For tomorrow's workers, AI will be more than a tool; AIs will be their co-workers and a ubiquitous part of their lives. If the next generation is to use AI and big data effectively – if they're to understand their inherent limitations, and build even better platforms and intelligent systems -- we need to prepare them now. That will mean some adjustments in elementary education and some major, long-overdue upgrades in computer science instruction at the secondary level. The U.S. is woefully behind many of our peer nations, and President Obama's Computer Science for All initiative may flounder amid budget cuts proposed by the Trump administration. Another major hurdle is that our schools face a severe shortage of teachers who are trained in computer science.

Prisons across the United States are reportedly building biometric databases that include voice recordings of incarcerated people, according to The Intercept. The report cites contracting documents for the state of New York's prison system, as well as statements from officials in Texas, Florida, Arkansas and Arizona confirming that prisons are actively using voice recognition technology that can extract and digitize voices to create unique and identifiable biometric signatures known as voice prints. According to the prisons, the systems are being used to improve security in the facilities and to combat fraud. Prisons can use the biometric identifier to track phone calls and find past communications that match the voice print of a particular person. The systems can also flag calls deemed to be suspicious to allow investigators to review the communications.

At the risk of dating myself, one of my favorite movies growing up as a kid was "WarGames" starring Matthew Broderick. I didn't realize it at the time, but in the climactic scene, the large supercomputer'WOPR' operated by the Defense Department, showed artificial intelligence capabilities. By playing tic-tac-toe against itself, it learned a lesson that prevented global thermonuclear war. In many ways, Hollywood has warped what many think of when they first hear the term artificial intelligence, or AI. My thoughts used to go to movies like "The Terminator" or "The Matrix" where sentient machines develop the ability to think for themselves and try to overthrow humankind.

The automation of measurement and data collection procedures, coupled with the development of vast capacity for data storage and the creation of highly sophisticated tools for analyzing and processing data, often in real time, is radically changing the world in which we live. This has prompted considerable debate, both philosophical and legal, about the right, legitimate, and proper ways to use such data. Also, since there is no absolute authority to whom we can appeal for guidance, it is important that we, the data creators, suppliers, and users, should engage with these ethical considerations. Emphasis naturally tends to be on the side of risk and protection, but we must always bear in mind the need for a proper balance between risk and benefit. Zero risk can be attained only at the cost of zero benefit, but the potential benefit from new data technologies is vast. Or, as leading data ethicists Floridi and Taddeo1 put it: "On the one hand, overlooking ethical issues may prompt ...

Central government of India is planning to launch a National Centre for Artificial Intelligence (AI) and make it operational by July as the work has begun for a launch, said a report by Business Standard. Called as the National Artificial Intelligence Centre, the unit is likely to cost around Rs 400-450 crore and will be part of the Ministry of Electronics and Information Technology (MeitY), and will work in collaboration with other entities of the department such as the National Informatics Centre (NIC) and the Centre for Development of Advanced Computing (C-DAC), said the report citing senior government officials. The report said the AI unit will be for citizens and will help in setting up data platform, skilling, reskilling and research platforms, thereby helping to solve legal, regulatory and cybersecurity challenges. "The panels gave us a lot of fodder to think on, and suggested setting up of a national AI centre," a senior government official told Business Standard. "The centre will try to innovate in AI for government applications. It will look at how AI can be used in health care, education, and agriculture from a public systems delivery perspective."

A gigantic cavity two-thirds the area of Manhattan and almost 1,000 feet (300 meters) tall has been found growing at the bottom of Thwaites Glacier in West Antarctica. About the size of Florida, Thwaites Glacier is currently responsible for approximately 4 percent of global sea level rise. It holds enough ice to raise the world ocean a little over 2 feet (65 centimeters) and backstops neighboring glaciers that would raise sea levels an additional 8 feet (2.4 meters) if all the ice were lost. About the size of Florida, Thwaites Glacier is currently responsible for approximately 4 percent of global sea level rise. A gigantic cavity two-thirds the area of Manhattan and almost 1,000 feet (300 meters) tall has been found growing at the bottom of it.

Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we develop and apply deep convolutional neural network (CNN) models to learn the spatiotemporal patterns of mismatch between TWS anomalies (TWSA) derived from GRACE and those simulated by NOAH, a widely used land surface model. Once trained, our CNN models can be used to correct the NOAH simulated TWSA without requiring GRACE data, potentially filling the data gap between GRACE and its follow-on mission, GRACE-FO. Our methodology is demonstrated over India, which has experienced significant groundwater depletion in recent decades that is nevertheless not being captured by the NOAH model. Results show that the CNN models significantly improve the match with GRACE TWSA, achieving a country-average correlation coefficient of 0.94 and Nash-Sutcliff efficient of 0.87, or 14\% and 52\% improvement respectively over the original NOAH TWSA. At the local scale, the learned mismatch pattern correlates well with the observed in situ groundwater storage anomaly data for most parts of India, suggesting that deep learning models effectively compensate for the missing groundwater component in NOAH for this study region.

We report the first, to the best of our knowledge, hand-in-hand collaboration between human rights activists and machine learners, leveraging crowd-sourcing to study online abuse against women on Twitter. On a technical front, we carefully curate an unbiased yet low-variance dataset of labeled tweets, analyze it to account for the variability of abuse perception, and establish baselines, preparing it for release to community research efforts. On a social impact front, this study provides the technical backbone for a media campaign aimed at raising public and deciders' awareness and elevating the standards expected from social media companies.

Adversarial attacks add perturbations to the input features with the intent of changing the classification produced by a machine learning system. Small perturbations can yield adversarial examples which are misclassified despite being virtually indistinguishable from the unperturbed input. Classifiers trained with standard neural network techniques are highly susceptible to adversarial examples, allowing an adversary to create misclassifications of their choice. We introduce a new type of network unit, called MWD (max of weighed distance) units that have a built-in resistant to adversarial attacks. These units are highly non-linear, and we develop the techniques needed to effectively train them. We show that simple interval techniques for propagating perturbation effects through the network enables the efficient computation of robustness (i.e., accuracy guarantees) for MWD networks under any perturbations, including adversarial attacks. MWD networks are significantly more robust to input perturbations than ReLU networks. On permutation invariant MNIST, when test examples can be perturbed by 20% of the input range, MWD networks provably retain accuracy above 83%, while the accuracy of ReLU networks drops below 5%. The provable accuracy of MWD networks is superior even to the observed accuracy of ReLU networks trained with the help of adversarial examples. In the absence of adversarial attacks, MWD networks match the performance of sigmoid networks, and have accuracy only slightly below that of ReLU networks.

As home to three top-ranked supercomputers of the last decade, the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) has become synonymous with scientific computing at the largest scales. Getting the most out of these science machines, however, requires a willingness to experiment with problems and systems of every size and scale. This is especially important as technology vendors introduce new system architectures and as scientists' problem-solving toolkit expands to include artificial intelligence (AI) and advanced data analysis. In that spirit, ORNL recently installed two NVIDIA DGX-2 systems, powerful GPU-accelerated appliances that will provide ORNL researchers with enhanced opportunities to conduct science--machine learning and data-intensive workloads in particular. The appliances will also provide an onramp to ORNL's Summit--the world's most powerful supercomputer--by enabling smaller and more experimental projects to be developed and tested before running on the 200-petaflop machine.