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You would be astonished at some of the non-public (not that they are secret) machine learning things that Uber does that you wouldn't even be aware of.

My favorite is that they do pothole detection, prediction and routing around based on what they classified based on driver accelerometer data. They've also reduced traffic incidents with insights from the same dataset that drove product changes.

That's interesting, and in line with what I perceived as Uber's extreme focus on operations.

To me, the accelerometer DATA is the new and important thing -- it's the thing that taxi companies don't have.

Whether the machine learning model is "sophisticated" doesn't seem critical on the face of it.

I guess it depends whether there are a lot of non-pothole events that look like pothole events to "naive" signal processing. Do you need deep learning for this problem? I don't know but I would be skeptical without a citation.

But I am interested in more info and examples. If someone can give a bunch of examples I would adjust my view.

Amazon uses machine learning to forecast demand and ship products to warehouses before you order something. It's part of what makes their 1 day deliveries work. https://www.npr.org/2018/11/21/660168325/optimized-prime-how...

Data is obviously crucial, but it's useless if it's just sitting on hard drive. Machine learning allows them to squeeze out as much juice from it as possible, and a few % point lift makes a huge difference at scale, especially in high volume businesses with low margins per transaction.

This is just nitpicking phrases at this point, a popular past time for us nerds, so I get it but it misses the point. "Sophisticated" is enough of a suitcase term that it's effectively worthless.

More importantly it's worth understanding that ML allows us to do things we couldn't do before, because it's cheaper and scales better than previous optimization methods, when you have the data.

So yes, the data infrastructure is the critical part, however the ML pipelines and Data Science work is what gives the massive multiplier at a cost/scale you can't get with traditional optimization methods.

Machine Learning is a technology the same way blockchain is. Customers buy products and solutions, not your tech stack so it doesn't make sense for any company to sell themselves as a machine learning company, unless you're selling APIs.

Alexa / Siri / Google Assistant would not be possible without machine learning. Same goes for Snapchat's filters and any other form of AR. Google's Pixel phones wouldn't have such a great camera without computational photography / machine learning.

Those of us doing it mostly roll our eyes at how VCs and (especially) marketers describe it, so I agree with you, but the "is it ML or stats?" conversation has been going nowhere for as long as this round of AI hype has been here.

That said, uber and lyft certainly benefit from much more interesting ML and stats than just "simple statistics." For example, setting prices and incentives to properly balance the driver side against the rider side of the marketplace.

Yeah I agree the argument is mostly about terminology. Anyway you slice it you have a bunch of data and you have a team that's improving the product with the data. That is for sure "table stakes".

And yes I probably should have said "data science" or "statistics" rather than "simple statistics", definitely for Uber and Lyft (less so for smaller companies).

I worked on a data science team 10 years ago when it was called "statistics". I even remember a 2009 NYTimes article that talked about data science without using the word "data science", because it hadn't come into use yet.

If people want to redefine "machine learning" to mean "data science", OK fine. I guess that's what's happening now. The term "data science" already annoyed some people, but I guess now it's "machine learning" and "deep learning".

> For example, do Uber and Lyft need machine learning?

Anyone doing large scale ETA prediction and routing these days is using ML models at various phases in the pipeline, from GPS noise filtering to road network data generation. You might get past the early stages of a rideshare or delivery service with more traditional naive models, but there are still significant efficiency gains to be made in even the most cutting edge systems today. For maps and navigation, this is especially true when you go global and need models that adapt to subtle cultural and behavioral changes across markets.