Compression could be machine learning's "killer app"

Pete Warden (previously) writes persuasively that machine learning companies could make a ton of money by turning to data-compression: for example, ML systems could convert your speech to text, then back into speech using a high-fidelity facsimile of your voice at the other end, saving enormous amounts of bandwidth in between.

Less exotically, ML is also used for "adaptive compression" algorithms that use ML-based judgments to decide how to compress different parts of a data-stream without compromising fidelity in ways that are perceptible by human observers.

Warden points out that companies already spend a lot of money on compression: vendors that want to sell ML-based compression systems would be asking for customers to switch who they spend an existing budget with, a much easier sell than convincing companies to spend money in an altogether new category.

One of the other reasons I think ML is such a good fit for compression is how many interesting results weve had recently with natural language. If you squint, you can see captioning as a way of radically compressing an image. One of the projects Ive long wanted to create is a camera that runs captioning at one frame per second, and then writes each one out as a series of lines in a log file. That would create a very simplistic story of what the camera sees over time, I think of it as a narrative sensor.

The reason I think of this as compression is that you can then apply a generative neural network to each caption to recreate images.