Microsoft Research and the Evolution of Computing
Limits in computing power and our ability to interact with computers have also imposed limits on our understanding of the world around us. Increasingly, those limits are being removed, clearing the way for new advances in almost every kind of human endeavor. Rick Rashid, Microsoft chief research officer and head of Microsoft Research, will present his vision of the future of computing research in light of these breakthroughs and the opportunities that lie ahead.
New Directions in Computer Science
Computer science is undergoing a fundamental change. Over the last 40 years the field was concerned with making computers useful. Focus was on programming languages, compilers, operating systems, data structures and algorithms. These are still important topics but with the merging of computing and communication, the emergence of social networks, and the large amount of information in digital form, focus is shifting to applications such as the structure of networks and extracting information from large data sets. This talk will give a brief vision of the future and then an introduction to the science base that is forming to support these new directions in computer science.
Computation Challenges for Creating Autonomous Systems
The current computing challenges for creating mobile autonomous systems that can interact in new ways with the physical world, on the ground, in water, and in the air. Recent progresses in Autonomous Mobile Networks are distributed ad-hoc networks of robots that can sense, actuate, compute and communicate with each other using point-to-point multi-hop communication. The nodes in such networks include static sensors, mobile sensors, robots, animals, and humans. Such systems combine the most advanced concepts in perception, communication and control to create computational systems capable of large-scale interaction with the environment, extending the individual capabilities of each network component to encompass a much wider area, range of data, and control capabilities.
Towards a Theory of Trust in Networks of Humans and Computers
How can I trust the information I read over the Internet? We argue that a general theory of trust in networks of humans and computers must be built on both a theory of behavioral trust and a theory of computational trust. This argument is motivated by increased participation of people in social networking, crowdsourcing, human computation, and socio-economic protocols, e.g., protocols modeled by trust and gift-exchange games, norms-establishing contracts, and scams. User participation in these protocols relies primarily on trust: trust in both the computational elements in the network and the human element. Thus, towards a general theory of trust, to computational trust, we add behavioral trust, a notion from the social and economic sciences. Behavioral trust captures participant preferences (i.e., risk and betrayal aversion) and beliefs in the trustworthiness of other protocol participants. We argue that a general theory of trust should focus on the establishment of new trust relations where none were possible before. This focus would help create new economic opportunities by increasing the pool of usable services, removing cooperation barriers among users, and at the very least, taking advantage of network effects. Hence a new theory of trust would also help focus security research in areas that promote trust-enhancement infrastructures in human and computer networks.
The Pipeline from Computing Research to Surprising Inventions
One of the most exciting aspects of computer science is that the results of basic research so often end up being applied in completely unexpected ways. At Microsoft Research, we actively seek out these surprising outcomes, by building a pipeline that connects long-term, blue-sky research to technological innovations. This talk will delve into the details of three examples, one each in the areas of entertainment, cloud computing, and personal productivity.
Divide-and-Conquer and Statistical Inference for Big Data
Divide-and-conquer is a natural computational paradigm for approaching Big Data problems, particularly given recent developments in distributed and parallel computing, but some interesting challenges arise when applying divide-and-conquer algorithms to statistical inference problems. One interesting issue is that of obtaining confidence intervals in massive datasets. The bootstrap principle suggests resampling data to obtain fluctuations in the values of estimators, and thereby confidence intervals, but this is infeasible with massive data. Subsampling the data yields fluctuations on the wrong scale, which have to be corrected to provide calibrated statistical inferences. The new procedure, the bag of little bootstraps, circumvents this problem, inheriting the favorable theoretical properties of the bootstrap but also having a much more favorable computational profile. Another issue is the problem of large-scale matrix completion. Here divide-and-conquer is a natural heuristic that works well in practice, but new theoretical problems arise when attempting to characterize the statistical performance of divide-and-conquer algorithms. Here the theoretical support is provided by concentration theorems for random matrices, and a new approach to this problem bases on Stein's method.
Transforming the Impossible to the Natural
Reading science fictions over the past one hundred years, one sees many seemingly impossible machines and services, which are now not only widely available, but have become accepted as natural. In this talk, Dr. Hsiao-Wuen Hon will share examples which show how technologies developed in research labs have impacted real life user experiences. For example, body gesture, speech, natural user intent understanding, and other new usage scenarios have all recently impacted how users utilize computing. Looking forward, Dr. Hsiao-Wuen Hon sees exciting opportunities for research to further extend what is considered natural when using computers. What's natural in computing at the end of 21st century will be drastically different than what we find common today.