A modern programming language that makes developers happier.

kotlinlang

&gt; Frankfurt: "One of the most salient features of our culture is that there is so much bullshit. Everyone knows this. Each of us contributes his share. But we tend to take the situation for granted."

Hello, after listening to the talks from <@U0HUJ25V1> and others,
I wonder if data-science could help protect us from Bullshit.
The basic idea would be to build a Bullshit/Non-bullshit classifier much like we do for Spam.
In practice we would build a Browser extension because that's where most bullshit come to us.
Initially all articles with a large amount of buzzwords inside Gartner's "Peak of Inflated Expectations" would be marked as "Bullshit".
If the content was actually a good take-down of the bullshit,
we would say so to the browser extension, which would learn from it by virtue of Bayesian Interference.

Could that work? Why wouldn't it?
It's a genuine question, I'm new to data-science.

This Bullshit/Non-bullshit classifier should be installed on the most famous search engines' side to filter out Bullshit. So it is not possible

But can we use data science to find the B.S. in "data science"? :trollface:

In all seriousness though, thanks for watching and glad it got you thinking. However, you got to be careful giving algorithms too much credit.  It's important to remember that machines have no cognizance  or real learning and awareness. They are just pattern-finding machines and this can create some underwhelming results amidst great hype. <http://tomstechnicalblog.blogspot.com/2018/12/is-deep-learning-already-hitting-its.html>

Sure, you can probably train a Bayesian categorizer that is triggered by BS buzzwords like "synergy", "AI", "blockchain", "revolutionary", "game-changing", etc. But this can easily backfire and have inadvertent consequences.