We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their relative presence in each microphone. To this end, we formulate a neural network architecture that can be interpreted as a nonnegative tensor factorization of a multi-channel audio recording. By clustering on the learned network parameters corresponding to channel content, we can learn sources' individual spectral dictionaries and their activation patterns over time. Our method allows us to leverage deep learning advances like end-to-end training, while also allowing stochastic minibatch training so that we can feasibly decompose realistic audio scenes that are intractable to decompose using standard methods. This neural network architecture is easily extensible to other kinds of tensor factorizations.

NASA's InSight lander has captured stunning images of sunrise and sunset from the Martian surface. Stunning images taken from the Insight's robotic arm shows the Red Planet landscape and the sun rising and setting at the equivalent of 5:30 am and 6:30 pm Mars time. The sun looks especially small because it's further away from Mars than the Earth so it is about two-thirds the size as seen on our planet. The Insight lander has been busy recently; just a handful of months into its exploratory mission on Mars it made a groundbreaking discovery after it detected for the first time a quake on Mars, or a'Marsquake'. NASA's stationary InSight lander has captured what sunrise and sunset looks like on Mars.

President Trump began his trade war with China out of concern that Beijing was using unfair economic practices to prevent the United States from dominating next-generation technologies like autonomous vehicles, advanced telecommunications and artificial intelligence. But as the two countries move closer to a trade deal, it seems increasingly unlikely that China will give ground in a crucial area that could determine which country wins the technology race. Despite months of pressure from the White House, Chinese negotiators have so far refused to relax tight regulations that block multinational companies from moving data they gather on their Chinese customers' purchases, habits and whereabouts out of the country. Such data is crucial as industries build next-generation technologies. With another round of trade talks underway this week, the United States and China appear headed toward an agreement that could end the monthslong trade war and lift tariffs on hundreds of billions of dollars of products.

Lurking behind the Trump administration's trade conflict with China lies an abiding fear that the United States could be losing its advantage in the global technology race. In US policymaking circles more broadly, China's "Made in China 2025" policy – intended to ensure Chinese dominance in cyber capabilities, artificial intelligence (AI), aeronautics, and other frontier sectors – is viewed not just as an economic challenge, but as a geopolitical threat. Everything from US telecommunications infrastructure and intellectual property to America's military position in East Asia are considered to be at risk. The fact that technology is driving geopolitical tensions runs against the predictions of many scholars and policymakers. As recently as the mid-2000s, some suspected that geography would no longer play a meaningful role in the functioning of global markets.

Digital innovations and advances in AI have produced a range of novel talent identification and assessment tools. Many of these technologies promise to help organizations improve their ability to find the right person for the right job, and screen out the wrong people for the wrong jobs, faster and cheaper than ever before. These tools put unprecedented power in the hands of organizations to pursue data-based human capital decisions. They also have the potential to democratize feedback, giving millions of job candidates data-driven insights on their strengths, development needs, and potential career and organizational fit. In particular, we have seen the rapid growth (and corresponding venture capital investment) in game-based assessments, bots for scraping social media postings, linguistic analysis of candidates' writing samples, and video-based interviews that utilize algorithms to analyze speech content, tone of voice, emotional states, nonverbal behaviors, and temperamental clues.

In October, Amazon had to discontinue an artificial intelligence–powered recruiting tool after it discovered the system was biased against female applicants. In 2016, a ProPublica investigation revealed a recidivism assessment tool that used machine learning was biased against black defendants. More recently, the US Department of Housing and Urban Development sued Facebook because its ad-serving algorithms enabled advertisers to discriminate based on characteristics like gender and race. And Google refrained from renewing its AI contract with the Department of Defense after employees raised ethical concerns. Those are just a few of the many ethical controversies surrounding artificial intelligence algorithms in the past few years.

By the third century B.C., engineers in Hellenistic Alexandria, in Egypt, were building real mechanical robots and machines. And such science fictions and historical technologies were not unique to Greco-Roman culture. In my recent book "Gods and Robots," I explain that many ancient societies imagined and constructed automatons. Chinese chronicles tell of emperors fooled by realistic androids and describe artificial servants crafted in the second century by the female inventor Huang Yueying. Techno-marvels, such as flying war chariots and animated beings, also appear in Hindu epics. One of the most intriguing stories from India tells how robots once guarded Buddha's relics.

Underwater robots could get their batteries recharged by munching the sea floor. A device created by the US Navy extracts electrical energy from layers of fish faeces and other organic matter to provide an endless source of power. All underwater devices have a fundamental limitation – battery life. They are incredibly useful for exploring and monitoring the depths, but once their power reserves start to run low there's no choice but to bring them to the surface or abandon them.

Convolutional neural networks (CNNs) are known for their good performance and generalization in vision-related tasks and have become state-of-the-art in both application and research-based domains. However, just like other neural network models, they suffer from a susceptibility to noise and adversarial attacks. An adversarial defence aims at reducing a neural network's susceptibility to adversarial attacks through learning or architectural modifications. We propose a weight map layer (WM) as a generic architectural addition to CNNs and show that it can increase their robustness to noise and adversarial attacks. We further explain the enhanced robustness of the two WM variants introduced via an adaptive noise-variance amplification (ANVA) hypothesis and provide evidence and insights in support of it. We show that the WM layer can be integrated into scaled up models to increase their noise and adversarial attack robustness, while achieving the same or similar accuracy levels.

We develop a unified model, known as MgNet, that simultaneously recovers some convolutional neural networks (CNN) for image classification and multigrid (MG) methods for solving discretized partial differential equations (PDEs). This model is based on close connections that we have observed and uncovered between the CNN and MG methodologies. For example, pooling operation and feature extraction in CNN correspond directly to restriction operation and iterative smoothers in MG, respectively. As the solution space is often the dual of the data space in PDEs, the analogous concept of feature space and data space (which are dual to each other) is introduced in CNN. With such connections and new concept in the unified model, the function of various convolution operations and pooling used in CNN can be better understood. As a result, modified CNN models (with fewer weights and hyper parameters) are developed that exhibit competitive and sometimes better performance in comparison with existing CNN models when applied to both CIFAR-10 and CIFAR-100 data sets.