Gradient Flow #22: AI Security, Time-series Databases, Concept Drift

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This edition has 482 words which will take you about 3 minutes to read.

“They beat me up unjustly, but since they did the same thing to everyone else, it was not unfair.”  – Sydney Morgenbesser

Data Exchange podcast

  • Securing machine learning applications  Ram Shankar is a Berkman Klein Center affiliate, and a researcher and engineer who works at the intersection of Machine Learning and Security. This episode is focused on the current state of tools and techniques for building secure and trustworthy machine learning applications.
  • Testing Natural Language Models   NLP model building tends to follow the following sequence: split your data into train-validation data sets; build a model using your training subset; and test its efficacy using your validation set. Marco Ribeiro, a Senior Researcher at Microsoft Research, describes how ideas from software engineering can be used to inject more rigor into the NLP model development process.

[Image: Art Deco Plane Clock by Dean Wampler, used with permission]

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[Baháʼí gardens in Haifa, Israel. Image by Ben Lorica]


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