Jeriko

Honest question: is just collecting and analyzing data not considered a use case for AI? I mean sure some businesses can be essentially run by a recommender system or something. But it also seems valuable if you can do something like use NLP to get better quantification of customer feedback, which is really just collecting and analyzing data for a human to use later.

There is a ton of programming in research, and almost all academic code is written by the researchers, whether they are in biology, physics, humanities, whatever. This has some of the drawbacks you mention, but the solution is not so obvious.

First, almost all academic code is really simple from a software engineering perspective, but really complex from a subject matter perspective. Having a deep understanding of both the data and the relevant hypotheses is critical, and is often actually helped by writing the code yourself. Trying to communicate every feature requirement perfectly, and making sure every assumption is met, to a third party CS person might be possible but is definitely non-trivial.

Second, most of these projects are one-time use (code up some project over a few months, write a paper, never touch again), and so spending a ton of time + money making it robust and efficient is not really worth it. For things like open source tools that are expected to be used by a lot of people it's much more feasible to get engineers involved. The Chan Zuckerberg initiative is actually funding a program that essentially does this [1].

[1]https://chanzuckerberg.com/rfa/essential-open-source-softwar...

0.03 is the odds ratio (a measure of effect size, an odds ratio of 1 would mean the treatment and control arms had the same rate of ICU admittance). The p value is 0.08 from here "CI:-0.30 - 0.03 p:0.08." which captures the likelihood the treatment had an effect.

That said, just looking at p values and applying a cutoff at 0.05 is pretty bad practice that is getting a lot of heat thanks to the replication crisis (does it make sense to behave as though p=0.08 is not true and something at p=0.049 is true? almost certainly not). If you get a value in this range and a huge effect size then it's a really good idea to repeat the experiment with way more data. It's also a common stats error to act as though p>0.05 is the same as knowing something DOES NOT work, all you can say is this specific study wasn't able to show that it does work with 95% confidence.

"Internet-connected smart vehicles aren't a mature technology. Not in the sense of this being the win2k era of that tech, but that our assumptions about how to build these systems might be fundamentally wrong. I don't know if it will ever be safe enough to trust human lives to it."

I often hear this kind of thing and am really surprised by it. Specifically for the tech in vehicles example, it seems like a real double standard. Around 37,000 people in the US die in car accidents every year[1]. That's 100 people a DAY. There is a huge cost to not adopting new safety measures, even if it depends on immature tech, and that needs to be factored against the potential new unknown risks.

Driving to work is almost certainly the riskiest thing you do most days. I find it plausible that people 50 years from now will think that the cars we drove before 2010 were unconscionable death traps.

[1] https://en.wikipedia.org/wiki/Motor_vehicle_fatality_rate_in...

If you are trying to use it to treat mild depression specifically, a placebo effect is great! Placebo effects get a bad rap. If something improves your subjective quality of life that should be considered a win, especially for depression.

Honestly the best thing vitamin D has going for it is being super inexpensive and safe. Because of that you can supplement vitamin D for almost any reason and expect to see at worst a very small cost (someone up thread said 3 cents a day, which seems in the ball park) and at best a significant subjective benefit (even if it's from a placebo effect).

It is all mind games. I feel like you are leaning on a negative association to the phrase rather than making an argument. If you say "I don't feel like I want to do a thing, but doing that thing will minimise anxiety and depression in the long run, therefore I do want to do the thing" that is definitely a mind game. It is also a good way to be happier and more satisfied in life.

I got the impression that the author was arguing exactly against this kind of equivalency. People can be unusual and also successful. This doesn't mean that being unusual is a valid predictor of success. Also that its pretty ironic for something that would be seen as a potential red flag for someone making a relatively low risk decision (i.e. hiring a branch manager) would suddenly be viewed as an asset by someone making multi-million dollar investments. I'm not sure I agree with the premise that everyone is blinded by some "great man" theory of innovation except for we street-wise low-middle class. But I feel the point about the double standard is valid.