AI News, When machine learning matters
- On Sunday, June 3, 2018
- By Read More
When machine learning matters
I joined Spotify in 2008 to focus on machine learning and music recommendations.
(The other key differentiator was licensing – until early 2009 Spotify basically just had all kinds of weird stuff that employees had uploaded.
In 2009 after a crazy amount of negotiation the music labels agreed to try it out as an experiment.
The cost of bandwidth and storage has gone down by an order of magnitude, not to mention the labor cost needed to build and maintain it.
I lead the tech team at a startup and we are nowhere near using any kind of sophisticated machine learning, two years into the process.
This unfortunately means that the machine learning team isn’t a team that creates the core business value and has a crucial strategic role.
It will be the team that comes in after 5-10 years once the “basic” features have been built and then squeezes out another 10% MAU by A/B testing the crap out of the product.
Building super nasty integrations with vendors, or figuring out the control engineering of the suspension system of a self driving car.
For the pure machine learning I think we’ll see a separate force of commoditization of machine learning in those areas, where the technological differental between companies coverges towards zero.
Another type of data I think people underestimate is in people’s heads – learnings from real production usage.
Google and other big players has shown that they are willing to pay a huge premium for smart teams (throwing out a fun conspiracy theory just for the sake of it: Google is going to acqui-hire any team with smart people just to create a talent monopoly.) These companies all had built some cool tech, but the price paid really represented the scarcity of skills.
- On Tuesday, January 28, 2020
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