5 ms·

Actually working in the field, I hate stuff like this so much. Like, it's a cool project, but the reporting and marketing and hype is all so fucking awful. Imag

Actually working in the field, I hate stuff like this so much. Like, it's a cool project, but the reporting and marketing and hype is all so fucking awful. Imagine if a person had done this instead of a model: > [I] Completed Beethoven's Unfinished Tenth Symphony > Now, thanks to [my] work, Beethoven’s vision will come to life. And so on. Your reaction would probably be "no, this isn't Beethoven's vision, it's some random person's vision." In this case, it's that much more egregious because it's not written by a journalist through a game of telephone. It's actually written in this breathless magical way by the person who built the model. There needs to be a new fallacy or something: "Appeal to AI." Because otherwise we treat "AI completed Beethoven's vision" as somehow more True or Correct than "I completed Beethoven's vision." And it's much more dangerous when we apply that free pass to things like "AI predicts recidivism." Is it just riding off of how people perceive mathematical correctness or something?

TheDudeMan 5y ago
Not working in the field at all, I completely agree! Well said.
belval 5y ago
What pains me is that these are the exact articles that make people outside of the field say "AI fails to deliver", "AI is just a bunch of ifs" or "AI is just snake oil the new winter is coming". Most actually interesting ML/DL projects are not stuff that the public directly interacts with so you end up with a ton of inconsequential stuff like this article and real progress is just not visible.
deleted 5y ago
[deleted]
shoto_io 5y ago
I agree that the media is overhyping AI without fully comprehending what it can and cannot do. At the same time, I believe your criticism is a little off-base. Sure, it's hard to tell it apart from how a person would have finished the symphony. That, however, is precisely the point. That's what's so fascinating about it.
nabla9 5y ago
This thing has been going on for a long time. First time some seriously tired to make "computer composer", maybe 1960s?, it passed double blind test flying colors. Professional musicians declared that computer could never compose something like this" about computer made music. Using learning and using generative grammars has been used to imitate different jazz musicians in 90s. Some modern composers use computers to generate music, they just tweak parameters until it is how they like it. It's still human made.
williamtrask 5y ago
I couldn’t agree more. We have very little basis to judge whether the algorithm did Beethoven justice and so all the celebration is really for a non-event. A computer wrote down notes after other notes. Maybe they sound not too dissonant. That’s not news.
- macrolocal 5y ago
  This statement feels too strong. An algorithm completed Beethoven's tenth symphony, and it did a passable job, maybe better than what a graduate student could achieve. That's exciting for tool-assisted composition, even if it tells us nothing about Beethoven's historical vision for the symphony.
  - moviewise 5y ago
    Agree! This is great and exciting. When people learn to play music composed by someone else, they are experiencing/living to some degree the mind of the composer. And music can fairly be described as sets of repeatable patterns. So if AI can take the entirety of a composer's life's work and discern key patterns, replicate them even, then some aspect of the mind of the composer is reborn. This is immortality. A dedicated grad student could probably do it for one composer; AI can do it for ALL composers. And humanity is all the better for it :)
kovek 5y ago
I don’t think a computer could know what the artist intended to create. I wonder, how accurate can the computer be at predicting the next note (the first note from the uncompleted section)? Is that an interesting question?
- kace91 5y ago
  That can be proven though. Keep a work by the author out of the training phase, then see if the model can predict it from an incomplete version. Otherwise...
- 6gvONxR4sf7o 5y ago
  Yes, it's a very interesting question! This how many language models work. You might be interested in reading about a metric called perplexity. [0] The major caveat is that it's invalid to evaluate on completed, polished work, and then extrapolate to what's practically a napkin note. Another important note about it is that some next token predictions are harder than others. For example, try predicting the next word (the _____) in each of these: > I really love this Dr Seuss Book called "The Cat in the _____ versus > I really love this Dr Seuss book called "The Cat in the Hat." _______ [0] https://en.wikipedia.org/wiki/Perplexity https://en.wikipedia.org/wiki/Perplexity
  - noduerme 5y ago
    You could also make a model which always responds with the word "hat". If the second sentence is the "creative" portion, it would then prove 100% accurate on your in-sample data, and the next sentence could be "Hat, hat hat." And then people could say, see, this brilliant AI speaks like Dr Seuss! You could also just string a bunch of random words together and build a model that learned to predict them with total accuracy. Of course if the words are truly random, it would have basically no chance of predicting the next random word from outside the learning set. The conceit really is that some model got enough important, critical contextual information from the partial symphony to be able to predict with greater than random probability the next portion. On a micro level, one note at a time, maybe. But every note further into the unknown is predicated on the previous ones, so on a macro level it's absurd to think it's "predicting" anything.
LuisMondragon 5y ago
> There needs to be a new fallacy or something: "Appeal to AI." I applaud and welcome this idea

