Machine Learning Algorithms and Techniques

The Google Brain team’s mission is "Make machines intelligent. Improve people’s lives." We combine open-ended machine learning research with world-class system engineering and Google-scale computing resources to realize this mission.

Our research started with the development of DistBelief, as a common platform to experiment with various unsupervised and supervised learning algorithms for computer vision, speech recognition and other areas. In computer vision, our team members have played key roles in developing award-winning AlexNet and InceptionNet models, and DeepDream. In Speech Recognition, our team members have pioneered the use of deep Learning for acoustic modeling. In natural language understanding, our team members have advanced word vectors, neural language modeling and pioneered sequence to sequence learning.

We currently conduct fundamental research to further advance key areas in machine intelligence and to create a better theoretical understanding of deep learning such as in Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. Our recent research achievements also include: unsupervised learning, adversarial training, structured learning, long-term dependencies, knowledge distillation, general learning algorithms, understanding of learning algorithms, reinforcement learning, AI safety and TensorFlow.

Representative publications by Google Brain team members

Selected publications

Publications by year

Current Google Brain team members who work on this area