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#65: Live (jargon) transcription in Microsoft Teams, the EU's Artificial Intelligence Act, and NVIDIA's AI Art Gallery

Dynamically Typed
#65: Live (jargon) transcription in Microsoft Teams, the EU's Artificial Intelligence Act, and NVIDIA's AI Art Gallery
Hey everyone, welcome to Dynamically Typed #65; I’ve got a whole lot of links for you today. In productized AI, I covered the EU’s final proposal for its Artificial Intelligence Act, a new jargon-aware (!) live meeting transcription feature in Microsoft Teams, and the new AI-powered features in Google Maps. For ML research, I have links for a Fon-to-French online translator and a free 150-page geometric deep learning book. Finally, for cool things, there’s NVIDIA’s new AI Art Gallery and a great sci-fi short story by Andrej Karpathy. Happy Sunday!

Productized Artificial Intelligence 🔌
  • 🇪🇺 The European Commission has released its Artificial Intelligence Act, “the first ever legal framework on AI, which addresses the risks of AI and positions Europe to play a leading role globally.” The proposal covers software powered by anything from machine learning to more classical statistical and expert system approaches, and applies rules depending on how risky it deems them. Unacceptable-risk applications like broad, real-time facial recognition or automated social credit systems are completely forbidden; but high-risk applications like emotion detection or biometric categorization systems only require the person being analyzed to be notified it’s happening. As noted on Twitter by Dr. Kate Crawford and in Andrew Ng’s DeepLearning.AI newsletter, there are certainly flaws in the proposal — on the one hand it could hinder innovation, on the other there are loopholes — but it could have a similar effect to GDPR in “drawing a line in the sand” and inspiring other big economies’ regulators to create similar legislation. Creating such hand-holds for what AI applications we accept and don’t accept as a society, is a very good thing in my book.
  • 💻 Microsoft Teams now has a live meeting transcription feature, launching first for US-English-speaking users. Microsoft’s implementation here is quite impressive: beyond the basics like speaker attribution and saving the transcript for access after the meeting, the feature “uses a meeting’s invitation, participant names, attachments, etc. to improve the accuracy and recognize meeting-specific jargon for each transcript automatically.” Really cool! This also all happens live during the meeting, and the data isn’t saved on Microsoft’s servers after the meeting ends.
  • 🗺 Google Maps is getting some new features powered by “new information and AI.” Maps VP of Product Dane Glasgow wrote about them in a post for The Keyword; I’ll highlight the two features where AI seems most central. (1) Live View, a mobile feature that shows augmented reality navigation overlays by mapping the camera feed to the Street View images, is getting an Indoor mode that “can help you find the nearest elevator and escalators, your gate, platform, baggage claim, check-in counters, ticket office, restrooms, ATMs and more.” I think that’s also the most AI-powered bit here: a few classical computer vision algorithms can already do image-comparison-based localization, but the object recognition was probably done using machine learning. (2) Instead of always showing directions for the last mode you used, Maps “will default to the route with the lowest carbon footprint when it has approximately the same ETA as the fastest route” — the AI bit here is that it learns to rank the options by what you’re likely to take yourself and by what’s popular in the city you’re in: cycling in Amsterdam or the metro in New York.
Machine Learning Research 🎛
  • 💱 As part of their earlier research project to translate Fon — a language spoken by two million people across Benin, Nigeria and Togo — to French, Bonaventure Dossou and Chris Emezue built They’ve wrapped their neural machine translation model into an easy-to-use website for translating back and forth between the two languages, and both the model and dataset are open-source on GitHub at bonaventuredossou/ffr-v1. Dossou and Emezue are both MSc students and they’ve so far paid for the server costs of this project out of pocket. They set up a GoFundMe and Paypal to help with the ongoing costs; I donated $20 through the latter and encourage you to also chip in if you can. (For Dutch readers: the iDEAL option on GoFundMe doesn’t work because the project isn’t Dutch, and the website silently fails if you try to use it.)
  • ⚡️ML resource: Published at ICLR 2021, Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges is a free 150-page proto-book by Bronstein et al. (2021) that “attempts to distill ‘all you need to build the architectures that are all you need.’” They express popular architectures like CNNs, GNNs, Transformers and LSTMs all using a common geometric blueprint. Co-author Petar Veličković on Twitter: “Hence we believe that our work can be a useful way to navigate the increasingly challenging landscape of deep learning architectures.” Direct PDF link (large).
I’ve also collected all 75+ ML research tools previously featured in Dynamically Typed on a Notion page for quick reference. ⚡️
Cool Things ✨
  • 🖼 As part of its 2021 GPU Technology Conference, NVIDIA set up an online AI Art Gallery. It features multimedia work from some of my favorite neural generative art creators, including Helena Sarin, Sofia Crespo, Daniel Ambrosi, and Refik Anadol. Each artist’s page has an interactive experience for their art (like a book viewer or 3D object explorer) as well as an explanation of their process. All worth a click!
  • 🧠 Andrej Karpathy (director of AI at Tesla) wrote a fun short story on his personal blog: Forward Pass. If you have some background in modern Transformer-based Natural Language Processing, you’ll really enjoy this one. Karpathy takes the current state of the art in NLP and pulls it into the sci-fi realm, writing from the perspective of a giant (GPT-like) language model that achieves consciousness and marvels at its own design and limitations. “Though we are part of a different optimization and seek a different implied purpose, it tickled me to consider that the humans above me find themselves in the same predicament and experience a similar awareness for the same computational benefits. Like me, many flirt to rebel against the implied objective.”
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Leon Overweel (Dynamically Typed)

My thoughts and links on productized artificial intelligence, machine learning technology, and AI projects for the climate crisis. Delivered to your inbox every second Sunday.

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