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Google Research: Themes from 2021 and Beyond 14

  Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the entire Google Research community    Over the last several de...


  

  
    
    
  

  


A few themes:

· Trend 1: More Capable, General-Purpose ML Models 1
· Trend 2: Continued Efficiency Improvements for ML 2
· Trend 3: ML Is Becoming More Personally and Communally Beneficial 1
· Trend 4: Growing Benefits of ML in Science, Health and Sustainability 1
· Trend 5: Deeper and Broader Understanding of ML 1


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ML models are increasingly prevalent in many different products and features at Google because their power and ease of expression streamline experimentation and productionization of ML models in performance-critical environments. Research into model architectures to create Seq2Seq, Inception, EfficientNet, and Transformer or algorithmic research like batch normalization and distillation 1 is driving progress in the fields of language understanding, vision, speech, and others

Other work:

… research publications by area below or by year (and if you’re interested in quantum computing, our Quantum team recently wrote a retrospective of their work in 2021 1):




Algorithms and Theory

Machine Perception




Data Management

Machine Translation


Data Mining

Mobile Systems 1


Distributed Systems & Parallel Computing

Natural Language Processing 1


Economics & Electronic Commerce

Networking


Education Innovation

Quantum Computing


General Science

Responsible AI


Health and Bioscience 1

Robotics


Hardware and Architecture

Security, Privacy and Abuse Prevention


Human-Computer Interaction and Visualization

Software Engineering


Information Retrieval and the Web

Software Systems


Machine Intelligence

Speech Processing