(T) As he did last year, Jeff Dean, the tech lead of the Google Brain Team and probably one of the most famous Silicon Valley engineers, shared some of the key achievements of his team for 2017 in two recent blog posts:

• The Google Brain Team — Looking Back on 2017 (Part 1 of 2)
• The Google Brain Team — Looking Back on 2017 (Part 2 of 2)

I would recommend everyone interesting in machine and deep learning to thoroughly read those two blog posts.

Last year, Jeff summarized the progress of Google in machine and deep learning in one blog post. This year, Jeff summary is in two blog posts. Google is firing on all cylinders both in fundamental research and many applications that are beyond the scope of Google product portfolio.

Below are the key takeaways from the two posts:

Core Research

• AutoML, a completely new approach to automate the ML process (data preprocessing, model training, and hyperparameter tuning)
• New end-to-end models for speech recognition
• New approaches to fundamental machine learning algorithms (CNN, RNN, RL)
• Use of machine learning in core computer systems
• Ensuring privacy while training the models
• Better understanding of how deep learning work or do not work

Datasets and tools

• Open sourcing large internal Google datasets
• Launched of TensorFlow 2.0 and second-generation of TPU (TensorFlow Processor Units) for running TensorFlow applications

Applications

• Healthcare:
  • Assisting pathologists in detecting cancer
  • Understanding medical conversations to assist doctors and patients
  • Deep learning for highly accurate genomes
• Robotics: allow robots to operate in messy, real-world environments and to quickly acquire new skills and capabilities via learning
• Science:
  • Predicting molecular properties in quantum chemistry
  • Finding new exoplanets in astronomical datasets
  • Earthquake aftershock prediction
  • Deep learning to guide automated proof systems
• Creativity: assisting artists and designers in their creative endeavors
• People + AI Research (PAIR): research and design the most effective ways for people to interact with AI systems
• Fairness and inclusion for all Machine Learning practitioners

Reference: The Google Brain Team — Looking Back on 2016

Note: The picture above is Rennes’ City Hall in Britany.

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