The UK is "on the cusp of a huge health tech revolution that could transform patient experience", said health minister Matt Hancock when he announced £250 million to fund a new AI Lab for the National Health Service earlier this month. The lab has been set up to bring together academics and technology companies to work on some of the biggest challenges in health and care. But the AI sector has a reputation for overpromising on what it can deliver – as do politicians.

The notion of cyber-physical systems (CPS) describes the co mbination of Information and Communication Technology (ICT) and software (the "cyber" part) with physical compone nts. A CPS can emerge from embedded systems by internetworking them. The first big research program focusi ng on CPS has been started by the US National Science Foundation in 2006, where the term CPS is defined in as such tha t it "refers to the tight conjoining of and coordination between computational and physical resources," stating "[ w]e envision that the cyber-physical systems of tomorrow will far exceed those of today in terms of adaptability, auto nomy, efficiency, functionality, reliability, safety, and usability" [1]. While the notion of CPS by the U.S. National Science Foundati on, as outlined above, includes ICT, it does not explicitly name Artificial Intelligence (AI) as a necessary component to raise an embedded system to the status of a CPS. Y et, the availability of sensory data together with a co mmunications system and the ability to exert actions upon the physical world that have been planned for the whole compo und of embedded systems components readily suggests that issues of planning, the increase of reflectivity, effici ency, and lowering resource usage is achieved by increasing the "intelligence" of the overall system. As such, research ers in the domain of AI have found numerous application domains. However, the two worlds of CPS and AI usually operate on diffe rent terms: CPS require operation within well-defined boundaries, i.e., as far as possible deterministic behavio r within well-known, strictly enforced margins of error. In contrast, many AI techniques--Artificial Neural Networks (A NNs) foremost--are firmly rooted in the domain of statistics, which is probably very well seen in the ANN train ing process.

A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision is b ecoming important to ensure the secure and safe use of AI techniques. Thus, we, the Special Interest Group on Semantic Web and Ontology of the Japanese Society for AI, organized a challenge calling for techniques that reason and/or estimate which character s are criminals while providing a reasonable explanation based on an open knowledge graph of a well - known Sherlock Holmes mystery story . This paper presents a summary report of the first challenge held in 2018, including the knowledge graph construction, t he techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results. The first prize went to an approach that formalized the problem as a constraint satisfaction problem and solved it using a lightweight formal method; the secon d prize went to an approach that used SPARQL and rules; the best resource prize went to a submission that constructed word embedding of characters from all sentences of Sherlock Holmes novels; and the best idea prize went to a discussion multi - agents model . We conclude this paper with the plans and issues for the next challenge in 2019.

Cutting-edge robots are on display at the 2019 World Robot Conference in Beijing, running from August 20 to 25, are expected to attract nearly 200 guests from 22 countries. The conference features a series of exhibition areas for new robotic technologies and products - including medical, multi-legged, and smart logistics - as well as four contests with an anticipated 4,500 professional participants. Over 700 robots specialising with more than 21 industrial applications will be exhibited between now and the close of the conference. Among those exhibiting will be HRG Robotics, whose, president Wang Meng, said: 'We will be showcasing a string of successful companies which have got off the ground through the help of HRG, alongside our representative products at WRC 2019, as we aim to form new partnerships with companies around the world.' Also on display will be SmartBird, created by German firm Festo, whose design was inspired by the herring gull and whose flight mimics that of the bird. The ultralight flying drone was created with the best aerodynamics and maximum agility in mind and it is able to take off, fly and land under its own power.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Cybersecurity, a huge industry worth over $100 billion, is regularly subject to buzzwords. Cybersecurity companies often (pretend) to use new state-of-the-art technologies to attract customers and sell their solutions. Naturally, with artificial intelligence being in one of its craziest hype cycles, we're seeing plenty of solutions that claim to use machine learning, deep learning and other AI-related technologies to automatically secure the networks and digital assets of their clients. But contrary to what many companies profess, machine learning is not a silver bullet that will automatically protect individuals and organizations against security threats, says Ilia Kolochenko, CEO of ImmuniWeb, a company that uses AI to test the security of web and mobile applications.

