I work on a production ML platform, so I spend way too much time rabbit-holing on interesting looking projects.

If you're looking for interesting startups/projects-not-from-big-tech:

- Glisten.ai (https://www.glisten.ai/). Recent YC startup, uses a combination of different models to parse product information (actually a huge manual problem in retail/ecommerce) and expose it as an api.

- Wildlife Protection Solutions - Recently deployed a model that can automatically detect poachers in nature preserves. Detects twice as many poachers as previous monitoring solutions.

- Ezra.ai - Uses models to search MRIs for cancers, operational in a few different US cities.

- AI Dungeon - A text adventure game built on GPT-2 (now GPT-3). Super fun, if a little silly.

Now, those are just a handful of smaller companies whose core products are ML. There are a ton of financial institutions using ML for fraud detection, real estate platforms like Reonomy that use a variety of models for evaluating investments, and security companies using ML.

But of course, the obvious answer to this question is "Every popular app you use incorporates ML."

Gmail: Smart Compose, spam filtering, etc.

Uber/Maps: ETA Prediction

Netflix/Spotify/all content platforms: Recommendation engines

Facebook/Instagram/Snap/image apps: A variety of models for recognizing faces, object tracking, etc.

People have this weird "Skynet or it's snake oil" paradigm they use to evaluate ML, ignoring the fact that production machine learning is more or less ubiquitous at this point.

> Maps: ETA Prediction

So THAT's why the ETA given is always too short! If it's based on how often a typical driver makes it, and typical driver is a speeding asshole, then no wonder that the estimates are unrealistic for someone who actually drives under the speed limit. It's a shame that Google is actually normalizing assholiness.

What's wrong with speeding? People generally drive at a speed such that they're taking an appropriate level of risk. The speed limits usually set these risk limits too low. Moreover, the job of google's ETA is to be accurate for the most number of people. If you're in the minority, then too bad.

Moreover, I'm not sure how speeding makes you an asshole.

By speeding, you increase the risk of death/injury of other people, you increase their stress level and you also increase road noise. Moreover, you unilaterally (and self-servingly) decide that the norms established by authorities are stupid and that you know better. This all sounds like a description of an asshole to me.

How about translation? Living in a foreign country I possibly find it useful more often than the average person.

Machine generated subtitles on YouTube is another example. I find those useful even in my native tongue, especially when watching at 2x speed.

If you make purchases on the web, there is a good chance that machine learning is behind the fraud detection that you don't even know is taking place.

I'm not much into machine learning myself, but I must admit that ML has been invaluable for good voice recognition and good voice synthesis. Writing programs to interpret speech accurately and synthesizing a "natural" sounding voice by hand is practically impossible, but the perfect application for ML.