Facebook announced today that it is open-sourcing QNNPACK, a high-performance kernel library optimized for mobile AI. The computing power of mobile devices is but a tiny fraction of that of data center servers. As such it is essential to find ways to optimize mobile devices’ hardware performance in order to run today’s compute-hungry AI applications.
Last month’s ReWork Deep Learning Summit in London provided a peek at current recent research progress and future trends in artificial intelligence technologies. The two-day event featured top scientists and engineers from Facebook, MIT Media lab, DeepMind and other leading institutes.
Skip is an experimental research language project that Facebook developed over the last three years: “Skip tracks side effects to provide caching with reactive invalidation, ergonomic and safe parallelism, and efficient garbage collection. Skip is statically typed and ahead-of-time compiled using LLVM to produce highly optimized executables.”
Anglian Water plans to install an AI-based energy storage machine system in its water treatment facilities. The system will provide a real-time energy consumption balancing service and increase the lifespan of storage machines. The AI system was designed by Open Energi and is expected to be full operational by next year.
Facebook is working with a select group of advertisers to create augmented reality ads for its News Feed. When users activate an ad’s tap-to-try AR capability it can display for example how a pair of glasses would look on their face via the user’s webcam and screen. Facebook says it also intends to expand shopping support in Instagram Stories.
Salesforce announces a deeper data sharing partnership with Google. Consumer insights from Salesforce’s Marketing Cloud and Google Analytics 360 will be merged into one dashboard for either platform. Marketing Cloud data can be used to create a more customized web experience.
As Facebook struggles with fallout from the Cambridge Analytica scandal, its research arm today delivered a welcome bit of good news in deep learning. Research Engineer Dr. Yuxin Wu and Research Scientist Dr. Kaiming He proposed a new Group Normalization (GN) technique they say can accelerate deep neural network training with small batch sizes.
To boost learning research aimed at endowing robots with better generalization capabilities, Yi Wu from UC Berkeley and Yuxin Wu, Georgia Gkioxari, and Yuandong Tian from Facebook AI research recently published the paper Building Generalizable Agents with a Realistic and Rich 3D Environment.
In order to provide personalized ads, tech giants such as Google and Facebook are trying to abstract their users’ personality from their posts on social media. Hence, it is essential for social networking applications to predict personality from written text.