#60: Google Pixel car crash detection, Model Search for TensorFlow, an a movie frame search engine

Hey everyone, welcome to Dynamically Typed #60. Two weeks after we were ice skating on the canals, spring has arrived in Amsterdam and I’m sitting on the roof, writing this issue in the sun after a great row this morning! 🌞🚣‍♂️

For productized AI, I wrote about the car crash detection feature on Google’s Pixel phones, and about an NYT article on Exposing.AI, a tool that traces facial recognition datasets back to their Flickr origins. For ML research, I’ve got a cool new open-source AutoML tool from Google called Model Search, plus three new long reads from Distill. Finally, for cool things, I found an ML-powered website that lets you find movie screenshots based on the objects in them.

Productized Artificial Intelligence 🔌

• 🚘 Apparently, Google’s Pixel phones can detect car crashes. This was making the rounds on Twitter after a Reddit user wrote on r/GooglePixel that car crash detection saved them from hours of suffering because they had an accident on their own property, where no one would otherwise have found them for a long time. When the phone detects a crash, it calls local emergency services and says, “You are being contacted by an automated emergency voice service on behalf of a caller. The caller’s phone detected a possible car crash, and they were unresponsive. Please send help,” followed by the phone’s latest location. Pretty amazing stuff, that’s being built into more and more products — Apple Watches have a similar fall detection feature. Dave Burke, Google’s VP of engineering for Android, noticed the story and tweeted a photo of the setup they used to train the ML model powering this feature. Worth a click.
• 👁 Cade Metz and Kashmir Hill at The New York Times wrote about how old Flickr photos became a part of facial recognition datasets. The story centers around Exposing.AI, a tool that can show you whether your face is a featured in any popular facial recognition datasets like VGG Face, MegaFace and FaceScrub, based on your Flickr username or a photo URL. Beyond that, it’s a good read that goes into how, five to ten years ago when AI was not yet very influential, commercial and university labs were building lots of different facial recognition datasets and, in the spirit of open science, sharing them publicly on the internet. Only now that it’s becoming clear that facial recognition systems are biased — as I covered last summer in Is it enough for only big tech to pull out of facial recognition? and Facial recognition false arrest — some of these datasets are being taken offline. But these systems exist now, and taking down the datasets won’t stop them from being used; only regulation will.

Machine Learning Research 🎛

• ⚡️ Google has released Model Search, an open-source AutoML platform for the TensorFlow ecosystem. The pitch: “Model Search is domain agnostic, flexible and is capable of finding the appropriate architecture that best fits a given dataset and problem, while minimizing coding time, effort and compute resources.” It can run on a single machine or in a distributed setting, and uses a reinforcement learning-inspired “explore & exploit” methodology to find a model architecture that optimizes for user-specified metrics. For efficiency, Model Search also uses knowledge distillation and weight sharing between experiments runs. It’s available on GitHub at google/model_search.
• 📜 Three new long reads on Distill: a bit of a meta article about how they think about Visualizing Weights, which has been an important feature in lots of the publication’s recent articles; a new entry to the Circuits thread on reverse-engineering Curve Circuits; and an application of Neural Cellular Automata (NCA) for generating Self-Organizing Textures. That last one features a fun interactive graphic at the top. I didn’t get around to reading these in detail before sending out today’s DT — they’re quite long — but wanted to share them anyways.

Cool Things ✨

Searching for “clock” on Film.

• 🎞 After I wrote about same.energy, a visual search engine in the last issue of DT, I came across another similar project this week: Flim is a search engine for famous movie frames, which uses a computer vision model to tag screenshots with the objects featured in them. A search for “clock”, for example, yields screen caps from Slumdog Millionaire, V for Vendetta, and Peter Pan. I can imagine this’ll become a very useful tool for cinematographers or film students who are exploring the different creative ways in which certain subjects have been portrayed in the past.

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