Explicit high-order feature interactions efficiently capture essential structural knowledge about the data of interest and have been used for constructing generative models. We present a supervised discriminative High-Order Parametric Embedding (HOPE) approach to data visualization and compression. Compared to deep embedding models with complicated deep architectures, HOPE generates more effective high-order feature mapping through an embarrassingly simple shallow model. Furthermore, two approaches to generating a small number of exemplars conveying high-order interactions to represent large-scale data sets are proposed. These exemplars in combination with the feature mapping learned by HOPE effectively capture essential data variations. Moreover, through HOPE, these exemplars are employed to increase the computational efficiency of kNN classification for fast information retrieval by thousands of times. For classification in two-dimensional embedding space on MNIST and USPS datasets, our shallow method HOPE with simple Sigmoid transformations significantly outperforms state-of-the-art supervised deep embedding models based on deep neural networks, and even achieved historically low test error rate of 0.65% in two-dimensional space on MNIST, which demonstrates the representational efficiency and power of supervised shallow models with high-order feature interactions.

Manifold learning and dimensionality reduction techniques are ubiquitous in science and engineering, but can be computationally expensive procedures when applied to large data sets or when similarities are expensive to compute. To date, little work has been done to investigate the tradeoff between computational resources and the quality of learned representations. We present both theoretical and experimental explorations of this question. In particular, we consider Laplacian eigenmaps embeddings based on a kernel matrix, and explore how the embeddings behave when this kernel matrix is corrupted by occlusion and noise. Our main theoretical result shows that under modest noise and occlusion assumptions, we can (with high probability) recover a good approximation to the Laplacian eigenmaps embedding based on the uncorrupted kernel matrix. Our results also show how regularization can aid this approximation. Experimentally, we explore the effects of noise and occlusion on Laplacian eigenmaps embeddings of two real-world data sets, one from speech processing and one from neuroscience, as well as a synthetic data set.

Although artificial intelligence is here since a long time in many forms and ways, it's a term that quite some people, certainly IT vendors, don't like to use that much anymore – but artificial intelligence is very real, for your business too. Instead of talking about artificial intelligence (AI) many describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing or focus on several real-life applications of artificial intelligence that often start with words such as "smart", "intelligent", "predictive" and, indeed, "cognitive", depending on the exact application – and vendor. Despite the term issues, artificial intelligence is essential for and in, among others, information management, medicine/healthcare, data analysis, digital transformation, security (cybersecurity and others), various consumer applications, scientific advances, FinTech, predictive systems and so much more. There are many reasons why several vendors doubt using the term artificial intelligence for AI solutions/innovations and often package them in another term (trust us, we've been there). Artificial intelligence (AI) is a term that has somewhat of a negative connotation in general perception but also in the perception of technology leaders and firms.

Lonnie Johnson was brought up in Mobile Alabama in the 1960s, when black children were not expected to go far, but such was his talent for engineering that he worked for Nasa, and helped test the first stealth bomber. But as he explains here, the invention that made his fortune was a water pistol - the extremely powerful Super Soaker. It started with my dad. He gave me my first lesson in electricity, explaining that it takes two wires for electric current to flow - one for the electrons to go in, the other for them to come out. And he showed me how to repair irons and lamps and things like that. The kids in the neighbourhood took to calling me "the Professor".

An ambitious mission that will visit a comet and pluck a boulder from its surface to create an orbiting base for astronauts has been given the final go-ahead. A robot shipwill pluck a large boulder off an asteroid and sling it aroundthe moon, becoming a destination to prepare for futurehuman missions to Mars, the U.S. space agency has revealed. The so-called Asteroid Redirect Mission is estimated to costabout 1.4 billion not including launch costs and is targetedfor liftoff in December 2021. In the Spacecraft Structures Lab at NASA's Langley Research Center, the Asteroid Redirect Mission robotic contact and restraint system is prototyped and tested. A robot ship will pluck a large boulder off an asteroid and drag it into orbit around the moon, becoming a'testbed' for future human missions to Mars, the U.S. space agency has revealed.

We've been hearing a drumbeat recently of claims that a universal basic income--in effect, a monthly welfare check sent to everyone--is going to be necessary to save all the poor unfortunate souls put out of work by self-driving cars, artificial intelligence, robots, and other new forms of automation. We are told that the basic income will be "the only way to keep the country's economy afloat" in an age of automation, or that it will be necessary to absorb millions of truckers thrown out of middle-class jobs by the advent of autonomous vehicles. Of course, this being the field of high technology, there are always those who will say that it's not a bug but a feature. So we get Peter Diamandis reassuring us that "technological socialism" can "demonetize living." I have already thrown some skepticism at the idea that there is going to be a traumatic transition that will throw middle class people out on the streets without warning--rather than a long and gradual transition over decades, to which people can adapt.

Apple CEO Tim Cook waves to the crowd as takes the stage at Apple's annual Worldwide Developers Conference at the Bill Graham Civic Auditorium in San Francisco, California, onJune 13, 2016. SAN FRANCISCO – Tim Cook described his role at the helm of the world's most valuable technology company as "lonely." Reflecting on his five years in the role, Apple's CEO said the constant scrutiny of his every decision – by media, analysts, investors and consumers – has forced him to develop a thicker skin. His comments were part of a 10,000-word interview with The Washington Post Monday. "It's sort of a lonely job," he said.

The Pentagon wants to incorporate artificial intelligence into more systems but first needs to ensure its employees fully understand what drives AI, a new broad agency announcement suggests. Potential applications for defense are endless--autonomous aerial and undersea war-fighting or surveillance, among others--but humans won't make full use of AI until they trust it won't fail, according to the Defense Advanced Research Projects Agency. A new DARPA effort aims to nurture communication between machines and humans by investing in AI that can explain itself as it works. An intelligence analyst who receives recommendations from algorithms about what to investigate further "needs to understand why the algorithm has recommended certain activity," the BAA said. And the personnel overseeing a new autonomous system needs to know why it makes decisions so they "can decide how to use it in future missions."

The future of defense technology will be driven by artificial intelligence (AI), but robotic weapons, vehicles, and soldiers won't be much use if human service members don't trust or understand their automated counterparts. In order to build human confidence in machines, Pentagon researchers want to develop systems that explain exactly what they're doing. Last week, the United States Defense Advanced Research Projects Agency (DARPA) announced their Explainable AI (XAI) program, an initiative that will ensure people won't be confused by emerging battlefield tech. The agency will begin development in May 2017 and work on the project for four years. The human-robot trust gap is a growing concern for the Department of Defense.

Robert Gordon has written a magnificent book on the economic history of the United States over the last one and a half centuries. His study focuses on what he calls the "special century" from 1870 to 1970--in which living standards increased more rapidly than at any time before or after. The book is without peer in providing a statistical analysis of the uneven pace of growth and technological change, in describing the technologies that led to the remarkable progress during the special century, and in concluding with a provocative hypothesis that the future is unlikely to bring anything approaching the economic gains of the earlier period. The message of Rise and Fall is this. For most of human history, economic progress moved at a crawl. According to the economic historian Bradford DeLong, from the first rock tools used by humanoids three million years ago, to the earliest cities ten thousand years ago, through the Middle Ages, to the beginning of the Industrial Revolution around 1800, living standards doubled (with a growth of 0.00002 percent per year). Another doubling took place over the subsequent period to 1870. Then, according to standard calculations, the world economy took off. Gordon focuses on growth in the United States.