There is vast potential for AI, 5G and the Metaverse to be applied towards advancing the Environment, Social and Governance (ESG) cause, including Sustainability. Our global economy is facing challenges and the tragic warfare in Ukraine has had consequential impacts on energy supply for much of Europe and beyond. Indeed, McKinsey forecast that $275 Trillion may have to be spent in the period to 2050 (ranging from circa 7% to 9% of global GDP) to achieve carbon next zero. There is a strategic imperative for the EU, UK and the US (following the adoption of the Inflation Reduction Act in 2022) to push for accelerating the scaling and adoption of renewable energy and advancing battery storage technology at a time when action on Climate Change is a high priority and also the need to diversify energy supplies is growing. AI technology may play a key role in helping scale these areas.

The company's previous demo had involved marching a human out in a robot-like bodysuit, so when Optimus walked slowly around the stage, it was met with delight from the cheering crowd. Despite the show's futuristic framing, robotics experts were mostly underwhelmed by the reveal. Optimus' clunky attempts at something like a dance seemed less advanced than other humanoid robots, such as Honda's ASIMO, which played soccer with former President Barack Obama back in 2014. Tesla engineers boasted that Optimus' hand had as many as 11 degrees of freedom (that's to say, all the ways in which robotic parts can bend). In comparison, a robotic hand designed by a Japanese engineer back in 1963 had 27. What is it about Optimus that makes us feel threatened?

Robin Farmanfarmaian is a Silicon Valley-based entrepreneur working in technology and artificial intelligence. She has been involved with more than 20 early-stage biotech and healthcare startups, including ones working on medical devices and digital health. With more than 180 speaking engagements in 15 countries, she has educated audiences on many aspects of technology intersecting healthcare, including artificial intelligence and the shift in healthcare delivery to the patient's home. She has written four books, including The Patient as CEO: How Technology Empowers the Healthcare Consumer and, most recently, How AI Can Democratize Healthcare: The Rise in Digital Care. Healthcare IT News spoke with Farmanfarmaian to discuss where AI is impacting remote patient monitoring today, and how AI can democratize healthcare.

And last month a promotional video for machine-learning firm Paperspace Co. showed talking semblances of the actors Tom Cruise and Leonardo DiCaprio. None of these celebrities ever spent a moment filming these campaigns. In the cases of Messrs. Musk, Cruise and DiCaprio, they never even agreed to endorse the companies in question. All the videos of digital simulations were created with so-called deepfake technology, which uses computer-generated renditions to make the Hollywood and business notables say and do things they never actually said or did. Some of the ads are broad parodies, and the meshing of the digital to the analog in the best of cases might not fool an alert viewer.

In recent months there has been talk about how artificial intelligence can create images from textual prompts. Therefore, when one associates the words artificial intelligence and art, one immediately thinks of DALL-E, Stable Diffusion, and other algorithms. In this article, instead, I want to discuss why artworks are often less safe than we think, and how artificial intelligence can help preserve them. "Every act of creation is first of all an act of destruction." It is a mistake to think that cultural heritage is safe. Many of humanity's most valuable works are also among the most fragile. Throughout history, only a fraction of works of art has managed to survive over time. For example, during wars, cultural heritage is often damaged.

The United States is pushing for a narrower Artificial Intelligence definition, a broader exemption for general purpose AI and an individualised risk assessment in the AI Act, according to a document obtained by EURACTIV. The non-paper is dated October 2022 and was sent to targeted government officials in some EU capitals and the European Commission. It follows much of the ideas and wording of the initial feedback sent to EU lawmakers last March. "Many of our comments are prompted by our growing cooperation in this area under the U.S.-EU Trade and Technology Council (TTC) and concerns over whether the proposed Act will support or restrict continued cooperation," the document reads. The document is a reaction to the progress made by the Czech Presidency of the EU Council on the AI regulation last month.

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are non-independent and identically distributed (non-IID). In this paper, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data. Specifically, ERL consists of two stages in an iterative procedure: a rule cooperation stage that updates global rules by aggregating local rules based on their activation statuses and a rule evolution stage that evolves the global rules and updates the activation statuses of the local rules. This procedure improves both the generalization and personalization of the FedFNN for dealing with non-IID issues and data uncertainties. Extensive experiments conducted on a range of datasets demonstrate the superiority of the FedFNN over state-of-the-art methods.

Prior studies of zero-shot stance detection identify the attitude of texts towards unseen topics occurring in the same document corpus. Such task formulation has three limitations: (i) Single domain/dataset. A system is optimized on a particular dataset from a single domain; therefore, the resulting system cannot work well on other datasets; (ii) the model is evaluated on a limited number of unseen topics; (iii) it is assumed that part of the topics has rich annotations, which might be impossible in real-world applications. These drawbacks will lead to an impractical stance detection system that fails to generalize to open domains and open-form topics. This work defines OpenStance: open-domain zero-shot stance detection, aiming to handle stance detection in an open world with neither domain constraints nor topic-specific annotations. The key challenge of OpenStance lies in the open-domain generalization: learning a system with fully unspecific supervision but capable of generalizing to any dataset. To solve OpenStance, we propose to combine indirect supervision, from textual entailment datasets, and weak supervision, from data generated automatically by pre-trained Language Models. Our single system, without any topic-specific supervision, outperforms the supervised method on three popular datasets. To our knowledge, this is the first work that studies stance detection under the open-domain zero-shot setting. All data and code are publicly released.

Recent research has shown that Machine Learning/Deep Learning (ML/DL) models are particularly vulnerable to adversarial perturbations, which are small changes made to the input data in order to fool a machine learning classifier. The Digital Twin, which is typically described as consisting of a physical entity, a virtual counterpart, and the data connections in between, is increasingly being investigated as a means of improving the performance of physical entities by leveraging computational techniques, which are enabled by the virtual counterpart. This paper explores the susceptibility of Digital Twin (DT), a virtual model designed to accurately reflect a physical object using ML/DL classifiers that operate as Cyber Physical Systems (CPS), to adversarial attacks. As a proof of concept, we first formulate a DT of a vehicular system using a deep neural network architecture and then utilize it to launch an adversarial attack. We attack the DT model by perturbing the input to the trained model and show how easily the model can be broken with white-box attacks.

With the rapid development and integration of artificial intelligence (AI) methods in next-generation networks (NextG), AI algorithms have provided significant advantages for NextG in terms of frequency spectrum usage, bandwidth, latency, and security. A key feature of NextG is the integration of AI, i.e., self-learning architecture based on self-supervised algorithms, to improve the performance of the network. A secure AI-powered structure is also expected to protect NextG networks against cyber-attacks. However, AI itself may be attacked, i.e., model poisoning targeted by attackers, and it results in cybersecurity violations. This paper proposes an AI trust platform using Streamlit for NextG networks that allows researchers to evaluate, defend, certify, and verify their AI models and applications against adversarial threats of evasion, poisoning, extraction, and interference.