We present RenderFormer, a neural rendering pipeline that directly renders an image from a triangle-based representation of a scene with full global illumination effects and that does not require per-scene training or fine-tuning. Instead of taking a physics-centric approach to rendering, we formulate rendering as a sequence-to-sequence transformation where a sequence of tokens representing triangles with reflectance properties is converted to a sequence of output tokens representing small patches of pixels. RenderFormer follows a two stage pipeline: a view-independent stage that models triangle-to-triangle light transport, and a view-dependent stage that transforms a token representing a bundle of rays to the corresponding pixel values guided by the triangle-sequence from the view-independent stage. Both stages are based on the transformer architecture and are learned with minimal prior constraints. We demonstrate and evaluate RenderFormer on scenes with varying complexity in shape and light transport.

Through decades of support, Microsoft was an instrumental force helping China become the AI powerhouse it is today. Now, as the very thought of a U.S. company partnering in tech projects in China draws scrutiny from lawmakers, national security hawks, and human rights advocates, Microsoft could be forced to grapple with tough decisions surrounding the thriving AI ecosystem it fostered there. Microsoft established its research lab in Beijing in 1998, when it was a pioneer paving the way for AI research and business collaborations between the U.S. and China. It was three years before China joined the World Trade Organization, a time when President Bill Clinton actively pushed for closer trade ties with the country, and when AI was mostly the stuff of sci-fi pipe dreams. Since then, Microsoft Research Asia, or MSRA, has been known as one of the most influential hubs of AI research in the world, advancing speech recognition, natural language and image processing, and other deep-learning work, spreading its discoveries far and wide. Elements of research conducted at MSR China have been used to build Microsoft's advertising, chatbots, Bing search, Windows, Xbox, Azure Cloud, and other products used everywhere.

The purpose of the Association for the Advancement of Artificial Intelligence, according to its bylaws, is twofold. The first is to promote research in the area of AI, and the second is to promote the responsible use of these types of technology. The result was a 35th AAAI Conference on Artificial Intelligence (AAAI-21) schedule that broadens the possibilities of AI and is heavily reflective of a pivotal time in AI research when experts are asking bigger questions about how best to responsibly develop, deploy, and integrate the technology. Microsoft and its researchers have been pursuing and helping to foster responsible AI for years--developing innovative AI ethics checklists and fairness assessment tools like Fairlearn, establishing the Aether Committee to make principle-based recommendations, and laying out guidelines for human-AI interaction, to name only a few of the milestones in this area. As a natural extension, researchers from Microsoft are presenting papers at this year's AAAI that show the wide net they're casting when it comes to developing responsible AI and using it for applications that do good.

In 1998, China was hardly a technological rival to the US. With only 7 million internet users, fewer than 26 million personal computers, and an ecommerce industry that generated a paltry $42 million the following year, it was considered a laggard compared with many other countries. But Microsoft, then the world's richest and most powerful tech company, recognized the potential. That year, then-CEO Bill Gates created Microsoft Research China, an engineering outpost in Beijing to tap into a pool of talent and establish ties to the country's tech scene. In the following years, Microsoft launched internet operations in China when other US tech companies were stymied.

Microsoft Executive Vice President Harry Shum (Shen Xiangyang), the head of Microsoft AI and Research, will leave the tech giant in early 2020. A Microsoft spokesperson confirmed the departure of the 23-year Microsoft veteran, who will continue advising CEO Satya Nadella and company co-founder Bill Gates. Microsoft CTO Kevin Scott assumes Shum's position as head of Microsoft AI and Research effective immediately. Microsoft established its AI and Research Group in 2016 and chose Shum to lead it. At that time, the 5,000 person group comprised Cortana, Bing, and Ambient Computing and Robotics engineering teams mixed with parts of Microsoft Research.

Microsoft Research Asia (MSRA) has achieved eight top places in the recent machine translation challenge organized by the 2019 fourth Conference on Machine Translation (WMT19), out of the eleven tasks it undertook. Overall, there are nineteen machine translation categories in WMT this year. MSRA achieved first place in machine translation tasks for Chinese-English, English-Finnish, English-German, English-Lithuanian, French-German, German-English, German-French and Russian-English. Three other tasks were placed second in their respective categories, which included English-Kazakh, Finnish-English and Lithuanian-English. As one of the leading machine translation competition globally, WMT is a platform for leading researchers to demonstrate their solutions, as well as to understand the continuous evolvement of machine translation technology. Now in its 14th year, more than 50 teams globally from technology companies, leading academic institutions and universities participated in a bid to demonstrate their machine translation capabilities.

A comprehensive, cross-framework solution to convert, visualize and diagnosis deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network. In MMdnn, we focus on helping user handle their work better. This project is designed and developed by Microsoft Research (MSR). We also encourage researchers and students leverage this project to analysis DNN models and we welcome any new ideas to extend this project.

As the old saying goes: Time is money. And, that's never been truer than in the competitive world of international shipping, where unforeseen delays or miscalculations can cost millions of dollars. Thousands of cargo ships ply the oceans and keep world trade moving ahead every day. But poor weather, congested ports, equipment breakdowns, and mishaps of all kinds make for anything but smooth sailing. An unexpected delay for just one vessel can sometimes cascade into a logistical nightmare for an entire fleet with schedules thrown out of kilter across multiple ports and trading hubs – impacting the flow of globalized supply chains.

Hong Kong-based shipping company Orient Overseas Container Line Limited (OOCL) has partnered with Microsoft's research arm to improve network operations and achieve efficiencies within the shipping industry through applied Artificial Intelligence (AI) research. The partnership, between OOCL and Microsoft Research Asia (MSRA), will apply deep learning research to shipping network operations with a projected $10 million annual operational cost saving. The collaboration is also expected to nurture over 200 AI developers over the next 12 months. "With MSRA's efforts and expertise, we expect to save around 10 million in operation costs annually by applying the AI research and techniques for optimizing shipping network operations from our most recent 15-week engagement," said Steve Siu, Chief Information Officer of OOCL. "Moving forward, we will embark on an 18-month joint-partnership in research and development to apply deep learning and reinforcement learning in shipping network operations. Moreover, MSRA will assist us in training over 200 AI engineers by conducting machine learning and deep learning sessions at the Hong Kong Science Park over the next 12 months. We look forward to strengthening our partnership with MSRA to leverage AI research and innovations to drive digital transformation in the shipping industry and to exchange knowledge among our top developers so that we can better address customer needs with advanced technologies and predictive analytics."

This article was originally posted on LinkedIn. In a few short years, Artificial Intelligence (AI) has been thrust into the limelight – elevating itself from a far-fetched, science-fiction topic to one that is currently dominating my conversations with customers, partners and industry leaders across Asia. The journey to where we are today with AI is a long one – almost seven decades in the making. However, in the last few years, the convergence of big data, ubiquitous and powerful cloud computing, along with breakthroughs in software algorithms and machine learning have made exciting new scenarios in AI deployment a possibility. AI today is at the center of the digital transformation of organizations and even nations.