Asia Faculty Summit 2016

This session examines the future direction of robotics research. As a background movement, AI is sparking great interest and exploration. In order to realize AI in human society, it is necessary to embody such AI in physical forms, namely to have physical forms. Under such circumstance, this session explores and clarifies the current direction of basic robotics research. Thorough examination of what types of research components are missing, and how does such capability development affect the directional paths of research will be highlighted.

It is evident that when machine learning meets big data, it is vital to have a powerful system/infrastructure to support the distributed training task. In recent years, people have used different frameworks for this purpose, including iterative MapReduce, parameter server, and data flow. In this session, we are going to discuss how to enhance these frameworks from both system and algorithmic perspectives, and how to implement parallel machine learning algorithms under these frameworks. In addition, we will discuss the future trend of machine learning system and infrastructure and how to push its frontier through close collaboration between academia and industry.

In recent years, the growing research on deep learning and reinforcement learning has led to many exciting breakthroughs. However, on the other hand, there are still many open problems regarding deep learning and reinforcement learning. In this session, we will reflect, problem-solve and discuss key missing elements, as well as synthesizing the opportunities for academia and industry to further advance this field.

Generative learning and discriminative learning are two major approaches in machine learning. The fast development of deep learning demonstrates the power of discriminative learning. In recent years, some have started to integrate generative learning into the deep learning process in order to incorporate prior knowledge. In this session, we will discuss the pros and cons of each approach and how to seamlessly integrate them together.

In this session, we will discuss machine learning from two extremes, theory and application. The goal is to bring theory and application researchers together and inspire each other’s work, so that the theory research can become more targeted and the application research can have better theoretical guarantees.

Since the launch of a spoken dialogue based intelligent assistant, dubbed Siri, on the iPhone 4S in October 2011, major tech giants such as Apple, Microsoft, Google, Amazon, and Facebook have all invested significantly to develop such type of virtual-assistant Apps and/or services over the past few years. Despite all the hype, none of them have become pervasive yet. In this session, several leading experts from academia in Asia are invited to discuss how to make spoken dialogue based intelligent agent pervasive by addressing the following technical challenges: (1) Better speech capturing and processing for robust distant speech recognition; (2) Robust and scalable spoken language understanding; (3) Robust and flexible dialogue management.

Given the investment and evidence of progress in Artificial Intelligence some suggest that it is merely a matter of time until AI can match, complement or surpass human intelligence. This session looks at recent research advances in machine learning and cognitive science and discusses the needs and design principles to support fundamental research in AI. Together we will look at how to push AI technology towards more natural human-AI communication and interaction that will facilitate social learning and collaborations between humans and AI agents.

Capturing and reconstruction 3D real world (scene, objects, and dynamic human characters) plays important role in VR/AR applications. However, real time high quality 3D capturing and reconstruction is still a very challenging task. In this session, we are going to do some reflection on this important research field, and discuss what’s missing and what are the opportunities for academia and industry to further advance this field.

In recent years, the growing research on social multimedia and visual Q&A has led to many exciting breakthroughs. However, on the other hand, there are still many open problems regarding social multimedia and visual Q&A. In this session, we are going to do some reflection on this important research field, and discuss what’s missing and what are the opportunities for academia and industry to further advance this field.

In recent years, the growing research on deep learning has led to many exciting breakthroughs in vision and multimedia communities. However, on the other hand, there are still many open problems regarding deep learning for vision and multimedia. In this session, we are going to do some reflection on this important research field, and discuss what’s missing and what are the opportunities for academia and industry to further advance this field.

Video is the biggest big data that contains an enormous amount of information. Recently computer vision and machine learning technologies have been significantly leveraged to turn raw video data into insights to facilitate various applications and services. In this session, we intend to do some reflection on this important research field, and discuss what’s missing and what are the opportunities for academia and industry to further advance this field.

Computer vision has continued to be of the hottest and most active research areas in artificial intelligence, both in academia and industry. Although we have made tremendous progress in the past ten years which has opened vast opportunities, we are still facing multitudes of challenges in building computer vision systems that are as robust as the human vision system. For example, it still remains to be an open challenge to build a computer vision system that can learn and evolve in a similar way as a human vision system. This session intends to gather thoughts on the future trends of computer vision, while savoring the success of this fast evolving field in the past several years.

