Music generation
There are several use-cases for algorithmically generating music, and indeed there is a long history of both hardware and software in this field. Back in the 80s there was even a significant professional backlash against MIDI and drum machines.
For example, it would be quite handy to generate anechoic sounds for acoustics listening tests, so that they can be convolved with the Impulse Responses of simulated environments.
The current mania is randomly generated media, which is an offshoot of a longer-standing mania for using media as filler instead of as a signifier. A lot of what we see and read is just Lorem Ipsum: posts need an image to do well, so people just slap a random image in them. It’s algorithmic garbage causing other algorithmic garbage. One of our deepest worries regarding our own work is that we could be hired to do it just because immersive technology sounds cool, and not because it genuinely adds value to the project at hand.
Anyway, of course there are going to be attempts at using the newest technology for generating music, and here are three of them:
- Suno AI: this one definitely got the most attention
- Udio
- Stable Audio 2.0
There is also an OpenAI product, but it’s not generally available.
The increased repetitiveness in commercial music is definitely not an hindrance for these efforts.
Fundamentally, all of the do what they say they will do, but we do wonder why they focus on directly generating sounds instead of generating MIDI.
Energy consumption
Check out this Ars Technica syndacation of a Financial Times article: https://arstechnica.com/ai/2024/04/power-hungry-ai-is-putting-the-hurt-on-global-electricity-supply/
This is fundamentally an effect of computing getting increasingly centralised in massive data centers (just think of government over-reliance on Microsoft), which has noticeable economies of scale.
With AI there is also a significant divergence between the cost of executing the software, and the cost of training the software. The current AI boom is largely focused on just throwing resources at relatively “dumb” approaches, which is working better than it could be reasonably be expected for now, but that will inevitably run into resource limitations. For now, that bottleneck is energy, which is very bad news in environmental terms.
The silver lining is that if we managed to make companies pay for their environmental impact we would make smarter, more correct, approaches even more competitive.
Learn the basics
The always excellent 3Blue1Brown has released two new videos in his Deep Learning series, explaining Transformers. Here is a link to the whole series of videos: https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=1
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