Collaborating Authors


iiot ai_2022-06-03_03-34-51.xlsx


The graph represents a network of 1,617 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 03 June 2022 at 10:40 UTC. The requested start date was Friday, 03 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 6-hour, 42-minute period from Tuesday, 31 May 2022 at 17:17 UTC to Friday, 03 June 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Missed Out on Nvidia? 3 Artificial Intelligence Stocks to Buy Now


World-renowned semiconductor producer Nvidia is widely regarded as a pioneering force behind artificial intelligence (AI), and it remains a leader in the field today. But AI is a rapidly growing industry with plenty of room for other contributors, and in fact, some experts predict the majority of companies will be using AI by 2030, adding $13 trillion in value to the global economy. Therefore, while Nvidia is a $413 billion giant today, three Motley Fool contributors think C3ai (AI -0.70%), Riskified (RSKD 0.69%), and CrowdStrike (CRWD 0.83%) are artificial intelligence powerhouses of the future. Anthony Di Pizio (C3ai): One thing the artificial intelligence industry is missing is accessibility. Typically, only large technology companies with the financial resources and the ability to attract talented developers have been able to use AI in a meaningful capacity.

Artificial Intelligence in Cyber Security: Benefits and Drawbacks.


You can use artificial intelligence (AI) to automate complex repetitive tasks much faster than a human. AI technology can sort complex, repetitive input logically. That's why AI is used for facial recognition and self-driving cars. But this ability also paved the way for AI cybersecurity. This is especially helpful in assessing threats in complex organizations. When business structures are continually changing, admins can't identify weaknesses traditionally.

Why we need philosophy and ethics of cyber warfare


Cyber-attacks are rarely out of the headlines. We know state actors, terrorists, and criminals can leverage cyber-means to target the digital infrastructures of our societies. We have also learned that, insofar as our societies grow dependent on digital technologies, they become more vulnerable to cyber-attacks. There is no shortage of examples, ranging from the 2007 attacks against Estonia digital services and 2008 cyber-attack against a nuclear power plant in Georgia to WannaCry and NotPetya, two ransomware attacks that encrypted data and demanded ransom payments, and the ransomware cyber-attack on the US Colonial Pipeline, a US oil pipeline system that provides fuel to South-eastern States. My work focuses mostly on state vs state cyber-attacks.

State of AI Ethics Report (Volume 6, February 2022) Artificial Intelligence

This report from the Montreal AI Ethics Institute (MAIEI) covers the most salient progress in research and reporting over the second half of 2021 in the field of AI ethics. Particular emphasis is placed on an "Analysis of the AI Ecosystem", "Privacy", "Bias", "Social Media and Problematic Information", "AI Design and Governance", "Laws and Regulations", "Trends", and other areas covered in the "Outside the Boxes" section. The two AI spotlights feature application pieces on "Constructing and Deconstructing Gender with AI-Generated Art" as well as "Will an Artificial Intellichef be Cooking Your Next Meal at a Michelin Star Restaurant?". Given MAIEI's mission to democratize AI, submissions from external collaborators have featured, such as pieces on the "Challenges of AI Development in Vietnam: Funding, Talent and Ethics" and using "Representation and Imagination for Preventing AI Harms". The report is a comprehensive overview of what the key issues in the field of AI ethics were in 2021, what trends are emergent, what gaps exist, and a peek into what to expect from the field of AI ethics in 2022. It is a resource for researchers and practitioners alike in the field to set their research and development agendas to make contributions to the field of AI ethics.

Technology Ethics in Action: Critical and Interdisciplinary Perspectives Artificial Intelligence

This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.

Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Explainable AI for B5G/6G: Technical Aspects, Use Cases, and Research Challenges Artificial Intelligence

When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG), promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

Survey XII: What Is the Future of Ethical AI Design? – Imagining the Internet


Results released June 16, 2021 – Pew Research Center and Elon University's Imagining the Internet Center asked experts where they thought efforts aimed at ethical artificial intelligence design would stand in the year 2030. Some 602 technology innovators, developers, business and policy leaders, researchers and activists responded to this specific question. The Question – Regarding the application of AI Ethics by 2030: In recent years, there have been scores of convenings and even more papers generated proposing ethical frameworks for the application of artificial intelligence (AI). They cover a host of issues including transparency, justice and fairness, privacy, freedom and human autonomy, beneficence and non-maleficence, freedom, trust, sustainability and dignity. Our questions here seek your predictions about the possibilities for such efforts. By 2030, will most of the AI systems being used by organizations of all sorts employ ethical principles focused primarily on the public ...

On the Opportunities and Risks of Foundation Models Artificial Intelligence

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.