Rise of the Music Machines
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How did Google’s Music LM prototype train on 5,500 YouTube clips to generate new music?
A show where philosophy and reality meet.
From Last year, ten musicians got a call from Google. To help them do one of the most mind numbing jobs you could imagine. The company had five and a half thousand clips of music from YouTube. Music that sounded like this
and this. And this take me
all the way. And the job was to listen to every single one of these clips and describe them in words. Like take this example.
Let the music play on play on feeling the vibe, you know I'm feeling. Enjoy.
The description one of these musicians came up with was, this is a remix of an RV soul piece. There's a male vocal singing in a laid back manner, joined by an auto-tuned male vocal. The atmosphere of the piece is groovy and there's a feel-good aura to it. This piece could be used in the soundtrack of a sitcom. Well, they finished the job, and I crunched the numbers. These ten people listened to thirty-eight straight days worth of music. Ninety two hours each. Typing out their descriptions one by one before moving on to the next clip. They use the total of three hundred and seventy thousand words to describe all of the clips. Google ran the music and the words through a deep learning model, with a goal of figuring out what words correlate with what musical sounds.
The outcome, if the project went well, would be the ability for any of us to say anything to Google. And it would generate brand new music based on our instructions. And at the end of January in twenty twenty three. It released the prototype. Music LM A language to music generator. Okay Google, use AI to generate techno accordion music.
Okay, using AI to generate techno music with an accordion.
Okay Google, play me music from a club in the 1950s.
Okay, using AI to generate music from a club in the 1950s.
Okay, that was really bizarre. I don't know what to say about that one.
I'm not sure what that was supposed to be. This
is Robin James, a philosopher of music.
Yeah, I feel like it was trying to combine like Jerry Lee Lewis and Frank Sinatra.
Right.
Okay.
Okay, Google, now play music from a club in the sixties.
Maybe there's a little like surf guitar, maybe there's a little like Serge Gainsberg. Yeah. I mean
it's AI, you gotta remember, like no no human being did this, right?
Well yeah, it just shows that like it's being trained on, you know, these sort of broad data sets.
From Slate, this is Hi-Fi Nation, philosophy in story form. Recording from Princeton University, here's Barry Lamb. I actually like it.
There's nothing particularly aesthetically interesting to me about AI generated music that's a perfect copy of human-made music. Because why would I listen to that? I'll just listen to the human stuff. But glitchy, weird, robotic stuff that doesn't sound like anything humans would make? That's fun to hear and to think about.
Revelation
That was accordion rap, according to Google Music LM. I wish AI music researchers would focus on that more. What you might call distinctively robot glitch aesthetics. Well today's show is about machine generated music. There isn't just one thing that is AI music. There are hundreds of different projects going on simultaneously that are trying to use machines to analyze, recombine, and organize sounds into something we recognize as musical. I'm nowhere near an AI optimist about most AI technologies today. But with music, I think it has the promise to unlock a lot of musical potential from humans. So today we're going to look at three different kinds of AI music projects. And I'm going to do something rare for Hi-Fi Nation.
I'm actually going to give you my take about each project. Вот тай мінфер футюра музикал креативні.
So they got me in the lights and blasted the whip. Thought they laid me to rest, but the bag wasn't zip. This is the story of what really went down in ninety-six.
If the music industrial complex doesn't sue it out of existence first.
They made a tribute to me in the cemetery, but I was never dead and buried. I was
This is that night in Vegas. Featuring a deepfake AI Tupakshakura.
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Chapters
6 chapters
1
How did Google’s Music LM prototype train on 5,500 YouTube clips to generate new music?
0:03–10:34
2
What are vocal emulators and how are they used to deep‑fake singers like Tupac or Kanye?
10:34–18:31
3
How do AI‑generated composition tools (Music LM, Jukebox, MuseNet) turn text prompts into music?
18:31–24:33
4
What do philosophers Robin James and Theodore Gracyk say about AI’s creativity versus human creativity?
24:33–34:49
5
Why are AI‑generated “glitch” or “robotic” sounds considered more interesting than perfect copies?
34:49–45:59
6
How does musical improvisation technology work and why is it seen as a pure form of creativity?
45:59–50:00