If you hadn't heard, Elon Musk is worried about the machines. Though that may seem a quixotic stance for the head of multiple tech companies to take, it seems that his proximity to the bleeding edge of technological development has given him the heebie-jeebies when it comes to artificial intelligence. He's shared his fears of AI running amok before, likening it to "summoning the demon," and Musk doubled down on his stance at a meeting of the National Governors Association this weekend, telling state leaders that AI poses an existential threat to humanity. "Until people see robots going down the street killing people, they don't know how to react because it seems so ethereal. AI is a rare case where I think we need to be proactive in regulation instead of reactive. Because I think by the time we are reactive in AI regulation, it's too late," according to the MIT Tech Review.

Neural networks have become increasingly prevalent within the geosciences for applications ranging from numerical model parameterizations to the prediction of extreme weather. A common limitation of neural networks has been the lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have typically been used within the geosciences to accurately identify a desired output given a set of inputs, with the interpretation of what the network learns being used - if used at all - as a secondary metric to ensure the network is making the right decision for the right reason. Network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of a neural network can enable the discovery of scientifically meaningful connections within geoscientific data. By training neural networks to use one or more components of the earth system to identify another, interpretation methods can be used to gain scientific insights into how and why the two components are related. In particular, we use two methods for neural network interpretation. These methods project the decision pathways of a network back onto the original input dimensions, and are called "optimal input" and layerwise relevance propagation (LRP). We then show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network-related geoscience research.

NEW DELHI – Japan and India agreed Saturday to conduct their first joint fighter aircraft exercise in Japan as part of efforts to promote bilateral security cooperation in the face of China's military buildup and regional assertiveness. In inaugural "two-plus-two" security talks, the nations' foreign and defense ministers also welcomed the significant progress in negotiations for a pact that would allow the sharing of defense capabilities and supplies including fuel and ammunition. They called for a speedy conclusion to the acquisition and cross-servicing agreement (ACSA), according to a joint statement issued after the talks in New Delhi. The two governments are planning to sign the deal when Prime Minister Shinzo Abe visits India for talks with Prime Minister Narendra Modi in mid-December, according to Japanese officials. Tokyo and New Delhi aim to have a joint exercise involving fighter jets from the Air Self-Defense Force and the Indian Air Force next year, the officials said.

New Yorkers got their first glimpse of FedEx's delivery robot last week, when a prototype named'Roxo' was given a day out in Manhattan. But far from being welcomed by residents, the six-wheeled droid was promptly presented with a cease-and-desist order by the city. A long-standing ambition for many tech firms, delivery robots is finally getting close to becoming a reality. FedEx has trialled the bots in several U.S. cities including Memphis, Manchester and New Hampshire, before bringing one to New York City. Although the robot's trip out in New York was just a marketing stunt, rather than an actual trial, it has already attracted feedback from some residents - namely mayor Bill de Blasio.

Many parts of the transportation sector require advanced technology, like Machine Learning, to make important decisions. The transit agencies' foremost responsibility is to ensure safe and in-time transportation of the passengers. Machine Learning in transport can help departments to mine the data and find solutions to its various problems. Applications supporting public transport, travel, and parking have widespread use. They offer the possibility to develop smarter and more user-friendly services, which will promote more sustainable transport use.

The next few years are going to be lively ones for the datacenter, with more than half of new infrastructure being deployed in edge locations, half of core enterprise datacenters and two-thirds of the major edge IT sites leveraging artificial intelligence (AI) and machine learning (ML), more than half of datacenter infrastructure running "as-a-service" solutions, and a steadily growing number of companies relying on colocation partners. Those were a few of the predictions offered by the industry watchers at IDC last week with the release the analyst firm's first annual "Futurescape" forecast focused on the datacenter. Emphasizing trends emerging in 2020, the report was presented in part during a webcast led by some of its authors. "At the core of all of our predictions is the reality that technology is very rapidly moving from the back office to the front office," said Jennifer Cooke, research director of IDC's Cloud to Edge Datacenter Trends and Strategies research team. "And a lot of this is about the boundaries between an organization's internal operations and external ecosystem of customers, partners and markets. These boundaries are just disappearing."

At the Common Good in the Digital Age tech conference recently held in Vatican City, Pope Francis urged Facebook executives, venture capitalists, and government regulators to be wary of the impact of AI and other technologies. "If mankind's so-called technological progress were to become an enemy of the common good, this would lead to an unfortunate regression to a form of barbarism dictated by the law of the strongest," he said. In a related but contextually different conversation, this summer Joy Buolamwini testified before Congress with Rep. Alexandria Ocasio-Cortez (D-NY) that multiple audits found facial recognition technology generally works best on white men and worst on women of color. What these two events have in common is their relationship to power dynamics in the AI ethics debate. Arguments about AI ethics can wage without mention of the word "power," but it's often there just under the surface. In fact, it's rarely the direct focus, but it needs to be. Power in AI is like gravity, an invisible force that influences every consideration of ethics in artificial intelligence. Power provides the means to influence which use cases are relevant; which problems are priorities; and who the tools, products, and services are made to serve. It underlies debates about how corporations and countries create policy governing use of the technology.

What does a "journey" mean to you? At IBM, our long standing tradition of journey exploration has led humans to the moon and coined the term machine learning 50 years ago. Now we are helping organizations scale the ladder to AI to reap rewards in growth, productivity and efficiency with IBM Watson. This journey to AI mirrors the history of travel. In this article, I'll explain how IBM Cloud Pak for Data accelerates the journey to AI and delve into the ways AutoAI helps boost the speed of business returns.

Differential privacy is a cryptographically-motivated approach to privacy that has become a very active field of research over the last decade in theoretical computer science and machine learning. In this paradigm one assumes there is a trusted curator who holds the data of individuals in a database and the goal of privacy is to simultaneously protect individual data while allowing the release of global characteristics of the database. In this setting we introduce a general framework for parametric inference with differential privacy guarantees. We first obtain differentially private estimators based on bounded influence M-estimators by leveraging their gross-error sensitivity in the calibration of a noise term added to them in order to ensure privacy. We then show how a similar construction can also be applied to construct differentially private test statistics analogous to the Wald, score and likelihood ratio tests. We provide statistical guarantees for all our proposals via an asymptotic analysis. An interesting consequence of our results is to further clarify the connection between differential privacy and robust statistics. In particular, we demonstrate that differential privacy is a weaker stability requirement than infinitesimal robustness, and show that robust M-estimators can be easily randomized in order to guarantee both differential privacy and robustness towards the presence of contaminated data. We illustrate our results both on simulated and real data.

Ever since the industrial chemist Leo Baekeland began synthesizing phenol and formaldehyde in 1907, the world has developed a love-hate relationship with the resulting polymer: plastic. While plastic is convenient, durable, and cheap, 50% of all plastics (about 150 million tons every year, worldwide) are used only once and then thrown away. Even for those who dutifully recycle our plastic water bottles and sandwich bags, we're only tackling a small part of the problem. "Considering the size of the problem, there's relatively limited infrastructure in place to capture and treat stormwater," says Tony Hale, program director for environmental informatics at the nonprofit San Francisco Estuary Institute (SFEI). That's where SFEI is looking to use research and data--and most recently, drones--to make a difference.