With an artificial intelligence revolution overtaking the workforce, it's clear that how work is done is going to change. That brings with it worry, especially for employees concerned about job security. But with the right skills, AI and automation don't have to represent a loss. In fact, the coming rush of automation represents opportunity for many. Katherine LaVelle, managing director of talent and organization for Accenture, said, "The majority of CEOs that we talked to said there will be an increase in jobs as a result of AI."

Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction error and pre-obtained approximate similarities. To improve the representation ability, a novel unsupervised Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF) framework is proposed for clustering high-dimensional data. Specifically, RFA-LCF integrates the robust flexible CF by clean data space recovery, robust sparse local-coordinate coding and adaptive weighting into a unified model. RFA-LCF improves the representations by enhancing the robustness of CF to noise and errors, providing a flexible constraint on the reconstruction error and optimizing the locality jointly. For robust learning, RFA-LCF clearly learns a sparse projection to recover the underlying clean data space, and then the flexible CF is performed in the projected feature space. RFA-LCF also uses a L2,1-norm based flexible residue to encode the mismatch between the recovered data and its reconstruction, and uses the robust sparse local-coordinate coding to represent data using a few nearby basis concepts. For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates. By updating the local-coordinate preserving data, basis concepts and new coordinates alternately, the representation abilities can be potentially improved. Extensive results on public databases show that RFA-LCF delivers the state-of-the-art clustering results compared with other related methods.

It began with Mark Zuckerberg's leaked email on the future of Facebook. Whether it be through an augmented google glass-like device or within a fully immersed VR headset such as the Occulus Quest - One thing is for sure. Facebook is taking VR very seriously, and if I can't be so bold myself, it's their primary focus. In his email, dated 2014, Mark talks of acquiring Unity, a gold standard video game and development engine. His reason, to facilitate the exponential growth of VR/AR content by enabling video game developers to create content for their AR/VR platform.

Technology is and has always been a crucial part of finance. From the first promissory notes (banknotes) in the Netherlands and China, there was a race with counterfeiters that parasitically undermined trust. As in political communication, technology is the message, rather than merely "a tool": when it comes to money, trust is not just instrumental, it is fundamental. With cashless payments being the norm and social media platforms weαving an additional layer of involvement in our social data web – Amazon, Google, Facebook, Apple – Artificial Intelligence (AI) is already in our wallets, business, and financial affairs. In a non-western setting, one may refer to the Chinese "social rating" system, which allows the state to value and evaluate social behaviour patterns, creating a link to individual credit rating. That is a far-reaching "Panopticon" structure that would be unthinkable without AI.

For most people, science (and especially AI), has failed. It has failed to grant them immortality or even a more meaningful life. It also doesn't look like it will any time soon (if ever). These things, unfortunately, can only be found in the neat packages of religion and I shudder to think humanity is slowly inching its way back to that sort of thing.

Artificial intelligence and machine learning must be judiciously used, such as when monitoring internet of things devices, says David De Roure, professor of e-research at the University of Oxford, who offers insights on IoT risk management. "We can use AI and ML to improve the resilience of our systems and improve the responsiveness to the changing circumstances," De Roure says in an interview with Information Security Media Group during IoT India Congress held in Bengaluru. "But every time we add anything to these already complex systems [for IoT devices], we are introducing new complexity, new vulnerabilities, new opportunities both for failure and for susceptibility to attack." De Roure is a professor of e-research at the University of Oxford. From 2009 to 2013 he held the post of National Strategic Director for e-Social Science and was subsequently a Strategic Advisor to the UK Economic and Social Research Council.

Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He received an A.B. degree from Harvard in 1966, an S.T.B. degree from Harvard Divinity School in 1969, and a Ph.D. from Yale in 1975. Wilkinson wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. After the company grew to 50 employees, he sold SYSTAT to SPSS in 1994 and worked there for ten years on research and development of visualization systems. Wilkinson subsequently worked at Skytree and Tableau before joining H2O.

For his next book, Joseph LeDoux knew he had to go deep. He had to go back in time, way back, 3.5 billion years ago. The author of the seminal The Emotional Brain, followed by Synaptic Self and Anxious, sensed a missing element in those books on how brain anatomy and function shape human behavior and emotions. In his new book, The Deep History of Ourselves: The Four-Billion-Year Story of How We Got Our Conscious Brains, LeDoux takes readers back to the emergence of life on Earth to show what our protean brains today owe to the canny survival of Protozoa. "I started asking, 'How far back in evolution does the ability to detect and respond to danger go?'" he said to me in a recent interview at his home in New York City. LeDoux directs the Emotional Brain Institute at New York University. In his research and previous books, he has shown the human brain processes that detect and respond to danger differ from the conscious experiences of fear itself. "I felt I needed to understand more about this process," he said.

And so, you want your teams to reflect the humans that you are going to be serving. So, for us, we want our team to reflect the customer population of our robots because we want to be able to ask the right questions. We want to ask the questions that our customers are asking. We don't want to ask the questions that nerds like me want to know. I have a very specific set of things that I would love our robots to have, which you know, big whoop.

Justin Boutilier uses optimization and machine learning to improve healthcare access, delivery and quality, particularly in low- and middle-income settings. As a second-year PhD student at the University of Toronto, Justin Boutilier spent four weeks in Dhaka, Bangladesh, investigating ways to curb ambulance response times in the bustling capital of a developing country. He quickly got a firsthand look at the scope of the challenge: The roughly 10-mile trip from his hotel to meetings in the city took about three hours. "You could walk faster," he says, "but there's no sidewalk, so it's kind of dangerous." Boutilier, who has joined the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison as an assistant professor, uses optimization and machine learning to improve healthcare access, delivery and quality, particularly in low- and middle-income settings.