Are Hosts, Replicants, and robot clones closer than we think? Black Mirror already uncomfortably aligns with the real world, but we might be even closer to more advanced concepts from that show and others, like Westworld and Altered Carbon, becoming reality. The Ringer offers a look at just how far away we could be from Hosts, Replicants and robotic clones following a new trailer release for Keanu Reeves' long lost Replicas movie. The entire catalog of Prince's creative work, both released and unreleased, is under the care of one man. If you've ever tasted a MRE (Meals, Ready to Eat), you know there's only so much you can do that doesn't require cooking.

Computer historians have staged a re-enactment of World War Two code-cracking at Bletchley Park. A replica code-breaking computer called a Bombe was used to decipher a message scrambled by an Enigma machine. Ruth Bourne, a former wartime code-cracker who worked at Bletchley and used the original Bombes, oversaw the modern effort. Enigma machines were used extensively by the German army and navy during World War Two. This prompted a massive effort by the Allies to crack the complex method they employed to scramble messages.

The adversarial vulnerability [27] of deep neural nets (DNNs) threatens their applicability in security critical tasks, e.g., autonomous cars [1], robotics [9], DNN-based malware detection systems [21, 8]. Since the pioneering work by Szegedy et al. [27], many advanced adversarial attack schemes have been devised to generate imperceptible perturbations to sufficiently fool the DNNs [7, 20, 6, 30, 12, 3]. And not only are adversarial attacks successful in white-box attacks, i.e. when the adversary has access to the DNN parameters, but attacks are also successful in black-box attacks, i.e. it has no access to the parameters. Black-box attacks are successful because one can perturb an image so it misclassifies on one DNN, and the same perturbed image also has a significant chance to be misclassified by another DNN; this is known as transferability of adversarial examples [23]. Due to the transferability of adversarial examples, it is very easy to attack neural nets in a black-box fashion [15, 5]. In fact, there exist universal perturbations that can imperceptibly perturb any image and cause misclassification for any given network [17]. There is much recent research on designing advanced adversarial attacks and defending against adversarial perturbation.

Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy. For instance, the OpenMalaria framework is a computationally-intensive simulation used by various nongovernmental and governmental agencies to understand malarial disease spread and effectiveness of intervention strategies, and subsequently design healthcare policies. Given that such shared results form the basis of inferences drawn, technological solutions designed, and day-today policies drafted, it is essential that the computations are validated and trusted. In particular, in a multi-agent environment involving several independent computing agents, a notion of trust in results generated by peers is critical in facilitating transparency, accountability, and collaboration. Using a novel combination of distributed validation of atomic computation blocks and a blockchain-based immutable audits mechanism, this work proposes a universal framework for distributed trust in computations. In particular we address the scalaibility problem by reducing the storage and communication costs using a lossy compression scheme. This framework guarantees not only verifiability of final results, but also the validity of local computations, and its cost-benefit tradeoffs are studied using a synthetic example of training a neural network. Machine learning, data science, and large-scale computations in general has created an era of computationdriven inference, applications, and policymaking [1], [2]. Technological solutions, and policies with far-reaching consequences are increasingly being derived from computational frameworks and data. Multi-agent sociotechnical systems that are tasked with working collaboratively on such tasks function by interactively sharing data, models, and results of local computation. However, when such agents are independent and lack trust, they might not collaborate with or trust the validity of reported computations of other agents. Quite often, these computations are also expensive and time consuming, and thus infeasible for recomputation by the doubting peer as a general course of action. In such systems, creating an environment of trust, accountability, and transparency in the local computations of individual agents promotes collaborative operation.

This article is featured in the new DZone Guide to Security: Defending Your Code. Get your free copy for more insightful articles, industry statistics, and more! In April 2018, a social media giant had to notify nearly 90 million users that their personal data might have been "improperly shared." It was a colossal security breach. In October 2017, tens of thousands of harmless cameras were used to produce a massive distributed denial-of-service (DDoS) attack that dramatically decreased websites' function or even took them down by sending them billions of requests.

As reported in recent issues of Health Affairs, the New England Journal of Medicine, Harvard Business Review and other publications, Artificial Intelligence (AI) and Machine Learning can achieve breakthroughs in improving patient safety and health and reducing waste. AI infers patterns, relationships, and rules directly from large volumes of data in ways that can exceed human cognitive capabilities. As we previously described, opportunities for greater digitalization and deployment of AI in health/health care are made possible by increased electronic data availability from mobile devices, sensors, cameras, and electronic health records (EHRs); faster data processing capabilities; and newly developed computing techniques. While AI solutions are still only used sparingly in health/health care organizations, those organizations able to increase their AI capabilities will likely be better positioned for the transition to value-based care and achieving desired results with greater efficiency. We offer considerations for policy makers as well as decision makers in health organizations to rapidly scale up responsible implementation of AI solutions that have the potential to improve health and reduce inefficiencies.

The biggest actual threat faced by humans, when it comes to AI, has nothing to do with robots. And, like almost everything bad, it disproportionately affects the poor and marginalized. Machine learning algorithms, whether in the form of "AI" or simple shortcuts for sifting through data, are incapable of making rational decisions because they don't rationalize -- they find patterns. That government agencies across the US put them in charge of decisions that profoundly impact the lives of humans, seems incomprehensibly unethical. When an algorithm manages inventory for a grocery store, for example, machine learning helps humans do things that would, otherwise, be harder.

Periscope Holdings, the leader in government procurement solutions, introduced an entirely new government bid experience for suppliers, powered by artificial intelligence (AI). Periscope Holdings is transforming public sector procurement with technology and solutions for both buyers and suppliers. Supplier solutions include BidSync government bid notification tools. The completely rebuilt BidSync supplier application leverages machine-learning technology to deliver the most relevant, winnable government opportunities for each user. Smart enough to automatically filter out bids that are irrelevant to each supplier's individual business, the new BidSync boasts a host of new features that save subscribers valuable time finding and evaluating bids, including: a savvy user interface, an AI-powered relevance engine, expanded bid details, powerful search capabilities and a visual relevance indicator.

More than 2,000 European AI experts have come together to collaborate on research and call for large-scale funding from the European Union to counter China and America's rapid progress in the field. "European artificial intelligence is at a crossroads given the huge investments in the technology in the United States and China," said the Confederation of Laboratories for Artificial Intelligence Research in Europe (Claire), which met for the first time in Brussels last week. The alliance urges the European Commission to implement an AI strategy for the EU as a whole along the lines of the US National AI Research and Development Plan that was released in late 2016, and China's Next Generation AI Development Plan that was issued the following year. Some 2,100 AI researchers from 29 countries across the continent have already signed up, according to Philipp Slusallek, scientific director of the German Research Centre for Artificial Intelligence Research and one of Claire's three initiators. He said one of the main goals of the collaboration was to nurture AI talent and retain it in Europe.

Prejudice is a human attitude involving generally negative and unsubstantiated prejudgement of others. When acted upon, this results in wide-ranging behaviours such as sexism, ageism and discrimination against sexual preference1,2,3 through to ethnic, racial, nationalistic and religious extremism4,5, with bias and intergroup conflict characterised as a "problem of the century"6. Most recently, prejudice has been highlighted in connection to global political events: for example anti-immigration prejudice was a strong correlate of support for Brexit7. The human disposition to categorize others through their group identity creates an opportunity for discrimination8,9,10. As a consequence of in-group formation11, which occurs through cultural or biological identification with others, or as a consequence of identity-less strangers mutually cooperating12, bias can take hold in two ways.