After much sleuthing, I finally found the original comment. It has a link to images of the built out project which is nice and in the replies the OP posts links to the GitHub gist. If any interest in my react version I can make that public too, just know it was quick and dirty but does work.
20 years old PDF not downloadable publicly apparently.
They choose manually "please allow 7 working days for a response" whether they want to approve you.
Strange for something developed on public funds and sitting behind a wall.
They would likely benefit more (on their personal careers) if they let an open access and push researchers to build machine-learning models on their structure.
You have to wall it off, or you risk polluting your data. Like all of the people that think cats are dumb will start uploading content of their dogs. or worse. Also, you have to be able to filter your input so it matches your results. I thought this is like research 101 level stuff
Might be worth considering that in a specific example or context this may make sense, but zoom out further you will find that goals are solutions and solutions are goals.
It's a hierarchy, as Paul referred to as a "tree".
Each node in the acyclic graph is connected to a "why" node above it (goal) and a "how" node below it (solution).
OKRs reflect this in an organization.
People make decisions based on their values hierarchy, implicit or explicit.
If this isn't easy to follow maybe an example will help...
Let's say I have a goal of "provide reliable shelter for my family", the solution may be to "buy a house". Buying a house is also a goal, which maybe is slightly out of reach. So my solution is to "save a large portion of my income" and "secure a high paying job", these are also goals. The solution to saving may be a fintech app, discipline, good communication with my spouse, etc.. every solution is a goal with its own solutions and you can follow this tree down until you get into really specific motor tasks like taking a credit card out of a wallet or opening a door or turning the key to start a car.
This would be interesting, feel free to email me if you get stuck. If you had a camera at eye level, you could try to train it on recognizing the player jersey numbers.
Facial recognition would be better. Don’t forget that canonically in Mighty Ducks D2 Goldberg and Russ switched jerseys so that Russ could get his infamous “Knuckle Puck” shot off undisputed because everyone thought the puck was passed to Goldberg until the mask came off. So the ML training on jerseys would have missed this critical moment and potentially assigned the score to Goldberg, when really it was Russ (wearing Goldberg’s jersey) who should have gotten the credit.
One might argue that this sort of thing rarely happens so it’s not worth doing more complex facial recognition vis a vis Jersey numbering. But I say that while it may be rare, when it does happen it’s a major event, so no complexity should be spared to ensure we capture it accurately.
I would have multiple camera footage. One gopro would be just be a wide-angle of the bench behind the players, another would be on the game clock, and additional ones would be on-ice footage. Typically my gopro set-up has been behind the goalie (https://www.youtube.com/watch?v=CCavsdzc-OY) and the rinks have Livebarn feeds (here's one on my YT from 2018 https://www.youtube.com/watch?v=5WEE9y4cAHg) but there are challenges in quality abound.
He seems to be already fully booked until the 13th of August, must have been really successful, or maybe just the result of the exposure on HN? Hopefully people aren't booking spots just to troll.
