AI Technology & Industry Review
The world’s largest and most exciting technology show, CES officially kicks off this morning in Las Vegas, USA. With an abundance of products to present, tech giants such as Nvidia, Qualcomm, Samsung, Intel started revving up their engines two days ago, hosting press conferences to showcase their “New Year’s Resolutions.”
10 AI News You Need To Know From December W 4 – January W 1
To welcome 2019 with some fresh AI insights, Synced posed three questions to forty researchers and experts at last month’s NeurIPS 2018 conference in Montréal, Canada.
Synced Global AI Weekly January 6th
At the 2018 World AI Conference in Shanghai, Tencent Cybersecurity Group and the Cybersecurity Innovation Institute co-published their AI Empowers Cyberspace Security: Patterns and Practices report. The work was sponsored by the Shanghai government.
It all started with a tweet from Google Japan Data Project Manager Suzana Ilić: “Yesterday someone (ML, CS PhD, Stanford) said he would not hire a person who is online educated in Machine Learning. Who here agrees and who thinks differently?” The question triggered a long and occasionally heated discussion that spread from Ilić’s twitter across the machine learning community.
It did not take long to see the first major AI talent reshuffling of 2019. Multiple sources are now confirming that reputed AI researcher Dr. Tong Zhang left his position as Executive Director of Tencent AI Lab effective December 31. Rumours suggest Zhang might return to teaching.
Researchers at the Korea Advanced Institute of Science and Technology and Pohang University of Science and Technology have introduced a machine learning algorithm system, InstaGAN, which can perform multiple instance-aware image-to-image translation tasks — such as replacing sheep in photos with giraffes — on multiple image datasets.
Tsinghua Natural Language Processing Group (THUNLP) has published a great reading list for any budding AI researchers whose New Year’s resolution is to study machine translation. The list compiles the most influential machine translation papers from the past 30 years, spotlighting the 10 most important contributions to the development of machine translation.
Alibaba Cloud recently announced that it has open sourced Mars — its tensor-based framework for large-scale data computation — on Github. Mars can be regarded as “a parallel and distributed NumPy.” Mars can tile a large tensor into small chunks and describe the inner computation with a directed graph, enabling the running of parallel computation on a wide range of distributed environments, from a single machine to a cluster comprising thousands of machines.