Thanks to the availability of large scale digital datasets and massive amounts of computational power, deep learning algorithms can learn representations of data by exploiting multiple levels of abstraction. These machine learning methods have greatly improved the state-of-the-art in many challenging cognitive tasks, such as visual object recognition, speech processing, natural language understanding and automatic translation. In particular, one class of deep learning models, known as deep belief networks, can discover intricate statistical structure in large data sets in a completely unsupervised fashion, by learning a generative model of the data using Hebbian-like learning mechanisms. Although these self-organizing systems can be conveniently formalized within the framework of statistical mechanics, their internal functioning remains opaque, because their emergent dynamics cannot be solved analytically. In this article we propose to study deep belief networks using techniques commonly employed in the study of complex networks, in order to gain some insights into the structural and functional properties of the computational graph resulting from the learning process.

Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, its embedding in complex sociotechnical processes. Recent advances in machine learning (ML) enabled by deep neural networks has exacerbated the challenge of evaluating such software due to the opaque nature of these ML-based artifacts. A key related issue is the (in)ability of such systems to generate useful explanations of their outputs, and we argue that the explanation and evaluation problems are closely linked. The paper models the elements of a ML-based AI system in the context of public sector decision (PSD) applications involving both artificial and human intelligence, and maps these elements against issues in both evaluation and explanation, showing how the two are related. We consider a number of common PSD application patterns in the light of our model, and identify a set of key issues connected to explanation and evaluation in each case. Finally, we propose multiple strategies to promote wider adoption of AI/ML technologies in PSD, where each is distinguished by a focus on different elements of our model, allowing PSD policy makers to adopt an approach that best fits their context and concerns.

Progressively intricate cyber infiltration mechanisms have made conventional means of defense, such as firewalls and malware detectors, incompetent. These sophisticated infiltration mechanisms can study the defender's behavior, identify security caveats, and modify their actions adaptively. To tackle these security challenges, cyber-infrastructures require active defense techniques that incorporate cyber deception, in which the defender (deceiver) implements a strategy to mislead the infiltrator. To this end, we use a two-player partially observable stochastic game (POSG) framework, wherein the deceiver has full observability over the states of the POSG, and the infiltrator has partial observability. Then, the deception problem is to compute a strategy for the deceiver that minimizes the expected cost of deception against all strategies of the infiltrator. We first show that the underlying problem is a robust mixed-integer linear program, which is intractable to solve in general. Towards a scalable approach, we compute optimal finite-memory strategies for the infiltrator by a reduction to a series of synthesis problems for parametric Markov decision processes. We use these infiltration strategies to find robust strategies for the deceiver using mixed-integer linear programming. We illustrate the performance of our technique on a POSG model for network security. Our experiments demonstrate that the proposed approach handles scenarios considerably larger than those of the state-of-the-art methods.

The current resurgent interest in Artificial Intelligence (AI) has been driven by the availability of data (particularly labelled data), the democratisation of computing infrastructure and tooling, and the ability to combine these elements to create AI algorithms. Benefit is achieved once an algorithm is deployed into an operational system to achieve an operational advantage. The ability to exploit the opportunities offered by AI within UK Defence calls for an understanding of systemic issues required to achieve an effective operational capability. This paper provides the authors' views of issues which currently block UK Defence from fully benefitting from AI technology. These are situated within a reference model for the AI Value Train, so enabling the community to address the exploitation of such data and software intensive systems in a systematic, end to end manner. The paper sets out the conditions for success including: - Researching future solutions to known problems and clearly defined use cases; - Addressing achievable use cases to show benefit; - Enhancing the availability of Defence-relevant data; - Enhancing Defence'know how' in AI; - Operating Software Intensive supply chain ecosystems at required breadth and pace; - Governance and, the integration of software and platform supply chains and operating models.

Google CEO Sundar Pichai delivers the keynote address at the Google I/O 2018 Conference at Shoreline Amphitheater on May 8, 2018, in Mountain View, Calif. Google's two day developer conference runs through May 9, 2018. SAN FRANCISCO -- Rebuked by lawmakers and slammed by President Trump, Google is on the political hot seat -- and its CEO is headed to Capitol Hill to make peace. Earlier this month, Sundar Pichai didn't show up to a congressional hearing on state-sponsored election interference; top execs from Facebook and Twitter did. In a public scolding, the Senate Intelligence Committee left an open chair to spotlight Pichai's absence.

Stunning new maps covering over 2,000 square kilometers of northern Guatemala have revealed the site of an ancient Maya mega-city hidden in the dense tropical forest. Researchers uncovered more than 61,000 ancient structures at the site using LiDAR technology, which relies on laser pulses to map out the topography. Evidence from the exhaustive survey supports earlier suspicions that upwards of 11 million people lived in the Maya Lowlands from the year 650 to 800 CE. Stunning new maps covering over 2,000 square kilometers of northern Guatemala have revealed the site of an ancient Maya megacity hidden in the dense tropical forest. The researchers have now published the results of what they say is the largest LiDAR survey to date, months after first revealing their remarkable discovery.

A consortium of universities, research laboratories and industry partners will take a $20 million grant from the National Science Foundation to modernize the state's century-old electrical grid. Announced by lawmakers on Friday, the grant will fund a SMART Grid Center at the University of New Mexico -- it's not a physical building, but a "novel, interdisciplinary research center that will address pressing design, operational, data, and security challenges of next-generation electric power management," said William Michener, principal investigator for the award. Michener is also the state director of New Mexico's Established Program to Stimulate Competitive Research, or EPSCoR, program, which is directed at jurisdictions traditionally underfunded in research grants. The SMART Grid Center -- which stands for Sustainable, Modular, Adaptive, Resilient and Transactive -- has four main research objectives: improving the resilience and cybersecurity of the grid, utilizing machine-learning algorithms to optimize power production and building in simulations and testbed systems to validate performance and sustainability. The fourth and most comprehensive objective will be to adapt the existing electrical infrastructure to accept wind, solar and other new forms of energy, without a noticeable decrease in supply.

Toronto's tech boom – driven in part by artificial intelligence research at the University of Toronto – has prompted talk of a "Silicon Valley North." But those actually ensconced in the Bay Area instead paint a picture of a research-driven innovation hub that's collaborative, inclusive and uniquely Canadian. At this year's three-day Elevate technology "festival" in Toronto, Eric Schmidt, a Google board member and former CEO, lauded the way Canada's post-secondary sector is being used to power the country's innovation engine and said Canada should be home to "one or two" of the globally important companies that spring from the coming AI revolution. "You have strong universities and the government is actually small enough, and sane enough, to help universities," Schmidt told a packed auditorium at the Sony Centre for the Performing Arts minutes before former U.S. vice-president and climate change crusader Al Gore took the stage. He added that Canada also benefits from close ties between post-secondary institutions and industry players.

New York University's AI Now Institute, a research hub investigating the wider social impacts of machine learning algorithms, has published a report critiquing how the US government uses the technology. The report, emitted this week, is based around a series of case studies discussed during a workshop held in June earlier this year. Research into the ethics of algorithms is flourishing, and most people are now aware of the common pitfalls of machine learning that are particularly troubling. AI systems have been described as black boxes. It's impossible to see what's going on and understand how machines make decisions since there are so many hidden variables.

A so-called "robot brothel" being proposed for Houston is getting pushback from local officials and community groups, with the mayor saying the city is reviewing its ordinances to determine if they address public safety and health concerns potentially associated with the business.