Doug Eck
Senior Research Director, Google DeepMind · 1 appearance on the record.
computed by AI from the episodes · how this works → · full disclaimer →
scientistexecutiveacademic@douglas_eck ↗research.google/people/105574 ↗
Douglas Eck is best known for creating Google's Project Magenta, an initiative exploring machine learning's role in music and art generation. At Google DeepMind, he co-leads generative media research following an earlier career as a machine learning faculty member at the Université de Montréal's MILA lab.
22statements → 6claims → 5claims resolved → 100%fully supported → 3.64/5average certainty → 1.68/5average debate potential →
5 supported 0 partly supported 0 contradicted 1 not checkable as stated how the 6 claims stand · each chip opens the sources
1 prediction · 5 assertions · 3 opinions · 9 insights · 4 disclosures · every statement was checked. The prediction and assertions are the 6 claims: statements the public record can support or contradict. 5 are resolved, and 1 names no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.
The record, in short
What the tape says about how Doug argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.
Expressed certainty vs assessment result
weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet
Everything Doug Eck said on the Y Combinator Startup Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →
Eck: Alex Graves advanced LSTMs more than anyone, including its creator
“Among the three of us, by far, Alex Graves has done the most with LSTM. So he continued, after he finished his PhD, and he continued doggedly to try to understand how recurrent neural networks worked, how to train them, and how to make them useful for sequence…”
Eck: Untrained listeners rated Magenta compositions as more Bach-like than Bach
“When we put these tunes out for, like, untrained listeners to listen to, they sometimes voted them as sounding more Bach-y”
Eck: As of 2017, AI cannot generate a single coherent text paragraph
“So, so everybody understands that's listening or watching, you know, we can, we can't generate a coherent paragraph, right? So, I don't mean we, magenta. I mean, kind of we, humanity.”
Eck: Magenta aims to build creative tools, not act as artists
“The reason that I put that quote there, I think, is to be honest with the division between engineering and research and artistry, and to not think that what I'm doing is being a machine learning artist, but we're trying to build interesting ways to make new ki…”
Eck: Users' first instinct with AI art models is breaking them
“The first thing you're gonna do, if you think, if someone comes to you and says, here's this really smart model that you can make art with, what are you gonna do? You're gonna try to show the world that it's a stupid model, right? But maybe the way that, maybe…”
Eck: Magenta's generated AI music is not yet good enough for testing
“I, at least for Magenta, I haven't felt like the quality of what we've been generating has been good enough to bother, so to speak. Like you find it, you cherry pick, you find some good things, you're like, okay, this model trains, and it's interesting, and no…”
Eck: Audio model generation errors sound musical because of learned training variance
“I think they're interesting because the model has been forced to capture some of the important sources of variance in real music audio, and even when it Fails to reproduce all of them when it fills in with confusion, so to speak. Even that confusion is, is som…”
Eck: AI music tools require strong human improvisation skills
“But if you don't have the musical, so basically it's the musician bringing all the skills to the table. Right. So like, even, even with the primitive sequence generation stuff, like it's still been interesting to see that it's musicians with a lot of musical t…”
Doug Eck: Vanilla LSTMs cannot handle long-timescale hierarchical musical patterns
“Like LSTM in its most vanilla form, I think everybody's pretty convinced that it's not going to handle really long time scale hierarchical patterning.”
Eck: RL is slower to train than GANs but offers more flexibility
“So another way to do this is to use reinforcement learning. Yeah. And it, it's slower to train, because all you have is a single number, scalar reward, instead of this whole gradient flowing. Than GANs, but it also is more flexible.”
Eck: Adding Deep Q-learning rules made Google Magenta's music noticeably catchier
“And we had, I thought, some very nice generated samples of music that were pretty boring with the LSTM network. But then the LSTM network trained additionally using a kind of reinforcement learning called deep Q learning to follow some of these rules. The gene…”
Eck: AI will automate routine music creation, pushing artists toward new complexity
“Like some things that used to be hard will be easy. And so, We'll offload all of that. And if people are happy just listening to the stuff that's now easy, then yeah, it's a problem solved and we'll be able to generate lots of it. But then what people tend to …”
Eck: Long-form ML models will let composers delegate macro structure
“If we get to models that can handle longer structure and nested structure, we'll have a lot more ways in which we can decide what we want to work on versus what we have the machine help us with, right?”
Eck: Google Magenta's NSynth cannot synthesize audio in real time yet
“Right now, it's quite slow to listen, so to speak. We're not able to do things in real time.”
Eck: Artists will not do a huge amount with Quick Draw data
“I think, you know, I'm not expecting that artists really do a huge amount with this quick draw data because as cool as it is, These things were drawn in 20 seconds, right? There's kind of a limit to how much we can do with them.”
Eck: Google AI Duet relies on simple 2002 RNN technology
“And what I noticed, even with AI Duet, which is this like web-based, like, it's a simple RNN. It's like, I can lay claim. It's technology that was published in 2002. It's really a very simple, really simple.”
Eck: Magenta prioritized timbre generation over sequence generation
“I haven't, well, we haven't pushed the sequence generation stuff much because we really wanted to focus on, on tamper.”
Eck: Low-temperature AI models produce caricatures rather than authentic samples
“If you sample from Sketch RNN with very low temperature, meaning with, like, without a lot of noise in the system, you actually get what, like, if you want to squint your eyes and break philosophy is like the platonic cat. You know, you get a cat that looks mo…”
Eck: Automating rhythm with drum machines enabled more complex human vocal phrasing
“So you've solved the metronomic, you know, beat problem. And then what you actually find is that artists who are really good at this, they play off of it and they're allowed like when they sing to do many more like rhythmical things than they could do before. …”
Eck: Magenta aims to generate coherent long-form music and art
“I think composing, creating long form pieces, whether they're music or art, I think is, is something we want to do. And this hints at this idea of like, not just having these things that make sense at like, 20 seconds of music time, but actually say something …”
Eck: Magenta has a core API enabling real-time MIDI AI collaboration
“I think the API, the core API that allows us to move music around in real time in MIDI, and actually have a meaningful conversation between an AI model and multiple musicians is there, and there's just a bunch more thinking that needs to happen, right, to get …”
Appearances (1)
| Episode | Date | Speaking time |
|---|---|---|
| Making Music and Art Through Machine Learning - Doug Eck of Magenta · Y Combinator | Jul 21, 2017 | 33m |