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

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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:

  • BlueDot and HealthMap, two companies that use natural language processing to scrape local news, warned customers about “a new type of pneumonia in Wuhan, China” on December 30th and 31st 2019, respectively — a solid week before the US Centers for Disease Control and World Health Organization did the same.
  • Alizila (part of Alibaba) has a system for detecting COVID-19 in CT scans, that by March of 2020 had already helped diagnose over 30,000 people across 26 hospitals in China. Now that PCR tests and rapid tests have become much more widely available over the past year, though, I don’t know if such systems are still in use.
  • To forecast/nowcast the actual (not just positive-tested) numbers of COVID-19 cases, hospitalizations, and deaths for a region, several organizations now use machine learning models and ensembles. Youyang Gu’s model was quite popular on Twitter for a while, and the US CDC has one too.
  • DeepMind used AlphaFold 2 to predict the shapes of some proteins related to COVID-19.

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 🎛

  • 🐛 Cool new paper from Drain et al. (2021) at Microsoft Research: DeepDebug is a Transformer-based model that can fix Python bugs using stack traces, back translation and code skeletons. One interesting contribution is their “neural bugs” injection model, which was trained to revert bug-fixing commits and “can generate near arbitrary edits that are drawn from the distribution of mistakes developers actually make.” On the QuixBugs benchmark, DeepDebug increases the number of bug fixes found by 50% while reducing false positives from 35% to 5%, all while decreasing the run timeout from six hours to one minute. Can I get this in PyCharm?
  • 🧾 Papers with Code has a new feature to link papers to independent reproducibility reports done as part of their ML Reproducibility Challenge (RC2020) event, which now covers NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR and ECCV. Both in university and at my previous and current jobs, paper reproductions have always been some of my favorite learning experiences: you don’t truly understand a paper (and the math inside it) until you’ve coded it up and gotten it to perform similarly to the original! It’s great to see some more formal infrastructure being built up around this practice, now including Papers with Code’s recurring event, standardized reports, and cross-linking.
  • 🏢 A lot of the people behind some of my favorite recent machine learning research (like Circuits and Multimodal Neurons) have joined up to form a new AI safety and research company called Anthropic, and raised a $124 million series A round “to build more reliable, general AI systems.” I hope they keep publishing their research to Distill!


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