Over the past several years, commercial use of biometric data has become increasingly prevalent. In response, several states have adopted biometric data privacy legislation. Consequently, companies that rely on biometric data face new regulatory risks, in addition to increased legal exposure to individual and class action lawsuits. In fact, the Ninth Circuit Court of Appeals recently affirmed certification of a class action alleging Facebook's face-scanning practices violate Illinois' biometric privacy law, finding that the class alleged sufficiently concrete injuries based on Facebook's alleged use of facial recognition technology without users' consent to establish standing. Insurance policies currently available on the market, including cyber insurance policies, may not adequately cover these risks.

The United States Geological Survey confirmed with the Center for Data Innovation over email on July 22, 2019, that "1-meter DEMs are available or in progress for 45% of the country."

Last week, researchers from OpenAI and Google introduced Activation Atlases, a tool that helps make sense of the inner workings of neural networks by visualizing how they see and classify different objects. At first glance, Activation Atlases is an amusing tool helps you see the world through the eyes of AI models. But it also one of the many important efforts that are helping explain decisions made by neural networks, one of the greatest challenges of the AI industry and an important hurdle in trusting AI in critical tasks. Artificial intelligence, or namely its popular subset deep learning, is far from the only kind of software we're using. We've been using software in different fields for decades.

--Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by adding maliciously crafted perturbations to the benign inputs. As a growing number of attacks have been reported to generate adversarial inputs of varying sophistication, the defense-attack arms race has been accelerated. MODEF intelligently combines unsupervised model denoising ensemble with supervised model verification ensemble by quantifying model diversity, aiming to boost the robustness of the target model against adversarial examples. Evaluated using eleven representative attacks on popular benchmark datasets, we show that MODEF achieves remarkable defense success rates, compared with existing defense methods, and provides a superior capability of repairing adversarial inputs and making correct predictions with high accuracy in the presence of black-box attacks. The recent advances in deep neural networks (DNNs) have powered numerous applications in different domains due to their outstanding performance compared to traditional machine learning techniques. However, it has been shown that DNNs can be easily fooled by adversarial inputs [1], making them become a double-edged sword as the vulnerability of DNNs to adversarial attacks has posed serious threats to many security-critical applications, such as biometric authentication and autonomous driving. As a number of defenses are being proposed, more attacks of varying sophistication have been put forward, accelerating the defense-attack arms race. Some even argue that designing new attacks requires much less efforts than developing effective defenses. Thus, improving the robustness and defensibility against adversarial attacks is crucial. Adversarial examples are generated by maliciously perturbing benign examples sent to the target DNN model through querying its prediction API, aiming to fool and mislead the target model to misclassify by producing incorrect predictions randomly (untargeted attack) or purposefully (targeted attack).

--Regulatory compliance is an organization's adherence to laws, regulations, guidelines and specifications relevant to its business. Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements. Keeping up with the changes entail two main tasks: fetching the regulatory announcements that actually contain changes of interest, and incorporating those changes in the business process. In this paper we focus on the first task, and present a Compliance Change Tracking System, that gathers regulatory announcements from government sites, news sites, email subscriptions; classifies their importance i.e Actionability through a hierarchical classifier, and business process applicability through a multi-class classifier . Na ıve Bayes, logistic regression etc.), hierarchical classification method, rule based approach, hybrid approach with various preprocessing and feature selection methods; and show that despite the richness of other models, a simple hierarchical classification with bag-of-words features works the best for Actionability classifier and multi-class logistic regression works the best for Applicability classifier . The system has been deployed in global delivery centers, and has received positive feedback from payroll compliance officers. Organizations are faced with rapidly changing regulatory policies, and ever-growing number of regulations.