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Analytics and Big Data are everywhere – but perhaps not always in the first places you look
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Invited talk by Craig Saunders. Royal Holloway, University of London, UK 10th of March 2014.
Abstract: There is no denying the fact that terms like Analytics and Big Data are being used everywhere: in business, research and even mainstream media. One can follow many discussions on what these terms mean, where on the hype curve we currently are, whether or not there is a really need for new technologies and research for Big Data — the list of debates is practically endless. The bottom line is that we are producing, storing and have access to, more and more data. When you couple this with increasingly advanced technological enablers to use that data in a myriad of ways, and a tech-savvy world of corporations and end-users that not only demand but expect services that can only be delivered by mining and using such data, you end up with a highly competitive business space of commercial leaders who are already capturing the still maturing analytics business market, and also a range of possibilities and opportunities that are not yet completely understood. This is where we are today, but what are the real implications of this for companies operating in this space, or indeed individuals on the ground with specific skills and expertise? A cursory look at the many reports may suggest the answer is simple: data scientists can unlock hidden secrets and give you access to this exciting new world. This is certainly a part of the puzzle, but unsurprisingly the reality is much more complex. In this talk I will discuss some of the challenges faced by corporations in navigating the Analytics and Big Data oceans, illustrate this with some examples of how Xerox is using novel analytics research to provide innovative services, and give a perspective on what all this means for today and tomorrow. What are the big challenges? What are the trends? Where is expertise most needed? And what does that mean for those of us who work in the analytics and big data world?
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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