Microsoft Research has released this podcast on Harvesting Randomness, HAIbrid Algorithms and Safe AI.

Dr. Siddhartha Sen is a Principal Researcher in MSR’s New York City lab, and his research interests are, if not impossible, at least impossible sounding: optimal decision making, universal data structures, and verifiably safe AI.

Today, he tells us how he’s using reinforcement learning and HAIbrid algorithms to tap the best of both human and machine intelligence and develop AI that’s minimally disruptive, synergistic with human solutions, and safe.

TensorFlow.js is a library for developing and training machine learning models in JavaScript and deploying them in a browser or on Node.js.

It is an open source, hardware-accelerated JavaScript library for training and deploying machine learning models.

Amazing to see the innovation of TensorFlow combined with the reach of more developers.

In recent times, a lot of attention is being paid to artificial intelligence (AI) and machine learning (ML). And in this context, the two most popular technologies are the Python and R environments or even C++ libraries. One of the most popular frameworks among developers is TensorFlow, which was developed by Google in 2011. Most of TensorFlow was designed in C++ and has bindings to Python or Java or R, but the most crucial language is missing, which is JavaScript.

Keras shot to popularity some years ago, but in response to the rise of other deep learning frameworks such as PyTorch, Keras has transformed itself into a tightly-connected part of the TensorFlow 2.0 ecosystem.

Pluralsight has released a course on building a ML workflow with Keras and TensorFlow 2.0.

In this course, Build a Machine Learning Workflow with Keras Tensorflow 2.0, you will see how to harness the combination of the Keras APIs and the underlying power of TensorFlow 2.0