Taking a look into the future, Ericsson (NASDAQ: ERIC) ConsumerLab presents the sixth edition of its annual trend report The 10 Hot Consumer Trends for 2017 and beyond. AI is an important theme this year and consumers see it playing a much more prominent role than before - both in society and at work. In fact, 35 percent of advanced internet users want an AI advisor at work, and one in four would like an AI as their manager. At the same time, almost half are concerned that AI robots will soon make a lot of people lose their jobs. Two in five believe smartphones will learn their habits and perform activities on their behalf automatically.

In the summer of 1967, Secretary of Defense Robert McNamara commissioned a group of thirty-six scholars to write a secret history of the Vietnam War. The project took a year and a half, ran to seven thousand pages, and filled forty-seven volumes. Only a handful of copies were made, and most were kept under lock and key in and around the Beltway. One set, however, ended up at the RAND Corporation, in Santa Monica, where it was read, from start to finish, by a young analyst there named Daniel Ellsberg. Ellsberg was dismayed by what he learned. For a generation, the U.S. government had been lying to the American people about the Vietnam War. He put the first of the volumes in his briefcase, praying that the security guards at RAND would not stop him, and made his way to a small advertising agency in West Hollywood, where a friend told him there was a Xerox machine he could use. "It was a big one, advanced for its time, but very slow by today's standards," Ellsberg writes in his 2002 autobiography, "Secrets: A Memoir of Vietnam and the Pentagon Papers": It could do only one page at a time, and it took several seconds to do each page. I tried pressing the book down on the glass to do two pages at a time, but the middle section was faint and uneven. Fortunately the books were bound with metal tapes through holes so they could be taken apart. . . . The machine didn't collate, and the bar had to come back and travel just as slowly for each copy.

Ford CEO Mark Fields holds Velodyne Puck LIDAR sensor at a press conference at CES in Las Vegas in January. Carmakers and tech firms competing to develop automated vehicles seek a combination of sensors and cameras that provide maximum perception and visibility of surroundings at a cost that's manageable for mass production. Velodyne, a leading maker of laser-based LiDAR, or Light, Detection and Ranging, sensors, says it has designed a new solid-state version of its technology that provides 3D imaging for automated vehicle systems that will cost less than $50 per unit when manufactured at high volume. That's a fraction of the $8,000 cost of its current mechanical spinning LIDAR devices used in prototype robotic cars. The new design "creates a true solid-state LiDAR sensor, while significantly raising the bar as to what can be expected from LiDAR sensors as far as cost, size and reliability," company founder and CEO David Hall said in a statement.

I'm fairly new to data mining myself, but I've been reading a book called "Data Mining: Practical Machine Learning Tools and Techniques" by Witten and Frank. The book is a little light on theory, but it has a pretty good overview of many different techniques for data mining, and it has enough detail that you can start applying some of the techniques on your data immediately. I'm using the book in conjunction with a course on decision trees that I'm taking at statistics.com, which I would recommend. "Elements of Statistical Learning" seems to be the closest thing to an authoritative text on data mining that I've found, and it has more theory than the book I'm using for my class, so that may be of use to you. I'm planning on studying it more after I've finished taking my class.

At the start of December, Stephen Hawking issued a stark warning: Automation and AI are going to destroy middle-class jobs, risking massive political upheaval. "The automation of factories has already decimated jobs in traditional manufacturing," the famed physicist wrote, "and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes." It's an issue that Shahar Waiser knows about first-hand: In the coming years and decades, almost his entire workforce will become obsolete. The Russian-born entrepreneur is the CEO of Gett -- a taxi app used by thousands of cabbies in more than 100 cities around the world. And all of their jobs are at risk of being replaced by self-driving cars.

An artificially intelligent computer system is making breast cancer treatment recommendations on a par with those of cancer doctors, a new study reports. The IBM computer system -- called Watson Oncology -- made treatment recommendations that jibed nine out of 10 times with those of a multidisciplinary board of doctors at a top cancer hospital in India, researchers say. In cases involving more complex cancers, however, the computer did not hit that 90 percent mark. Another version of Watson famously defeated two former winners on the game show "Jeopardy!" in 2011, winning a first-place prize of $1 million. It then spends another minute reviewing all existing medical evidence regarding their particular form of cancer, said study co-author Dr. S.P. Somashekhar, chairman of the Manipal Comprehensive Cancer Center in Bengaluru, India.

One of our greatest challenges in today's society is responding to the impact of technology automation. Over the last decade, technology has increasingly displaced jobs resulting in a reduction of the middle class and the widening gap of income inequality. Other factors such as offshoring play a role in job loss but the impact of technology is in full steam and there is no end in sight. My concern is that our society hasn't come to appreciate the extent of the issue and doesn't have a thoughtful plan to address it. The future of the middle class depends on our ability to comprehend the changing world technology has presented, and how we respond to close the jobs gap.

Coalition formation typically involves the coming together of multiple, heterogeneous, agents to achieve both their individual and collective goals. In this paper, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions. We focus on this type of problem given that in many real-world applications, agents may be connected by a communication network or only trust certain peers in their social network. We propose a novel representation of this problem based on the concept of edge contraction, which allows us to model the search space induced by the GCCF problem as a rooted tree. Then, we propose an anytime solution algorithm (CFSS), which is particularly efficient when applied to a general class of characteristic functions called $m+a$ functions. Moreover, we show how CFSS can be efficiently parallelised to solve GCCF using a non-redundant partition of the search space. We benchmark CFSS on both synthetic and realistic scenarios, using a real-world dataset consisting of the energy consumption of a large number of households in the UK. Our results show that, in the best case, the serial version of CFSS is 4 orders of magnitude faster than the state of the art, while the parallel version is 9.44 times faster than the serial version on a 12-core machine. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems of agents (i.e., with more than 2700 agents).

Whereas CNNs have demonstrated immense progress in many vision problems, they suffer from a dependence on monumental amounts of labeled training data. On the other hand, dictionary learning does not scale to the size of problems that CNNs can handle, despite being very effective at low-level vision tasks such as denoising and inpainting. Recently, interest has grown in adapting dictionary learning methods for supervised tasks such as classification and inverse problems. We propose two new network layers that are based on dictionary learning: a sparse factorization layer and a convolutional sparse factorization layer, analogous to fully-connected and convolutional layers, respectively. Using our derivations, these layers can be dropped in to existing CNNs, trained together in an end-to-end fashion with back-propagation, and leverage semisupervision in ways classical CNNs cannot. We experimentally compare networks with these two new layers against a baseline CNN. Our results demonstrate that networks with either of the sparse factorization layers are able to outperform classical CNNs when supervised data are few. They also show performance improvements in certain tasks when compared to the CNN with no sparse factorization layers with the same exact number of parameters.

The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis due to our limited ability to assess damaged neurons at a cellular level in vivo. We simulate the effects of neurodegenerative disease and TBI using convolutional neural networks (CNNs) as our model of cognition. We utilize biophysically relevant statistical data on FAS to damage the connections in CNNs in a functionally relevant way. We incorporate energy constraints on the brain by pruning the CNNs to be less over-engineered. Qualitatively, we demonstrate that damage leads to human-like mistakes. Our experiments also provide quantitative assessments of how accuracy is affected by various types and levels of damage. The deficit resulting from a fixed amount of damage greatly depends on which connections are randomly injured, providing intuition for why it is difficult to predict impairments. There is a large degree of subjectivity when it comes to interpreting cognitive deficits from complex systems such as the human brain. However, we provide important insight and a quantitative framework for disorders in which FAS are implicated.