The National Institutes of Health (NIH), for example, experimented with concealing information about applicants’ identities from peer reviewers as part of an effort to understand why Black applicants are 35% less likely than white researchers to receive grants. The results were mixed—Black applicants’ scores did not improve, but those for white researchers decreased—and some experts were skeptical about whether the reviewers truly didn’t know the identities of the applicants.
Humans are such amazing pattern matchers. Even with the identities concealed, the reviewers were able to figure out who was black and discriminate against them in favor of white applicants. This level of systemic racism is absolutely dumbfounding.
And people are flabbergasted at the healthy skepticism towards pharmaceutical companies, when they literally treat people like expendable cattle at every chance they get.
"insurmountable challenges." That rules out funding and community engagement, since both of those can be improved. The problem must have been systemic...
Were there any warning signs of this decision? I quick look at their blog[1] doesn't raise any red flags. Who knew about this? I'm sure the community would have kept them going if they had known. Is there more to the story?
I suspect we'll discover/invent an IR (intermediate representation) that behaves like RAG in that it primes the LLM to produce a specific bit of knowledge/facts, but the IR is a lot less like normal english, and more like a strange pseudo-english.
>In any case, scales can lie too.
Can you stop with low effort bait please? Is it really worth our combined time to draw people in with silly statements about miscalibrated scales?