I only looked at the stills before, but I agree that the animation really is an impressive achievment. However, there is nothing below the surface here. The stick figure takes a few steps and lifts its hat. The medium is impressive, the art not so much.
That validation already happens. I worked as a scientist for a decade before I went to industry, getting grants was a constant struggle - agencies and politicians don't just give out money randomly. One can argue that these processes aren't perfect and that some money gets wasted, and that it makes for weird incentives - but that's a whole other debate.
It sure looks pretty. Both the site and the machine. And the tech sounds interesting too! There are a bunch of fabulous devices out there though, not sure how to understand just how great this is, some third party validation would help me. Anyhow, excellent presentation!
Scientific work is too often challenged with this kind of question. If all you care about is results you know will happen you will never discover anything you don't allready know.
Actually, I used to love reading, but the last years I've felt exactly like I had no energy to read any more, and have switched to audiobooks and podcasts. I then checked my eyes, and it turns out I need glasses. So as you get older, check your eyes!
If the track is known, then from the context of this and other things you like, you would still reach useful recommendations in not too many clicks. Track level would be interesting, but is also harder as data is a lot sparser making it harder to build reliable recommendations.
I tried it before, and I just tried it again. It is great. I just came back from RecSys a large conference on recommender systems. Researchers and companies like Spotify, Amazon music, Deezer gave lots of presentation. However, nothing they showed were so immediately useful as this. Awesome service!
Sure, there is relief and a feeling this was necessary and for the best - but also broken dreams, failing social expectations, broken friendships. I guess I mostly agree with you, but I think such traumas make for unpleasant experiences...
A short, worthwhile read. A mostly irrelvant comment: I think that when writing, it is fine to use ≠ and =. We can be reasonably sure that = means equals and not assignment.
Shorts are terrible, they suck you in and 15 mindless videos later you can't even remember what you've seen. I almost only want to search for videos or check subscriptions, the main page with recommendations is full of traps...
But if you aren't using VSCode (we exist) this looks quite useful. That said, I just open my jupyter sessions in firefox and am pretty happy with that...
Getting inputs and publishing results very often means working with databases, and that choice is often not up to the data scientist. What you do in between is another story, but at least some db skills are essential…
I use poetry for dependency management and pyenv-virtual for venvs. I found this set up to work better for me than poetry's venv. Maybe things have changed, and I'm out of date on that, but now I'm just used to this setup....
Did you write many papers in experimental particle physics? I did, and in my experience, peer review was not a joke. And results from different experiments really are used as validation. Citations are not just used as a metric, but helps to leverage prior work.