DeepMind announced today that it has opened its Graph Nets (GN) library to the public, enabling the use of graph networks in TensorFlow and Sonnet. Graph Nets is a machine learning framework that was published by DeepMind, Google Brain, MIT and University of Edinburgh on Jun 15.
Founded in 1999, Tokyo-based DeNA has developed popular platforms and services for gaming, E-commerce, automotive, healthcare and entertainment content distribution. As AI continues transforming all things digital, DeNA is expanding its deep learning tech capabilities to support R&D on new techniques.
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.
The computational power of smartphones and tablets has skyrocketed to the point where they approach the level of desktop computers on the market not long ago. Although it’s easy for mobile devices to run all the standard smartphone apps, today’s artificial intelligence algorithms can be too compute-heavy for even high-end devices to handle.
UC Berkeley researchers have published a paper demonstrating how Deep Reinforcement Learning can be used to control dexterous robot hands for complicated tasks. Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations proposes a low-cost and high-efficiency control method that uses demonstration and simulation techniques to accelerate the learning process.
Nadja Rhodes is enamoured with artificial intelligence. A Seattle-based Microsoft software developer unpracticed in AI techniques such as deep learning, Rhodes had applied to a number of tech company sponsored AI residency initiatives, but to no avail. And so she was thrilled to be accepted by OpenAI Scholars.
At last month’s RE•WORK Deep Learning in Finance Summit in London, leading AI industry practitioners and academics from prestigious universities discussed their research, provided insights on business trends and real-life AI applications, and addressed current challenges facing the AI industry as a whole.
China’s computer vision company SenseTime today announced it had raised a staggering US$600 million in Series C funding, setting a world record for an AI company and bringing its value to an estimated US$4.5 billion to make it the world’s most valued AI startup.
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.
Chinese netizens are all ears for the company’s “hearty” AI-powered music recommendations. In an interview with Synced, NetEase Data Scientist Jia Xu and Product Manager Bowen Shen explained the NetEase system, which learns how to predict what songs will resonate with a user’s particular taste in music…
In applying the adversarial training, this paper adopts distributed word representation, or word embedding, as the input, rather than the traditional one-hot representation. The reason lies in the fact that the higher dimensionality the input has, the more likely it is to be disturbed by noise.