Gaining an in-depth understanding of users is critical for building artificial intelligence systems. With the rapid development of positioning, sensing and social networking technologies, large quantities of human behavioral data are now readily available. They reflect various aspects of human mobility and activities in the physical world. The availability of this data presents an unprecedented opportunity to user understanding. In addition, recent studies in psychology suggest that numerous psychological features, such as personality traits, are highly correlated to user behaviors. It will be interesting to study how we can design computational frameworks for inferring psychological features of users, based on their data at different levels and across heterogeneous domains, and how these frameworks can benefit the development of artificial intelligence systems. In this session, we plan to invite researchers from computer science, psychology and cognitive science areas. We will brainstorm innovative ideas, technologies, systems and applications along this interdisciplinary research direction.

Urban computing connects ubiquitous sensing technologies, advanced data management & machine learning models, and novel visualization methods to create win-win-win solutions that improve urban environment, human life quality, and city operation systems.

Machine computable knowledge is playing a key role and will play a more important role in the field of artificial intelligence. Only after the turn of this century, massive amounts of structured and semi-structured data that directly or indirectly encode human knowledge became widely available, turning the knowledge extraction, representation and computing problems into a computational grand challenge with feasible solutions in sight. Such world knowledge in turn enhances various applications such as semantic search, automatic question-answering, recommendation systems, chat engines in Web and enterprise scenarios, etc.

Conversation as a platform (CAAP), or chatbots, builds a seamless linked set of technologies ranging from chit-chat for social connection, to botification of search and question-answering for informational needs and up to dialogue systems for task completion. Chatbots will have profound impact as previous shifts we’ve had such as graphical user interface, the web browser and the touchscreen. Many companies have begun investing heavily in this area with the promise of booking a meeting room or buying a cup of coffee as easy as sending a short text message on a social network. It combines the human language (speech and natural language processing) with the power of cloud computing and big data and applies it pervasively to computing. Microsoft has built impactful Xiaoice, Rinna and Tay for markets in China, Japan and US. We would like to introduce our recent progress in this area and invite researchers and professors from universities to join us to present and discuss important topics in CAAP including but not limited to chit-chat, QnA and dialogue systems. We hope to find a few interesting topics for future collaborations.

In recent years, research in machine translation continues to make significant progress after statistical methods were widely adopted since 1990’s, especially in the area of large-scale learning from big data and employing deep learning methods to improve translation. In this session, we are going to go through recent breakthroughs in the machine translation area, and discuss directions, methods and challenges in the next step of machine translation research and real-world system construction.

Recent years have witnessed dramatic progress in machines exhibiting intelligent behaviors, ranging from chat bots performing complicated logistic scheduling or serving as TV news anchors, to machine beating humans in playing Go or recognizing objects in photo images. As the core algorithms behind these advancements were largely proposed and attempted since the last two decades, one plausible explanation to the sudden renaissance of machine intelligence points to the large amount of data available for training, and the cloud computing infrastructure being more mature and affordable for handling large scale data processing. In this panel discussion, we will be discussing whether and how these trends can be leveraged in the areas of scholarly communications and education. We will address the following: What the data available so far can tell us? Is it time for the community to jointly evolve beyond age-old impact metrics (e.g., JIF, h-index) that are known to have serious drawbacks? How can we improve research with big data? In education, how will classrooms over the next decades be delivered? All these are a slice of possible questions the panel will discuss and debate.

AI research at Microsoft Research Asia will be introduced, including machine learning, computer vision, natural language processing, knowledge mining and urban computing based on big data. Specifically, I will share our research on developing new learning algorithms and distributed machine learning platform for training very big models on big data based on heterogeneous hardware (e.g. CPU, GPU and FPGA cluster). In addition to deep learning, we are also working on knowledge mining and symbolic learning that integrates facts, common sense, and logic rules in a unified knowledge representation for machine comprehension of text. I will introduce these research works and show how they have been used to build artificially intelligent and socially engaging conversational agents such as XiaoIce and Microsoft’s Cognitive Services.