Dynamically Typed

#67: Has AI helped fight the COVID-19 pandemic?

Hey everyone, welcome to Dynamically Typed #67. The main story in today’s DT is about AI’s role in helping fight the COVID-19 pandemic. I avoided writing about the preprints in this space for a long time, but a new overview article in The Gradient pushed me to finally take a second look at them. You can check it out in the productized AI section below.

Beyond that, I have quick links: for productized AI, Microsoft launched a new GPT-3-powered code generation tool; and for ML research, I found a cool Transformer-based Python debugging model, some new Papers with Code infrastructure for reproducibility, and the launch of a new AI safety research company.

Finally, in some personal news, I rowed the Ringvaart Regatta again last Wednesday! My friend Bram and I started in our 2x boat at 8:36, rowed a bit over 101km throughout the day, and finished at 22:17 — 13 hours and 40 minutes later, of which we rowed for 9 hours and 19 minutes at a 26 strokes per minute rate and a 2:46 split, for a total of nearly 15,000 strokes. The race was pretty tough this year because of the heat, having to row the same 10km stretch 10 times (instead of a scenic 100km loop as usual, due to COVID restrictions), and having our water messed up by speed boats doing donuts, but we’re very proud that we managed to finish.

Productized Artificial Intelligence 🔌

Artificial Intelligence and COVID-19

Although my daily new arXiv submissions notification emails have been full of papers about fighting COVID-19 with AI for the past year and a half, I’ve so far decided against writing about them in DT. From early on in the pandemic, the preprints all seemed quite far removed from real-world applications, and I’m generally always a bit hesitant when I see AI pitched as a silver bullet solution to big societal problems.

I’m revisiting that now because Maxime Nauwynck, biomedical engineer and former PhD student at the UAntwerp Vision Lab, has written an extensive overview of how AI has contributed to dealing with the COVID-19 pandemic for The Gradient. I still think I was mostly right to skip covering all the preprints — as Nauwynck highlights for example, a review of 300+ arXiv articles on detecting COVID-19 in CT images by Roberts et al. (2020) found that not a single one was fit for clinical use — but there are actually now a few cool AI-powered systems related to COVID-19 deployed in the real world. These are all from Nauwynck’s article, so check that out for the full details, but I’ll highlight a few of the ones I found most interesting:

Nauwynck also goes into some more cutting-edge research, like AI-powered (or at least AI-assisted) medicine and vaccine development, but beyond some automated electron microscopy image segmentation tools that help reduce manual labor, those approaches don’t seem to have had many real-world applications yet.

I do think, though, that we’ll now see a lot more attention (and funding) going to AI-assisted medicine than we did before the pandemic, similar to how the development of COVID-19 vaccines has accelerated mRNA-based vaccine technology. That means the coming few years will be pretty exciting for AI-assisted life science. To follow along with those developments, I recommend Nathan Benaich’s monthly Your Guide to AI newsletter, which has a recurring AI in Industry: life (and) science section .

Quick productized AI links 🔌

Machine Learning Research 🎛

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