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- Mar 23, 2017
For number 3 yes there is bias in figure skating - so much that I did my masters thesis on it!
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I am afraid people are not going to like this, sorry about this, but since this can of worms is opened, here goes. Perhaps you didn't watch figure skating before IJS, but it was not only watchable, it was mega popular. The way US TV handled this was they hired good commentators, among them Dick Button, Peggy Fleming and others. Dick was very good at explaining what skaters did and how it mattered. They did a review of required elements, types of jumps, compulsory dance patterns, etc. with visuals, so that the audience would understand what was going to happen. They also did previews on athletes. It was common for a reporter team to fly to other countries to film documentaries about contenders before big events, so the audience would be introduced to the skaters, their lives, training condition, struggles, etc. They would make a show out of it. There were also pro championships where retired skaters did all sorts of creative stuff, and those were quite popular for a while. There were shows that aired on TV in prime time, in fact, I'd never have survived through my first hearing of Holst's Planets suite without this movie: https://www.youtube.com/watch?v=LnQDDH7rF5o&list=LL&index=261. My first intro to the full story of Carmen was through the Carmen on Ice show. There was no youtube back then, but nowadays you can learn quite a bit about classical music and cinematography by watching historic figure skating.Personally, I think figure skating judging system before IJS was a joke and the sport was unwatchable because of it. And I didn't watch it, lol. Now it's clear what they are going for, and once AI is there, TES would be solid.
Can we read it online?For number 3 yes there is bias in figure skating - so much that I did my masters thesis on it!
My first intro to the full story of Carmen was through the Carmen on Ice show. There was no youtube back then, but nowadays you can learn quite a bit about classical music and cinematography by watching historic figure skating.
I can remember back when the only time children in the U.S. would hear any classical music at all was either a figure skating show or a Buggs Bunny cartoon. Is it the Achilles Heel or is it the last heroic stand of the old guard straddling the highway of progress and shouting, "STOP!"However, the PCS remains the Achilles' heel of the system.
The way US TV handled this was they hired good commentators, among them Dick Button, Peggy Fleming and others. Dick was very good at explaining what skaters did and how it mattered. They did a review of required elements, types of jumps, etc. so that the audience would understand what was happening. They also did previews on athletes. It was common for a reporter team to fly to other countries to film documentaries about contenders before big events, so the audience would be introduced to the skaters, their lives, training condition, struggles, etc. They would make a show out of it.
That's an interesting point. In American football, for instance, there is instant replay to clarify whether the runner stpped out of bounds or not, whether the defender illegally interfered with someone trying to catch the ball, whether the player's knee touched down before he lunged forward for a first down, etc., etc. Sometimes this technology sheds light, sometimes it is still unclear after many slow-motion replays from different angles.Couple of thoughts.
I would love AI to qualify jumps, spins, and ice coverage. For the average viewer some people don’t see UR and think they don’t matter since it doesn’t detract from the skating, but it is a sports and how many revolutions you do in the AIR matters.
Thank you for a thoughtful perspective.*Wh0le post*
This is not AI, it's slow-motion replay, as far as I understand you. AI is something you give an input, e.g. a phrase in one language or a footage of a jump, and receive an output, e.g. the same phrase in another language or the jump label. It's designed to emulate neuronal connections in the brain, in other words identify some patterns in the data the way brains supposedly do it (from math point of view it is just a function approximation, well, "just a function" is too simple, it is a complicated non-linear function approximator). This is done by training AI, i.e. solving a large optimization problem, in which parameters of a neural net (usually millions of them) are optimized and tuned so that for certain inputs for which the outputs are known, AI produces those known outputs. These inputs with known outputs are called training data, and typically one needs a lot of training data. AI is only as good as the training data it learns from. If there is one camera, and tech.panels mislabel the footage on that one camera because they can't properly see, and these data are then used to train an AI, mislabelling will propagate into the AI. In other words, if the data have mistakes, AI will learn them. Garbage in, garbage out. AI is effective at solving certain problems, but it has limitations, and those are relatively well understood and formulated, although there is no satisfactory general theory. For something as complex as a spin or a step sequence, for example, one would need a lot of good quality training data and smart net architectures, perhaps recurrent, because it's more of a time series. Each time the rules are changed and broken into, the data will have to be regenerated, relabelled, the net retrained, retested, etc. I am not sure if it's worth it. It is not an easy problem.That's an interesting point. In American football, for instance there is instant replay to clarify whether the runner stpped out of bounds or not, whether the defender illegally interfered with someone trying to catch the ball, whether the player's knee touched down before he lunged forward for a first down, etc., etc. Sometimes this technology sheds light, sometimes it is still unclear after many slow-motion replays from different angle.
Thank you for a flashback!Watch this from Euros 1996 and tell me this was not watchable:
Awesome jumping technique, nice Chopiniana music although sticking Paganini in the middle of Nocturne no. 2 is a bad idea- YouTube
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Everything old is new again.How many talent shows and reality shows were there in the eighties and early nineties? And how many were there at 2000?
Roman doesn't use the bonus in the sp and one of his programs had the three spins as the final element.
The good old days when spins were valued and spotlighted as the principal crowd pleasers.Thank you for that detailed explanation. I think that the primary obstacle for figure skating would be the research and development costs. The second would be the challenge in addressing esthetic considerations.This is not AI, it's slow-motion replay, as far as I understand you. AI is ...
A robot PCS judge sounds like a tool for a primitive children's game when compared to this oneMy favorite artificial gamester came out last year when the University of Tokyo announced Janken, a robot that plays Rock, Paper, Scissors and has a 100% winning percentage against humans. It works by scanning the human opponent and analyzing tiny muscular twitches that enable it to anticipate what sign the human is preparing to throw, and then responds with it's robot hand with the winning response in a thousandth of a second.![]()
I am actually not sure how they teach chess programs, but I think Janken is AI. It's not such a hard task to train a robot to scan hand muscles and predict what the hand is going to do next, and it's very fast when trained. This doesn't mean the robot will be able to sit an anatomy exam: I think it's based on image analysis. If one wants it to predict another response, e.g. related to leg muscles, they may have to retrain it.Thank you for that detailed explanation. I think that the primary obstacle for figure skating would be the research and development costs. The second would be the challenge in addressing esthetic considerations.
I actually have a little experience with chess-playing programs. Back in the glory days of the 1990s the race was on to produce a computer program that could beat the human world champion (Gary Kasparov at that time). Their strategy was to work on speeding up the hardware until it could generate and evaluate 100,000,000 or so positions per second. No luck. Finally, if you can't beat 'em, loin 'em. IBM invited Kasparov to join their team and the first thing he told them was that they didn't know anything about chess and so Deep Blue was evaluating certain features as negative when they were actually posirive, etc. Very quickly Dep Blue was baeting up on Kasparov and succeeding world champions with regularity.
Mu favorite artificial gamester came out last year when the University of Tokyo announced Janken, a robot that plays Rock, Paper, Scissors and has a 100% winning percentage against humans. It works by scanning the human opponent and analyzing tiny muscular twitches that enable it to anticipate what sign the human is preparing to throw, and then responds with it's robot hand with the winning response in a thousandth of a second.
(Not exact;tly, AI, though.)
I believe that there are several different approaches that people experiment with. The easiest method would be just to let the computer make the move that it likes best, then if it wins the game that type of move in that type of position is advanced, and if the computer loses, then that move is demoted.I am actually not sure how they teach chess programs...