What is driving the'robot age' and how can businesses leverage the capabilities being produced? Artificial intelligence is one of the 21st century's dominant fields of innovation. So it's no surprise that cutting-edge robots and other advanced smart machines fall under the rapidly expanding Internet of Things, which is projected to reach 25 billion devices by 2020. Every day we're reading headlines on machines getting'smarter' and robotics transforming a variety of industries, but what's driving this'robot age' and how can businesses successfully integrate and leverage this advanced automation? It's clear that artificial intelligence (AI) is a new industrial revolution, one that's driving the rise of robotics. But AI won't just be an industry – it will be part of every industry.

Imagine if computers could read and interpret documents. Humans could focus their efforts on understanding what analysis results mean to make better decisions. Our interest, at Dynamic Risk, is to improve the safety and reliability of energy pipeline networks by taking full advantage of the vast amounts of data locked in cumbersome formats, handwritten documents, drawings, photographs, and in paper archives. We want to fundamentally change how we ask questions and receive answers. Today, we ask questions based on the data we have available in structured databases.

Sources of inspiration is one thing we do not lack in machine learning. This is what, for me at least, makes machine learning research such a rewarding and exciting area to work in. We gain inspiration from our traditional neighbours in statistics, signal processing and control engineering, information theory and statistical physics. But our fortune continues, and we can take further inspiration from biological and evolutionary systems and, of importance to this series, the cognitive sciences of sociology, psychology and neuroscience. I previously explored important inspirations for machine learning offered by neuroscience and unpacked the role of prediction, sparsity, modularity and complementary learning in building learning systems.

IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing. We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.

IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing.  We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.  

Ubuntu is partnering with IBM Power Systems and OpenPOWER to bring the POWER8 architecture into the mainstream of dev ops practices and cloud operations. In this short video featured at IBM Interconnect, Mark Shuttleworth talks about the number one workload to create competitive advantage, and how the OpenPOWER foundation has enabled IBM to bring innovation back into the data centre.

IBM Research and the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology have joined forces to further develop the scientific field of machine vision – a core aspect of artificial intelligence. Big Blue and MIT will build the IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension, or BM3C, in Cambridge, Mass. Together they plan to develop cognitive computing systems that mimic the human ability to understand and integrate input from multiple sources for use in a variety of computer applications in industries such as healthcare, education, and entertainment. MIT researchers will collaborate with IBM scientists and engineers who will provide technology expertise and advances from the IBM Watson platform. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish.

Is your firm prepared to welcome in an era of legal practice using artificial intelligence (AI)? Where is this technology headed and why must law firm leaders fully understand how cognitive computing works in order to benefit from it? With the vast amounts of structured and unstructured data firms have to deal with, there is increasing pressure from nimble technologies and clients to attract AI and cognitive technologies. Understanding the market implications of digital business information and trends is now key to attaining the best possible performance, revenue gains and cost savings. Berwin Leighton Paisner was one of the first firms to embrace AI in the UK, developing its'contract robot' which promised to complete work in seconds that would have otherwise taken a team of paralegals months to do.

Prof Raghavan also serves as the Director of the NSF-sponsored Industry/ University Cooperative Research Center for Visual and Decision Informatics. In this role, he co-ordinates several multi-institutional, industry-driven research projects and manages a budget of over 500K/year. From 1997 to 2003, he led a 2.3M research and development project in close collaboration with the USGS National Wetlands Research Center and with the Department of Energy's Office of Science and Technical Information on creating a digital library with data mining capabilities incorporated. His research interests are in Big Data, data mining, information retrieval, machine learning and Internet computing. He has published over 250 peer-reviewed research papers --appearing in top-level journals and proceedings - that cumulatively accord him an h-index of 31, based on citations.

IBM Research and the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology have joined forces to further develop the scientific field of machine vision – a core aspect of artificial intelligence. Big Blue and MIT will build the IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension, or BM3C, in Cambridge, Mass. Together they plan to develop cognitive computing systems that mimic the human ability to understand and integrate input from multiple sources for use in a variety of computer applications in industries such as healthcare, education, and entertainment. MIT researchers will collaborate with IBM scientists and engineers who will provide technology expertise and advances from the IBM Watson platform. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish.