FocusAI - Edition #95
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Edition #95 - April 8th, 2022
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This week, three articles :
👨👩👧 No-code AI
🧬 Detect genetic disorders from your face
🐎 Google puts summarization into production
👨👩👧 No-code AI
The next killer AI application may be developed by someone who has never heard of gradient descent.
A rising generation of software development platforms serves users who aren’t familiar with AI — and even programming. Using no-code AI platform — an automated programming tool that either generates new code or customizes pre-existing code according to user input — generally requires access to a web browser and training data. From there, a user-friendly interface lets users train a prebuilt architecture.
Examples are : Teachable Machine from Google, AI Builder from Microsoft, Juji, Akkio...
Platforms that automate coding, data collection, and training are an important part of AI’s future. Although no-code AI tools are still maturing — for example, they’re limited to particular tasks and some aren’t yet suitable for commercial-grade applications — they’re on track to open the field to a far broader range of users, enabling them to apply tried-and-true approaches to certain classes of problems. And they may be useful to experienced AI developers, too. For instance, trained engineers may also use them to build wireframe versions of more intensive projects.
🧬 Detect genetic disorders from your face
People with certain genetic disorders share common facial features. Doctors are using computer vision to identify such syndromes in children so they can get early treatment.
Face2Gene is an app that recognizes genetic disorders from images of patients’ faces. Introduced in 2014, it was upgraded recently to identify over 1,000 syndromes (more than three times as many as the previous version) based on fewer examples. In addition, the upgrade can recognize additional conditions as photos of them are added to the company’s database — no retraining required.
Some 350 million people worldwide live with a rare genetic disorder. Such conditions are especially difficult to diagnose because they’re so numerous, and many doctors never encounter a case. Face2Gene, which reportedly is used by thousands of geneticists, has been credited with making the job much easier.
🐎 Google puts summarization into production
Google has put language model-powered text summarization into Google Docs. Specifically, Google has recently used its Pegasus model for abstractive summarization to give Google Doc users the ability to see short summaries of their docs.
Summarization is a hard task even for contemporary AI models. Some of the challenges Google has encountered include distributional issues, where "our model only suggests a summary for documents where it is most confident", meaning Google needs to collect more data to further improve performance, as well as open questions as to how to precisely evaluate the quality of summarizations. More pertinently for researchers, Google struggles to summarize long documents, despite these being among the most useful things for the system to summarize.
#Gene #Summarization #NoCode
A big thanks to our sources: https://jack-clark.net/, https://www.actuia.com/, https://thevariable.com/news/, https://techcrunch.com/, https://read.deeplearning.ai/the-batch/
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Have a good week-end,
Verónica 🐕, Maxime 🙃 from Toulouse, France with 🌺