In a February 2018 interview, Liz Goli, Commissioner of Queensland's Office of State Revenue (OSR), sat back in her chair: "The machine can actually improve our empathy with our customers," she reflected. Now that's interesting – the idea that an unfeeling machine could help human beings be more empathetic towards other human beings! Late last year, OSR implemented a successful machine learning prototype, and it's moving forward with a production pilot of this emerging technology. "We don't want a system where the machine is making decisions. But we do want the machine to offer up next best-action recommendations to our staff that they have the option to follow – or not – based on their experience and knowledge of how the legislation should be applied… We'd also like a system that can ingest Big Data and take action within certain parameters. For example, in case of a natural disaster, the machine might be able to find out which customers are impacted and replace debt-collection notices with proactive letters giving additional time to pay."

Microsoft has become the latest company dragged into the tech industry's ethical reckoning over the use of its products by government agencies. On Sunday, critics noted a blog post from January in which Microsoft touted its work with US Immigration and Customs Enforcement (ICE). The post celebrated a government certification that allowed Microsoft Azure, the company's cloud-computing platform, to handle sensitive unclassified information for ICE. The sales-driven blog post outlined ways that ICE might use Azure Government, including enabling ICE employees to "utilize deep learning capabilities to accelerate facial recognition and identification," Tom Keane, a general manager at Microsoft wrote. "The agency is currently implementing transformative technologies for homeland security and public safety, and we're proud to support this work with our mission-critical cloud," the post added.

Add Microsoft to the list of companies whose government deals are provoking outrage both inside and outside their offices. The Redmond firm sparked a wave of criticism on social networks after users discovered a January blog post noting that the company's Azure cloud service team was "proud to support" US Immigration and Customs Enforcement, which has come under fire for policies that include separating children from parents. Microsoft briefly took down the post on June 18th in response, but the company has since described it as "a mistake" and replaced the content. In a follow-up post, Microsoft stressed that it was "dismayed" by ICE's "forcible separation" of children, and said it didn't believe technology that could directly aid in this process (such as face recognition and natural language processing) was in use at the agency. Whatever the extent of Microsoft's involvement, the connection is causing alarm among its employees.

Waymo sued Uber in February 2017, claiming that former Waymo executive Anthony Levandowski downloaded more than 14,000 confidential files before leaving to set up a self-driving truck company, called Otto, which Uber acquired soon after. Levandowski has declined to answer questions about the allegations, citing constitutional protections against self-incrimination. Waymo claims former Waymo executive Anthony Levandowski downloaded more than 14,000 confidential files before leaving. Levandowski, then-head of Uber's self-driving program, is pictured above in 2016 The trial had been scheduled to begin on Dec. 4., but Waymo said it learned of new evidence after the U.S. Department of Justice shared it with Alsup. As a result, judge said in November that Uber withheld evidence in the lawsuit, delaying a trial to give Waymo time to review a letter alleging Uber trained employees to steal trade secrets and hide their tracks.

ABSTRACT This paper studies the effect of various hyper-parameters and their selection for the best performance of the deep learning model proposed in [1] for distributed attack detection in the Internet of Things (IoT). The findings show that there are three hyper-parameters that have more influence on the best performance achieved by the model. As a consequence, this study shows that the model's accuracy as reported in the paper is not achievable, based on the best selections of parameters, which is also supported by another recent publication [2]. INTRODUCTION Diro and Chilamkurti [1] have introduced a distributed deep neural network model for intrusion detection in the IoT environment. The primary principle behind the proposed model is to train the deep neural network model using multiple nodes in a distributed computing environment while parameter sharing and optimization is done through a coordinating master node.

Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Existing techniques in this field are able to predict, at each step of a case, the likelihood that the case will end up in an undesired outcome. These techniques, however, do not take into account what process workers may do with the generated predictions in order to decrease the likelihood of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects. The framework incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptive process monitoring in a given setting. The paper also outlines an approach to optimize the generation of alarms given a dataset and a set of cost model parameters. The proposed approach is empirically evaluated using a range of real-life event logs.

Humans can ground natural language commands to tasks at both abstract and fine-grained levels of specificity. For instance, a human forklift operator can be instructed to perform a high-level action, like "grab a pallet" or a low-level action like "tilt back a little bit." While robots are also capable of grounding language commands to tasks, previous methods implicitly assume that all commands and tasks reside at a single, fixed level of abstraction. Additionally, methods that do not use multiple levels of abstraction encounter inefficient planning and execution times as they solve tasks at a single level of abstraction with large, intractable state-action spaces closely resembling real world complexity. In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular. We show that the accuracy of the grounding procedure is improved when simultaneously inferring the degree of abstraction in language used to communicate the task. Leveraging hierarchy also improves efficiency: our proposed approach enables a robot to respond to a command within one second on 90% of our tasks, while baselines take over twenty seconds on half the tasks. Finally, we demonstrate that a real, physical robot can ground commands at multiple levels of abstraction allowing it to efficiently plan different subtasks within the same planning hierarchy.

Computational studies of voting are mostly motivated by two intended applications: the coordination of societies of artificial agents, and the study of human collective decisions whose complexity requires the use of computational techniques. Both research directions are too often confined to theoretical studies, with unrealistic assumptions constraining their significance for real-world situations. Most practical applications of these results are therefore confined to low-stakes decisions, which are of great importance in expanding the use of algorithms in society, but are far from high-stakes choices such as political elections, referenda, or parliamentary decisions, which societies still make using old-fashioned technologies like paper ballots. In this paper I argue in favour of conceiving "voting avatars", artificial agents that are able to act as proxies for voters in collective decisions at any level of society. Besides being an ideal test-bed for a large number of techniques developed in the field of multiagent systems and artificial intelligence in general, agent-mediated social choice may also suggests innovative solutions to the low voter participation that is endemic in most practical implementations of electronic decision processes.

Roundtable Analytics Inc., an AI/ML-based software company focused on improving the operational and financial performance of Emergency Departments (EDs), expanded its team of data scientists with the recent appointment of Michael Hyman, Ph.D., as vice president. Michael (Mike) Hyman has had a distinguished academic career, graduating with honors from the University of North Carolina with a Bachelor of Science in mathematics and a minor in chemistry. Pursuing his passion for the application of data science, Mike went on to earn both an M. Stat and Ph.D. in statistics from the University of Florida, one of the premier statistics graduate programs in the U.S. During his studies at UF, he was named a National Science Foundation Fellow, receiving an IGERT award for interdisciplinary training, and shared an office with the future founders of Roundtable Analytics Inc. Prior to joining Roundtable, Mike spent over five years working with the U.S. Department of Agriculture, performing statistical and geospatial research and developing the statistical methodology for projects such as the U.S. Census of Agriculture. Previously, during his spare time, Mike organized and completed a charity bike ride across the United States and is delighted to now return to his hometown of Raleigh, North Carolina.

The government is obsessed with your face. This is more of a creepy stalker fixation. From the federal Department of Homeland Security down to local police departments, governmental use of biometric facial recognition software has gained a startling amount of traction in recent years. And these agencies are getting help from big business, to boot. For example, DHS is reportedly developing a massive new biometric and biographic database with extensive data on citizens and foreigners alike.