> Probably. Most managers would also argue that because you're so great with machines, you'll surely be even greater at managing others who are supposed to be great with machines. Does that make sense? No. Do managers and executives think like this anyways? Yes.
I'd say the opposite is true. In modern management theory, the value of domain knowledge for managers is severely undervalued.
> the value of domain knowledge for managers is severely undervalued
Sure, but you can always pick that up as you learn how things work. It's a bit harder to do that in engineering as it requires years of experience with your craft.
Just like a manager just starting out isn't gonna have the right intuition and hunches until some years of experience, you can't just "pick that up", that is the expertise, unlike domain knowledge.
In The Art of Learning, Joshua Waitzkin talks about how this was a strategy for him in tournaments as a child as well. While most other players were focusing on opening theory, he focused on end game and understanding how to use the different pieces. Then, by going with unorthodox openings, he could easily bring most players outside of their comfort zone where they started making mistakes.
...and Sam Altman once again posts a response including uppercase, similar to when Ilya left. It's like he wants to let everyone know that he didn't actually care enough to write it himself but just asked chatGPT to write something for him.
Microsoft having a lot of partnerships with the military industry, and now also getting their hands on all of OpenAIs technology. In parallel, OpenAI stops sharing information about their models, making it harder for other countries to copy. Add to that the chip ban. Hm...?
Impressive how this post references back to a post six years before as the starting point. Shows the value of tenacity and sticking with it, as learning compounds over time.
What's weirder to me is how I can't remember even seeing them advertised within Spotify. I know there are some really need content Spotify is producing, but the ones I'm aware off I've generally found by accident somewhere on the Internet, never through their own UI.
I have listened to exactly 1/2 of one episode of a podcast on Spotify and now have to actively navigate away from the main page of the app if I want to do what I do the other ~99.9% of the time, which is listen to one of a handful of playlists or try to find other similar music.
Rather than wanting to confirm priors, I believe this usually is a problem with neither the PM nor the data scientist ensuring that the problem formulation is good enough before diving in. I.e., what data would be needed to actually test the hypothesis? Do we have that data or not? Is the hypothesis even formulated in a way to be falsified in theory?
I've seen so many analysis tasks where data scientists without questioning went away for a few weeks to crunch data and come back with some random graphs and statistics that are completely useless as decision support.
You're overthinking it. Executives and managers quite literally want to see data that confirms their existing convictions and beliefs so they can act on those beliefs under the guise of it being "data-driven".
The Swedish island Gotland is of central strategic importance in the baltic sea, so in case of a conflict either NATO or Russia would seize control of the island with or without Sweden’s consent.
Not to mention to secure against a Russian invasion of the mainland, of course…
Well, human thinking relies on prior models/filters for understanding the world as well so that would invalidate us as having general intelligence too?
Human thinking includes building new models/filters for understanding the world, not just applying old ones. And that isn't used for learning, we do it all the time when solving any kind of challenging problem or even for simple problems like trying to recognize a face. Computer models might never compete with human performance unless they can learn how to solve a problem as it is solving it, because that is what humans do.
There's this big mass of models. And it's got all kinds of sections. Special sections that we learn about in school. Special sections called "science". Sections that we invent ourselves. Sections that we inherit from our parents, religion, etc. It's partially biological. Partially cultural. A massive library of models, mostly inherited.
You move in relationship with the mass in different ways.
You can create new models. That's what basic science is. Extending the edge of the mass. Naming the nameless.
You can operate freely from the mass. Creating your own models or maybe operating model-less. Artists, mystics, weirdos.
You can operate completely within the mass. Never really contending with unmodelled reality. The map and territory become one. Like in a videogame. I think that's the most popular way.
Actually, I remember reading an article from someone at Spotify who argued that the economics in that situation would end up hurting small artists and favour Taylor Swift much more than the current model.
As an example; big music aficionados might listen to 75 different bands in a month, which will mean 1% of their subscription money will go to each of them.
The large majority of music listeners though listen to 5-10 big-name artists only.
Distributing money fairly after what each premium subscriber listens too thus would end up favouring the big name artists much more than the current model they have.
I have no way of knowing whether this is true, but thought it was an interesting perspective...
It doesn't really matter. Spotify just doesn't pay that much per listen in the first place. There is only so much money you can squeeze out of a $10 subscription.
I think a tipping system would be a better solution, preferably one where 100% of the tip goes to the artist. Spotify still gets their cut from subs and I get to make sure I support the artists I actually listen to.
Clarity edit: I mean a tipping system on top of the existing system.
I'd say the opposite is true. In modern management theory, the value of domain knowledge for managers is severely undervalued.
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