These gains are realized in two frameworks: one is JAX, which provides rich support for these primitives through the XLA compiler.

We're excited to announce that the Machine Learning Blueprint is joining the IBM DataScience Community! We've always strived to source high quality content across the web and put deep thought into our curations. However, continually delivering this every week is not easy, so after two years of publishing, we decided to take a pause. Through our work with the IBM Community we identified an opportunity to join forces to grow a community of machine learning practitioners, we were thrilled at the prospect. Their mission was clear: provide a place for data scientists to interact with other experts, share support and insights and start dialogue around relevant topics.

In today's increasingly hyperconnected world, it's more important than ever to protect our privacy online. But the more information companies collect about us, the more difficult it becomes to protect, even if it's properly anonymized. To address this critical issue, we're excited to announce that we've worked with Google to make our differential privacy library publicly available through TensorFlow, the industry's leading open-source machine learning framework. So what is differential privacy, and why are we teaming up with the world's top technology firm to make it freely available? When we sign up for online services, they assure us that all of our information is "completely anonymous."

Roundup Hello, here's a quick recap on all the latest AI-related news beyond what we've already reported this week. You may have seen news reports that autonomous cars are unlikely to detect pedestrians crossing the road if they have dark skin, and thus run them over. And yes, the internal alarm bells in your head should be going off, as a closer look at the research behind the stories shows all those headlines screaming about racist AI are a little off the mark. The academic paper at the heart of the matter described a series of experiments testing different computer vision models, such as the Faster R-CNN model and R-50-FPN, on images of pedestrians with different skin tones. The study's authors, based at the Georgia Institute of Technology in the US, described how they paid humans to look through the collection of roughly 3,500 photos, and individually tag people in the snaps as either "LS" for light skin or "DS" for dark skin, and then trained the neural networks using this dataset.

Ahead of the 2019 TensorFlow Dev Summit, Google is announcing a new way for third-party developers to adopt differential privacy when training machine learning models. TensorFlow Privacy is designed to be easy to implement for developers already using the popular open-source ML library. The goal (via The Verge) of differential privacy for machine learning is to only "encode general patterns rather than facts about specific training examples." This allows user data to remain private, while the system overall still learns and can advance from general behavior. In particular, when training on users' data, those techniques offer strong mathematical guarantees that models do not learn or remember the details about any specific user.

Google has announced a new module for its machine learning framework, TensorFlow, that lets developers improve the privacy of their AI models with just a few lines of extra code. TensorFlow is one of the most popular tools for building machine learning applications, and it's used by developers around the world to create programs like text, audio, and image recognition algorithms. With the introduction of TensorFlow Privacy, these developers will be able to safeguard users' data with a statistical technique known as "differential privacy." Introducing this tool is in keeping with Google's principles for responsible AI development, Google product manager Carey Radebaugh tells The Verge. "If we don't get something like differential privacy into TensorFlow, then we just know it won't be as easy for teams inside and outside of Google to make use of it," says Radebaugh.

Today, we're excited to announce TensorFlow Privacy (GitHub), an open source library that makes it easier not only for developers to train machine-learning models with privacy, but also for researchers to advance the state of the art in machine learning with strong privacy guarantees. Modern machine learning is increasingly applied to create amazing new technologies and user experiences, many of which involve training machines to learn responsibly from sensitive data, such as personal photos or email. Ideally, the parameters of trained machine-learning models should encode general patterns rather than facts about specific training examples. To ensure this, and to give strong privacy guarantees when the training data is sensitive, it is possible to use techniques based on the theory of differential privacy. In particular, when training on users' data, those techniques offer strong mathematical guarantees that models do not learn or remember the details about any specific user.

Today, Google released TensorFlow Privacy, an open-source tool that will help keep your data anonymous, even as AI learns from it. The now-public code is based on differential privacy. That's what allows Gmail's Smart Reply to guess what you're going to say by collecting data from other people's emails, and at the same time, keeps Smart Reply from revealing any juicy secrets people have typed before. Differential privacy is not new. Essentially, it makes sure AI cannot encode information that is unique to you and could therefore reveal your identity.

Here's a short list of what's been happening so far since the Christmas and New Year break. TensorFlow updates: Google has released new code for developers interested in training machine learning models more privately as well as a sneak peak of TensorFlow 2.0. TensorFlow Privacy is a Python library that contains TensorFlow algorithms to train models with differential privacy for anonymizing data sets. It's good for handling sensitive data like medical records, where you want to scrub the data of any characteristics that could potentially identify a patient. Next, is the preview of the upcoming TensorFlow 2.0 updates.