Rob Roy, public sector chief technology officer at Micro Focus Government Solutions, has said machine learning and cloud platforms could help government agencies protect their networks from cybersecurity breaches and achieve efficiency. Roy wrote how unsupervised machine learning could assist agencies in detecting anomalous user behavior. "Unlike the rules-based approach, unsupervised machine learning lets the technology develop an understanding of how the network's users typically behave and alert administrators when something abnormal occurs, increasing the likelihood that a rogue event is detected and a response is orchestrated at machine speed," he added. He said data scientists could help agencies sort through raw data and determine relevant information to analyze in order to address a specific problem. He also mentioned the potential benefits of migrating common business-oriented language-based mainframe applications and other legacy systems running mission-critical functions to the cloud.

In January of 2019 Smart Dubai launched the city's official principles and guidelines for the ethical implementation of AI. What truly makes Dubai's approach to AI unique is our city-government launched AI Ethics Self-Assessment Toolkit – which allows anyone implementing AI to self-assess their performance against a set of criteria which when taken together assure an ethical approach. The process uses the data from the toolkit to create a positive feedback loop with those using and developing AI. Express Computer spoke to H.E. Younus Al Nasser, Assistant Director General, Smart Dubai and CEO, Smart Dubai Data. What potential do you see in AI for governance and happiness?

The Federal Reserve Bank of New York is turning to machine learning to cut down on the back and forth between the regulator and banks and predict potential misreporting. Sri Malladi, senior director at the New York Fed's data and statistics group, said during the Waters USA conference in Manhattan that the regulator's long-term goal with machine learning is to be able to predict potential issues with banks' reporting. "We want to be at the point where we know that distribution expert reports

Today, we're excited to formally launch the final version of OPSI's AI primer: Hello, World: Artificial Intelligence and its Use in the Public Sector. "Hello, World!" is often the very first computer program written by someone learning how to code, and we want this primer to be able to help pubic officials take their first steps in exploring AI. The primer is the result of 10 months of research and analysis focused specifically on the use and implications of AI in government. It has benefited from the input of dozens of experts and stakeholders, including through a seven-week public consultation, which received about 1,500 comments from over 75 individuals or organisations, including governments, academics, AI experts, businesses, and civil society organisations. OPSI made many revisions and additions to respond to the feedback received.

AI is reshaping the landscape of cyber defense. As new security fissures open up, threat analysts deploy more powerful tools to prevent and respond to attacks. Nicole Eagan, CEO of Darktrace, joins Azeem Azhar to discuss the escalating arms race in this new cybersecurity landscape. HBR Presents is a network of podcasts curated by HBR editors, bringing you the best business ideas from the leading minds in management. The views and opinions expressed are solely those of the authors and do not necessarily reflect the official policy or position of Harvard Business Review or its affiliates.

A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation. Yet effective accountability and regulation is stymied by a persistent lack of resources supporting public understanding of algorithms and artificial intelligence. Through interactions with a US-based civil rights organization and their coalition of community organizations, we identify a need for (i) heuristics that aid stakeholders in distinguishing between types of analytic and information systems in lay language, and (ii) risk assessment tools for such systems that begin by making algorithms more legible. The present work delivers a toolkit to achieve these aims. This paper both presents the Algorithmic Equity Toolkit (AEKit) Equity as an artifact, and details how our participatory process shaped its design. Our work fits within human-computer interaction scholarship as a demonstration of the value of HCI methods and approaches to problems in the area of algorithmic transparency and accountability.

ABSTRACT The V oxCeleb Speaker Recognition Challenge 2019 aimed to assess how well current speaker recognition technology is able to identify speakers in unconstrained or'in the wild' data. It consisted of: (i) a publicly available speaker recognition dataset from Y ouTube videos together with ground truth annotation and standardised evaluation software; and (ii) a public challenge and workshop held at Interspeech 2019 in Graz, Austria. This paper outlines the challenge and provides its baselines, results and discussions. Index T erms-- speaker verification, unconstrained conditions 1. INTRODUCTION The V oxCeleb Speaker Recognition Challenge (V oxSRC) 2019 was the first of a new series of speaker recognition challenges that are intended to be hosted annually. V oxSRC 2019 consisted of: (i) a publicly available speaker recognition dataset with speech segments'in the wild', together with ground truth annotations and standardised evaluation software; and (ii) a public challenge and workshop held at Interspeech 2019 in Graz, Austria.

-- In this paper, we propose SwarmNet - a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents in a centralized manner . T ested on artificially generated swarm motion data, the network achieves high levels of prediction accuracy and imitation authenticity. We compare our model to previous approaches for modelling interaction systems and show how modifying components of other models gradually approaches the performance of ours. Finally, we also discuss an extension of SwarmNet that can deal with nondeterministic, noisy, and uncertain environments, as often found in robotics applications. Multi-Robot Systems (MRS) [1] describe groups of robotic agents that collectively perform complex tasks in a distributed and parallel manner through repeated interactions among each other and the environment. Such systems have attracted considerable attention in recent years with remarkable successes in a number of application domains, including defense, agriculture, logistics, disaster management, and entertainment. In particular, today's fast-paced online economy is largely fuelled by tens of thousands of warehouse robots that transport millions of items across fulfillment centers all over the world. Despite this progress, programming groups of robots to perform a joint task is still considered a complex, time-consuming, and extremely challenging endeavour. One prominent formalism for the specification of MRS is based on the identification of cost functions [2] governing the group behavior. However, this approach is not intuitive and requires a deep understanding of complex theoretical concepts across a number of mathematical fields, e.g., graph theory, manifold theory, nonlinear optimization, etc. In addition, the real-world ramifications of even small changes in a given cost function are extremely difficult to foresee.

The top public-sector use cases for artificial intelligence (AI) are quality control, workforce management and cybersecurity, according to a recent survey. Quality control issues such as detecting defects and finding errors in software code topped the list of AI uses with nearly half -- 47% of survey respondents -- citing it, according to "Government executives on AI: Surveying how the public sector is approaching an AI-enabled future," a report released by Deloitte Consulting last month. Workforce management tasks, such as recruiting and training, and cybersecurity tied for second place with 38% of respondents citing them. Rounding out the top three was another tie: Thirty-five percent said IT automation and predictive analytics were their main uses for AI. Although public-sector organizations are largely enthusiastic about using AI, agencies are feeling the growing pains that come with the maturation of any technology.

Army Secretary Ryan McCarthy isn't ready to invest in the hype around artificial intelligence just yet. Rather, he's set the Army's focus for the next two years on acquiring the cloud architecture that can serve as the foundation for AI in the future. Expect to see more action and discussion around cloud than on AI in the Army "because you've got to put the horse in front of the cart in order to pull it," McCarthy said Thursday at an AEI event. "Cloud has to happen to maximize AI," he said. But if you talk to the people in the financial industry who basically did it first, the online trading with writing in an algorithm that helps you make a decision on whether to buy a barrel of oil or not -- that's from cloud architecture.