You can become utterly dependent on a new glamorous technology, be it cyber-space, artificial intelligence. . . But does it create a potential achilles heel? Artificial intelligence (AI) has become the technical focal point for advancing naval and Department of Defense (DoD) capabilities. Secretary of the Navy Carlos Del Toro listed AI first among his priorities for innovating U.S. naval forces. Chief of Naval Operations Admiral Michael Gilday listed it as his top priority during his Senate confirmation hearing.2

Steve Durbin is Chief Executive of Information Security Forum. He is a frequent speaker on the Board's role in cybersecurity and technology. A technology-led revolution, dubbed Industry 4.0, is gathering pace in the industrial world where traditional processes and legacy technologies are being replaced by smart devices, automated machines and advanced forms of computing. The rise of Cyber Physical Systems (CPS), owing to exponential growth in technologies like the Internet of Things (IoT), artificial intelligence (AI), cloud, robots, drones, sensors, etc., is helping manufacturers improve efficiencies, productivity and the autonomous operation of production lines. Businesses are pouring billions of dollars in AI and automation, and the Industrial IoT (IIoT) alone is set to become a $500 billion market by 2025.

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Facial recognition tools are becoming exceptionally accurate in identifying people from images. However, this comes at the cost of privacy for users of online services with photo management (e.g. social media platforms). Particularly troubling is the ability to leverage unsupervised learning to recognize faces even when the user has not labeled their images. In this paper we propose Ulixes, a strategy to generate visually non-invasive facial noise masks that yield adversarial examples, preventing the formation of identifiable user clusters in the embedding space of facial encoders. This is applicable even when a user is unmasked and labeled images are available online. We demonstrate the effectiveness of Ulixes by showing that various classification and clustering methods cannot reliably label the adversarial examples we generate. We also study the effects of Ulixes in various black-box settings and compare it to the current state of the art in adversarial machine learning. Finally, we challenge the effectiveness of Ulixes against adversarially trained models and show that it is robust to countermeasures.

Representing words by vectors, or embeddings, enables computational reasoning and is foundational to automating natural language tasks. For example, if word embeddings of similar words contain similar values, word similarity can be readily assessed, whereas judging that from their spelling is often impossible (e.g. cat /feline) and to predetermine and store similarities between all words is prohibitively time-consuming, memory intensive and subjective. We focus on word embeddings learned from text corpora and knowledge graphs. Several well-known algorithms learn word embeddings from text on an unsupervised basis by learning to predict those words that occur around each word, e.g. word2vec and GloVe. Parameters of such word embeddings are known to reflect word co-occurrence statistics, but how they capture semantic meaning has been unclear. Knowledge graph representation models learn representations both of entities (words, people, places, etc.) and relations between them, typically by training a model to predict known facts in a supervised manner. Despite steady improvements in fact prediction accuracy, little is understood of the latent structure that enables this. The limited understanding of how latent semantic structure is encoded in the geometry of word embeddings and knowledge graph representations makes a principled means of improving their performance, reliability or interpretability unclear. To address this: 1. we theoretically justify the empirical observation that particular geometric relationships between word embeddings learned by algorithms such as word2vec and GloVe correspond to semantic relations between words; and 2. we extend this correspondence between semantics and geometry to the entities and relations of knowledge graphs, providing a model for the latent structure of knowledge graph representation linked to that of word embeddings.

AI influences nearly 8 billion people and human & earth diverse ecosystems on an unprecedented scale. Startups accelerate to incorporate AI innovation as AI tools proliferate. UNESCO is the United Nations Educational, Scientific and Cultural Organization. The UNESCO recommendations on the ethics of AI recently adopted by member states provides a foundational global agreement on AI Ethics. The objectives ultimately drive emerging AI driven technologies that are trustworthy, safe, human-centered for the benefit of people and humanity.

According to the World Economic Forum Global Cybersecurity Outlook 2022 report, 48% of executives believe that artificial intelligence (AI) will influence cyber transformation in the next two years. AI is a powerful tool for both cybercriminals and cybersecurity experts. Hackers are using AI to make their attacks more sophisticated and harder to detect, while cybersecurity specialists are finding ways of integrating AI into corporate cybersecurity systems to minimize financial and reputational losses. A survey of the CEOs of the world s 500 most influential companies across 11 industries found that cybersecurity issues will be the greatest risk to company growth in the next three years. This is unsurprising, given the broader threat landscape and the global lack of cybersecurity staff, as well as the increasing frequency and severity of cyber attacks.

With 2020 well behind us, COVID-19's presence continues to linger worldwide. Of all the industries that have been forever changed by the impacts of the global pandemic, healthcare arguably has changed the most. With significant advances in technology and methods necessary to support the high demand for access to healthcare and the growing digitization of protected health information, the healthcare industry has evolved in novel ways to continue to deliver the same exceptional quality of service. As we press on into the future, it's critical to remain mindful of the trends driving healthcare technology in 2022. Although legacy software and infrastructure are critical to the success of modern hospitals and care centers, it's important that we consider how those systems can integrate with newer technologies or how they may eventually be replaced with more reliable systems. The focus should be on improving performance, productivity, efficiency, and security without sacrificing reliability or accessibility. If you're ready to explore the tech innovations driving the healthcare industry towards digital transformation this year, let's take a look at the most important technologies that have the potential to transform your organization.

Kellton Tech (BSE: KELLTONTEC) (NSE: KELLTONTEC), a global leader in next-generation digital transformation and enterprise intelligence solutions, announced that it has been selected as a technology partner by Hyderabad Metropolitan Water Supply and Sewerage Board (HMWSSB) to revolutionize its citizen service delivery and create world-class experiences. As a part of this engagement, Kellton Tech will leverage descriptive analytics and IoT-based smart metering technology to support HMWSSB with real-time information for planning, monitoring, and diagnosis of its systems and processes across its value chains. This holistic solution approach will help HMWSSB forecast demand and dynamically price resource consumption based on predictive analytics, thus promoting more efficient water use and ensuring fair pricing to citizens. Furthermore, HMWSSB will drive faster data-driven decisions, better optimize the government machinery and ecosystem for the timely elimination of inefficiencies, and curate frictionless experiences for the residents of Hyderabad Metropolitan today and in the future. Kellton Tech will also collaborate to help HMWSSB streamline its grievance redressal by introducing AI-based Metro Customer Care and addressing the needs of citizens and society with more agility and responsiveness.

Rep. Elise Stefanik, R-NY, reflects on President Biden's'reckless' first year in office on'Fox & Friends.' FIRST ON FOX: The GOP is evolving its game when it comes to digital fundraising, and it's already generating returns for the party. Small-dollar digital donations are becoming a focus of many Republican campaigns through the fundraising platform WinRed, following the lead set by House Republican Conference chairwoman Rep. Elise Stefanik of New York. Stefanik's campaign gave Fox News Digital an exclusive peek behind the curtain of how the congresswoman and other GOP candidates are adapting their digital strategies to court these increasingly coveted donors. The New York Republican's campaign told Fox News Digital that the congresswoman was one of the early candidates to capitalize off of the digital space and has compiled a list of over 500,000 small-dollar donors through WinRed. The campaign also noted that Stefanik has already raised over $5 million for other GOP candidates this cycle via WinRed.