TheDudeMan 5y ago

Not working in the field at all, I completely agree! Well said.

belval 5y ago

What pains me is that these are the exact articles that make people outside of the field say "AI fails to deliver", "AI is just a bunch of ifs" or "AI is just snake oil the new winter is coming". Most actually interesting ML/DL projects are not stuff that the public directly interacts with so you end up with a ton of inconsequential stuff like this article and real progress is just not visible.

deleted 5y ago

[deleted]

shoto_io 5y ago

I agree that the media is overhyping AI without fully comprehending what it can and cannot do. At the same time, I believe your criticism is a little off-base. Sure, it's hard to tell it apart from how a person would have finished the symphony. That, however, is precisely the point. That's what's so fascinating about it.

nabla9 5y ago

This thing has been going on for a long time. First time some seriously tired to make "computer composer", maybe 1960s?, it passed double blind test flying colors. Professional musicians declared that computer could never compose something like this" about computer made music. Using learning and using generative grammars has been used to imitate different jazz musicians in 90s. Some modern composers use computers to generate music, they just tweak parameters until it is how they like it. It's still human made.

williamtrask 5y ago

I couldn’t agree more. We have very little basis to judge whether the algorithm did Beethoven justice and so all the celebration is really for a non-event. A computer wrote down notes after other notes. Maybe they sound not too dissonant. That’s not news.

macrolocal 5y ago
This statement feels too strong. An algorithm completed Beethoven's tenth symphony, and it did a passable job, maybe better than what a graduate student could achieve. That's exciting for tool-assisted composition, even if it tells us nothing about Beethoven's historical vision for the symphony.
- moviewise 5y ago
  Agree! This is great and exciting. When people learn to play music composed by someone else, they are experiencing/living to some degree the mind of the composer. And music can fairly be described as sets of repeatable patterns. So if AI can take the entirety of a composer's life's work and discern key patterns, replicate them even, then some aspect of the mind of the composer is reborn. This is immortality. A dedicated grad student could probably do it for one composer; AI can do it for ALL composers. And humanity is all the better for it :)

kovek 5y ago

I don’t think a computer could know what the artist intended to create. I wonder, how accurate can the computer be at predicting the next note (the first note from the uncompleted section)? Is that an interesting question?

kace91 5y ago
That can be proven though. Keep a work by the author out of the training phase, then see if the model can predict it from an incomplete version. Otherwise...
6gvONxR4sf7o 5y ago
Yes, it's a very interesting question! This how many language models work. You might be interested in reading about a metric called perplexity. [0] The major caveat is that it's invalid to evaluate on completed, polished work, and then extrapolate to what's practically a napkin note. Another important note about it is that some next token predictions are harder than others. For example, try predicting the next word (the _____) in each of these: > I really love this Dr Seuss Book called "The Cat in the _____ versus > I really love this Dr Seuss book called "The Cat in the Hat." _______ [0] https://en.wikipedia.org/wiki/Perplexity https://en.wikipedia.org/wiki/Perplexity
- noduerme 5y ago
  You could also make a model which always responds with the word "hat". If the second sentence is the "creative" portion, it would then prove 100% accurate on your in-sample data, and the next sentence could be "Hat, hat hat." And then people could say, see, this brilliant AI speaks like Dr Seuss! You could also just string a bunch of random words together and build a model that learned to predict them with total accuracy. Of course if the words are truly random, it would have basically no chance of predicting the next random word from outside the learning set. The conceit really is that some model got enough important, critical contextual information from the partial symphony to be able to predict with greater than random probability the next portion. On a micro level, one note at a time, maybe. But every note further into the unknown is predicated on the previous ones, so on a macro level it's absurd to think it's "predicting" anything.

LuisMondragon 5y ago

> There needs to be a new fallacy or something: "Appeal to AI." I applaud and welcome this idea