Last month, the federal Food and Drug Administration (FDA) approved nearly half a dozen of their algorithms designed to detect heart murmurs and atrial fibrillation, irregular heartbeats that could lead to stroke or blood clots. And in December, the FDA granted a "breakthrough" device designation to an algorithm that analyzes data from the heart's electrical impulses for evidence of heart failure. Such a designation allows the agency to fast track significant innovations for approval.

The Trump administration's Office of Science and Technology Policy has released its inaugural report on artificial intelligence. The assessment comes a year after the White House launched the American AI Initiative under Executive Order 13859, which "focuses the resources of the federal government to support AI innovation," the document states. Notably, the 36-page document mentions the word "privacy" 18 times. "We remain committed to supporting the development and application of AI in a way that promotes public trust, protects civil liberties, and respects the privacy and dignity of every individual," U.S. Chief Technology Officer Michael Kratsios and Deputy U.S. CTO Lynne Parker state in their introduction. To help promote "responsible approach to AI," the administration calls for investment in AI research and development.

Smartphone owners are being given new rights to have their device repaired under laws introduced by the EU that could put an end to'throwaway culture'. Manufacturers will made to fix broken electronic devices under the EU's new Circular Economy Action Plan (CEAP), which will also cover the UK despite Brexit. The plan, unveiled on Wednesday by the European Commission, will give Europeans'the right to repair' by making devices easier to fix. The laws, which will also apply to tablets, laptops and printers, focus on a more circular economy – where electronic resources are kept in use as long as possible. Major tech companies making devices hard to fix, including Apple, Samsung and Huawei, is creating an electronic and electrical rubbish mountain – wasting resources and blighting the environment, say green campaigners.

MOUNTAIN VIEW, Calif., March 11, 2020 – Lenovo and SentinelOne, an autonomous cybersecurity platform company, announced a strategic partnership to integrate SentinelOne's autonomous endpoint protection platform within Lenovo's ThinkShield security portfolio. Lenovo customers now have the ability to purchase devices with SentinelOne, delivering real-time prevention, ActiveEDR, IoT security, and cloud workload protection powered by patented Behavioral AI. Security by design is the foundation with which Lenovo builds its ThinkShield portfolio, protecting customers with the most secure endpoint solutions. With today's announcement, SentinelOne is now a core component of Lenovo's ThinkShield security offerings, empowering workstations, servers, cloud workloads, and IoT devices to autonomously defend themselves in real-time. Its patented AI models live on each device, predicting tomorrow's attacks today and enabling devices to self-heal from any attack instantaneously.

This study considers the expressiveness (that is the expressive power or expressivity) of different types of Knowledge Organization Systems (KOS) and discusses its potential to be machine-processable in the context of the Semantic Web. For this purpose, the theoretical foundations of KOS are reviewed based on conceptualizations introduced by the Functional Requirements for Subject Authority Data (FRSAD) and the Simple Knowledge Organization System (SKOS); natural language processing techniques are also implemented. Applying a comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject headings system (LCSH) and a classification scheme (DDC). These are compared with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts and relations. It was observed that LCSH and DDC focus on the formalism of character strings (nomens) rather than on the modelling of semantics; their definition of what constitutes a concept is quite fuzzy, and they comprise a large number of complex concepts. By contrast, thesauri have a coherent definition of what constitutes a concept, and apply a systematic approach to the modelling of relations. Ontologies explicitly define diverse types of relations, and are by their nature machine-processable. The paper concludes that the potential of both the expressiveness and machine processability of each KOS is extensively regulated by its structural rules. It is harder to represent subject headings and classification schemes as semantic networks with nodes and arcs, while thesauri are more suitable for such a representation. In addition, a paradigm shift is revealed which focuses on the modelling of relations between concepts, rather than the concepts themselves.

In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks. We pretrain deep embedding models (BERT) on medical notes from the MIMIC-III hospital dataset, and quantify potential disparities using two approaches. First, we identify dangerous latent relationships that are captured by the contextual word embeddings using a fill-in-the-blank method with text from real clinical notes and a log probability bias score quantification. Second, we evaluate performance gaps across different definitions of fairness on over 50 downstream clinical prediction tasks that include detection of acute and chronic conditions. We find that classifiers trained from BERT representations exhibit statistically significant differences in performance, often favoring the majority group with regards to gender, language, ethnicity, and insurance status. Finally, we explore shortcomings of using adversarial debiasing to obfuscate subgroup information in contextual word embeddings, and recommend best practices for such deep embedding models in clinical settings.

Context: Autonomous Systems (ASs) are becoming increasingly pervasive in today's society. One reason lies in the emergence of sophisticated Artificial Intelligence (AI) solutions that boost the ability of ASs to self-adapt in increasingly complex and dynamic environments. Companies dealing with AI models in ASs face several problems, such as users' lack of trust in adverse or unknown conditions, and gaps between systems engineering and AI model development and evolution in a continuously changing operational environment. Objective: This vision paper aims to close the gap between the development and operation of trustworthy AI-based ASs by defining a process that coordinates both activities. Method: We synthesize the main challenges of AI-based ASs in industrial settings. To overcome such challenges, we propose a novel, holistic DevOps approach and reflect on the research efforts required to put it into practice. Results: The approach sets up five critical research directions: (a) a trustworthiness score to monitor operational AI-based ASs and identify self-adaptation needs in critical situations; (b) an integrated agile process for the development and continuous evolution of AI models; (c) an infrastructure for gathering key feedback required to address the trustworthiness of AI models at operation time; (d) continuous and seamless deployment of different context-specific instances of AI models in a distributed setting of ASs; and (e) a holistic and effective DevOps-based lifecycle for AI-based ASs. Conclusions: An approach supporting the continuous delivery of evolving AI models and their operation in ASs under adverse conditions would support companies in increasing users' trust in their products.

Digital transformation projects happening across public-sector organizations are a top priority and will transform activities as diverse as cybersecurity, citizen services and data analytics. In the same IDC survey, nearly a quarter of respondents say AI is currently a part of digital transformation efforts. Download this white paper to learn about the dynamic role AI is playing in the future of the public sector as it works towards complete digital transformation.

Artificial Intelligence is rapidly changing the environments of just about everything - from translation to speech recognition to jobs to decision-making processes. AI, with its thousands of promised benefits, is positioned to affect just about every business around the globe. But with all the hype surrounding what AI can automate and relieve human beings from manually having to do themselves, is AI truly the blessing it's presented itself to be - or a curse? The question doesn't arise from the skepticism on whether machines will become too intelligent or learn to the point of a robot takeover. While AI is fascinating and the stuff of science fiction, its emergence also raises many concerns, especially in its applications.

The five "key pillars" of the American AI Initiative are: The American AI Initiative follows several steps the Trump administration has already taken on A.I. A.I. needs to be a trusted technology; Government should invest in A.I.; Federal data should be available for A.I. to crunch; Barriers to achieving A.I. should be reduced; Someone should develop A.I. standards; Workers should be trained to do A.I., including federal workers; The U.S. should have an A.I. "action plan."