Artificial Intelligence #96

Hey, in this issue: a primer on ML interpretability and explainability; why adversarial image attacks are no joke; a tutorial on composing decision forest and neural network models; how AI is hijacking art history; Reddit’s ML model deployment and serving architecture; and more.

This issue is sponsored by Pachyderm and Data Reliability Engineering Conference.

• [The Gradient] Explain yourself - a primer on ML interpretability and explainability
• [Awful] AI is making applying for jobs even more miserable
• Why adversarial image attacks are no joke
• [Tutorial] Composing decision forest and neural network models with TensorFlow
• [Sponsored] Upcoming Webinar: Dispelling Hype vs Reality for AI in Healthcare

Join the team at Pachyderm and SFL Scientific as they discuss upcoming trends for AI/ML in Healthcare. Register for the upcoming webinar as they discuss everything from graph databases to bridging the gap between different data types.?

• How AI is hijacking art history
• AI-designed organisms made from frog cells are capable of self-replicating in a way that differs from plants and animals (also check this)
• [Sponsored] Data Reliability Engineering Conference 2021, Dec 14, one full day of reliability standards & innovation. Register Free!
• A whopping 301 newly confirmed exoplanets – discovered with new deep neural network ExoMiner (plus here and here)
• Reddit’s ML model deployment and serving architecture

The newsletter is now read by more than 520,000 subscribers. If you are building an AI or a data product or service, you are invited to join the list of?these great companies?by becoming a sponsor of one of the future newsletter issues. Feel free to reach out to?[email protected]?for more details on sponsorships.

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Have a nice weekend! See you next week. — Andriy