Predicting the future is hard and risky. But predicting the future in the computer industry is even harder and riskier due to dramatic changes in technology and limitless challenges to innovation. At the beginning of my term as 2014 president of IEEE Computer Society, with help from more than a dozen technology leaders, we set out to survey 23 potential technologies that could change the landscape of computer science and industry by the year 2022. With the IEEE CS 2022 Report, we created a comprehensive document that outlines future disruptive technologies, helps scientists and researchers understand the impact of technologies in the future, and provides the general public with an idea of how technology is evolving, along with its implications for society. At the foundation of the report is our understanding that by 2022, we will be well into a phase where intelligence becomes seamless and ubiquitous to those who can afford and use state-of-the-art information technology.

AWS DeepLens Looking for a new way to learn machine learning? Let a machine teach you with AWS DeepLens, the world's first deep learning enabled video camera for developers. Designed to connect securely to a variety of AWS offerings, including AWS IoT, Amazon SQS, Amazon SNS, and Amazon DynamoDB, AWS DeepLens uses Amazon Kinesis Video Streams to stream video back to AWS and Amazon Rekognition Video to apply advanced video analytics. Easy to customize and fully programmable with AWS Lambda, AWS DeepLens runs on any deep learning framework, including TensorFlow and Caffe.

A century ago, most Americans lived much like people in ancient times. As Robert Gordon explains in The Rise and Fall of American Growth, few had indoor plumbing, electricity or an automobile. Everyday chores, such as cooking a meal or washing yourself could take hours of backbreaking labor just to haul water and wood. After 1920 things began to change. Productivity surged as never before or since as secondary shifts, such as electrical appliances, developing infrastructure and a retail transformation helped pave the way for social changes, like women in the workplace and suburban life.

This paper proposes a new approach to construct high quality space-filling sample designs. First, we propose a novel technique to quantify the space-filling property and optimally trade-off uniformity and randomness in sample designs in arbitrary dimensions. Second, we connect the proposed metric (defined in the spatial domain) to the objective measure of the design performance (defined in the spectral domain). This connection serves as an analytic framework for evaluating the qualitative properties of space-filling designs in general. Using the theoretical insights provided by this spatial-spectral analysis, we derive the notion of optimal space-filling designs, which we refer to as space-filling spectral designs. Third, we propose an efficient estimator to evaluate the space-filling properties of sample designs in arbitrary dimensions and use it to develop an optimization framework to generate high quality space-filling designs. Finally, we carry out a detailed performance comparison on two different applications in 2 to 6 dimensions: a) image reconstruction and b) surrogate modeling on several benchmark optimization functions and an inertial confinement fusion (ICF) simulation code. We demonstrate that the propose spectral designs significantly outperform existing approaches especially in high dimensions.

Before scientists can effectively capture and deploy fusion energy, they must learn to predict major disruptions that can halt fusion reactions and damage the walls of doughnut-shaped fusion devices called tokamaks. Today, researchers at the U.S. Department of Energy's Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability....

Like it or not, artificial intelligence is changing how companies find, hire and manage talent.

For the first time, another solar system has been found in our galaxy with eight planets, just like our own – and it was Google's artificial intelligence that found it. Using machine learning and neural networks to achieve something humans could not, weak signal data from NASA's Kepler space telescope was scrutinized using Google machine learning technology to make the discovery. The discovery of an eighth planet circling the distant star Kepler-90 system – which lies 2,545 light years away in the constellation of Draco in the northern sky – marks the first time that another solar system has been found with the same number of planets as our own. Kepler-90i is a hot, rocky planet orbiting its star once every 14.4 days, and it was found by University of Texas at Austin astronomer Andrew Vanderburg and Christopher Shallue, a senior software engineer at Google AI in Mountain View, California. They did it by using a computer that'learned' to find planets in data from NASA's Kepler space telescope.

Today, researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability.

Big data, the Internet of Things, and artificial intelligence hold such disruptive power that they have inverted the dynamics of technology leadership. When science and technology meet social and economic systems, you tend to see something akin to what the late Stephen Jay Gould called "punctuated equilibrium" in his description of evolutionary biology. Something that has been stable for a long period is suddenly disrupted radically--and then settles into a new equilibrium.1 1.See Stephen Jay Gould, Punctuated Equilibrium, Cambridge, MA: Harvard University Press, 2007. Gould pointed out that fossil records show that species change does not advance gradually but often massively and disruptively. After the mass extinctions that have occurred several times across evolutionary eras, a minority of species survived and the voids in the ecosystem rapidly filled with massive speciation.

Earlier this year, the FAA and U.S. Interior Department separately barred drone flights within the boundaries of sites including the USS Constitution in Boston, the Gateway Arch in St. Louis and Independence National Historical Park in Philadelphia. The list also includes Glen Canyon Dam in Arizona, Hoover Dam in Nevada and Grand Coulee Dam in Washington state.