Imagine the endless possibilities of learning from 2.5 quintillion bytes of data generated every day. Artificial intelligence (AI), which began its journey 60 years ago is well on its course to make this implausible scenario a reality. Artificial Intelligence, is slowly taking over our lives. From personal assistants like Siri in Apple products to stock trading to medical diagnosis, AI is able to learn from seemingly unstructured data, take decisions and perform actions in a way previously unimagined. Businesses too are undergoing digitization rapidly.

Hiroshige Seko, the new head of the Minister of Economy, Trade And Industry, says the government needs to implement policies that will help the Japanese automotive industry keep its lead in the global arena by developing advanced autonomous driving technologies ahead of overseas competitors. "The internet of things, artificial intelligence and self-driving technologies are very important for Japan," Seko said in a recent interview. Asked if the government can achieve its targets for nuclear power and renewable energy included in the country's best energy mix for fiscal 2030, which was adopted by METI in July 2015, Seko offered assurances that nuclear plant safety measures tightened under the new standards introduced in July 2013 are sufficient, and that the Nuclear Regulation Authority conducts rigorous safety screenings regardless of government policy. It is important for the government to fully explain that detailed disaster management and evacuation plans have been laid out, said Seko, who took up his ministerial post in the Aug. 3 Cabinet reshuffle by Prime Minister Shinzo Abe. The energy mix sets Japan's reliance on nuclear power in the year to March 2031 at 20 to 22 percent and that on solar, wind and other renewable energy at 22 to 24 percent. The nuclear safety standards were drawn up based on lessons from the March 2011 crisis at the Fukushima No. 1 nuclear plant.

Woodside is Australia's largest independent oil and gas company. For more than 60 years it's been a critical thread in the nation's fabric: if its gas doesn't arrive promptly in Perth to fuel power generation the city goes dark in two days. The company no longer relies purely on gas and oil for its success, however; it relies on data. According to Woodside senior vice-president and chief technology officer Shaun Gregory: "Our people understand that data is king, that knowledge is king." However; it used to be a burden, he says.

These are lecture notes that are based on the lectures from a class I taught on the topic of Spectral Graph Methods at UC Berkeley during the Spring 2015 semester.

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".

The sun was sparkling on the Mediterranean Sea on the afternoon when a graduate student from my lab tossed our prize robot into the water for the first time. I watched nervously as our electronic creation sank beneath the waves. But the bot didn't falter: When we gave it the command to swim, it filled its expandable mantle with water, then jetted out the fluid to shoot forward. When we ordered it to crawl, it stiffened its eight floppy arms in sequence to push itself along the sandy bottom and over scattered rocks. And when we instructed it to explore a tight space beneath the dock, the robot inserted its soft body into the narrow gap without difficulty.

This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF). VolMin is a factorization criterion that decomposes a given data matrix into a basis matrix times a structured coefficient matrix via finding the minimum-volume simplex that encloses all the columns of the data matrix. Recent work showed that VolMin guarantees the identifiability of the factor matrices under mild conditions that are realistic in a wide variety of applications. This paper focuses on both theoretical and practical aspects of VolMin. On the theory side, exact equivalence of two independently developed sufficient conditions for VolMin identifiability is proven here, thereby providing a more comprehensive understanding of this aspect of VolMin. On the algorithm side, computational complexity and sensitivity to outliers are two key challenges associated with real-world applications of VolMin. These are addressed here via a new VolMin algorithm that handles volume regularization in a computationally simple way, and automatically detects and {iteratively downweights} outliers, simultaneously. Simulations and real-data experiments using a remotely sensed hyperspectral image and the Reuters document corpus are employed to showcase the effectiveness of the proposed algorithm.

"The information all becomes shareable and then the decision will be made by these kind of guardian angels for each of the firefighters," said Edward Chow, manager of the Jet Propulsion Laboratory's Civil Program Office and AUDREY program manager. The AI automatically warns a police officer inside to evacuate, while also telling incoming firefighters or hazardous-material teams to address the threat quickly. Those firefighters, police officers and EMTs of the future will carry body-worn sensors, cameras and augmented glasses with heads-up displays. "The proliferation of miniaturized sensors and internet of things devices can make a tremendous impact on first responder safety, connectivity and situational awareness," said John Merrill, Next Generation First Responder program manager for the DHS' Science and Technology Directorate.

Sessions include: 1) Setting the Scene: The Machine Intelligence Landscape in Renewables; 2) Predicting Energy Consumption Using Machine Learning; 3) Deep Learning for Multiple Weather Prediction Models; and 4) Discussion: What Are The Next Steps?

Tomorrow's graduates will be applying for jobs working in virtual worlds and outer space, experts claim, following the release of a new report predicting career trends for the next ten years. Research conducted by a group of leading technologists, academics and industry analysts suggests that a host of new job options will become available to those graduating in less than a decade's time, including "ethical technology advocates", "sustainable power innovators" and "virtual habitat designers". Contrary to common fears that robots will render human employment worthless, the expansion of new technology will provide countless more as yet unheard of career paths, experts believe, using virtual reality environments and better connected remote office environments. Ryan Asdourian, Microsoft's Windows and Surface Lead said of the report: "While these jobs may seem like the realms of science fiction, in reality they are indicative of changes that we are already seeing today." "The job market is changing at a more rapid pace than ever before, partly because of artificial intelligence."