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Sometimes we take a break from building cutting edge AI redaction models to stretch our academic muscles and write about privacy and machine learning. Check back here regularly for our musings.

Natural Language v. Regex: The Context wars

Natural Language v. Regex: The Context wars

Say you’re looking for credit card numbers. It’s quite easy to set up a regex that looks for 16-digit numbers or four groups of four numbers separated by a ‘-’. A regex like this is highly effective in the perfect world of computer data, but unfortunately the real world is much more complicated.

Customers Are Demanding Privacy

Customers Are Demanding Privacy

In the past three years there has been a massive wake-up in customer awareness about privacy. Many customers are now refactoring how they buy, taking their business elsewhere if they don’t trust a company’s data practices.

Accelerating Tensorflow Lite with XNNPACK

Accelerating Tensorflow Lite with XNNPACK

The new Tensorflow Lite XNNPACK delegate enables best in-class performance on x86 and ARM CPUs — over 10x faster than the default Tensorflow Lite backend in some cases.

A Brief Overview of Privacy-Preserving Software Methods

A Brief Overview of Privacy-Preserving Software Methods

A very brief overview of privacy-preserving technologies follows for anyone who’s interested in starting out in this area. I cover symmetric encryption, asymmetric encryption, homomorphic encryption, differential privacy, and secure multi-party computation.