Starting next week, Meta will no longer put an easy-to-see label on Facebook images that were edited using AI tools, and it will make it much harder to determine if they appear in their original state or had been doctored. To be clear, the company will still add a note to AI-edited images, but you'll have to tap on the three-dot menu at the upper right corner of a Facebook post and then scroll down to find "AI Info" among the many other options. Only then will you see the note saying that the content in the post may have been modified with AI. Images generated using AI tools, however, will still be marked with an "AI Info" label that can be seen right on the post. Clicking on it will show a note that will say whether it's been labeled because of industry-shared signals or because somebody self-disclosed that it was an AI-generated image.

Body language experts told Dr. Phil Tuesday after the presidential debate that the ABC News moderators clearly favored Vice President Kamala Harris. Dr. Phil spoke with experts Scott Rouse and Greg Hartley in a special post-debate town hall broadcast. Rouse holds multiple certificates in advanced interrogation training and has been trained alongside the FBI, Secret Service, U.S. Military Intelligence, and the Department of Defense. Hartley is a former Army interrogator with expertise in intelligence, business, body language and behavior. When asked whether they saw bias from moderators David Muir and Linsey Davis in Tuesday's debate, Hartley said they were against former President Trump.

When a company triples in value in just a few months, as computer chip company Nvidia has, investors take notice. But regulators do too, because they know from experience how monopolies engage in illegal anti-competitive behavior that squashes competitors and manipulates the market to expand their dominance. The US Department of Justice (as well as other competition authorities and tech observers) suspects Nvidia has used such tactics to entrench its chips monopoly, and last month it was reported that the Department of Justice was opening an antitrust investigation. Before the pandemic, few beyond video game enthusiasts – whose top-of-the-line gaming computers and consoles are built on high-capacity Nvidia chips – had ever heard of the company. But thanks to the generative AI boom, Nvidia has become one of the fastest-growing companies ever, and its chips have powered every important AI milestone – including OpenAI's development of ChatGPT, which holds two-thirds of the AI business tools market.

China this week chose not to sign onto an international "blueprint" agreed to by some 60 nations, including the U.S., that looked to establish guardrails when employing artificial intelligence (AI) for military use. More than 90 nations attended the Responsible Artificial Intelligence in the Military Domain (REAIM) summit hosted in South Korea on Monday and Tuesday, though roughly a third of the attendees did not support the nonbinding proposal. AI expert Arthur Herman, senior fellow and director of the Quantum Alliance Initiative with the Hudson Institute, told Fox News Digital that the fact some 30 nations opted out of this important development in the race to develop AI is not necessarily cause for concern, though in Beijing's case it is likely because of its general opposition to signing multilateral agreements. Participants are shown prior to the closing session of the REAIM summit in Seoul, South Korea, on Sept. 10, 2024. "What it boils down to … is China is always wary of any kind of international agreement in which it has not been the architect or involved in creating and organizing how that agreement is going to be shaped and implemented," he said.

Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is represented by a homophilic graph. However, recent works have revealed that many relevant applications involve heterophilic data where the performance of GNNs can be notably compromised. To address this challenge, we present a simple yet effective architecture designed to mitigate the limitations of the homophily assumption. The proposed architecture reinterprets the role of graph filters in convolutional GNNs, resulting in a more general architecture while incorporating a stronger inductive bias than GNNs based on filter banks. The proposed convolutional layer enhances the expressive capacity of the architecture enabling it to learn from both homophilic and heterophilic data and preventing the issue of oversmoothing. From a theoretical standpoint, we show that the proposed architecture is permutation equivariant. Finally, we show that the proposed GNNs compares favorably relative to several state-of-the-art baselines in both homophilic and heterophilic datasets, showcasing its promising potential.

Generative Artificial Intelligence (AI) models may carry societal transformation to an extent demanding a delicate balance between opportunity and risk. This manuscript is the first of a series of White Papers informing the development of IEEE-SA's p3995: `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence (AI) Models', Chair: Marina Cort\^{e}s (https://standards.ieee.org/ieee/3395/11378/). In this first horizon-scanning we identify key attention areas for standards activities in AI. We examine different principles for regulatory efforts, and review notions of accountability, privacy, data rights and mis-use. As a safeguards standard we devote significant attention to the stability of global infrastructures and consider a possible overdependence on cloud computing that may result from densely coupled AI components. We review the recent cascade-failure-like Crowdstrike event in July 2024, as an illustration of potential impacts on critical infrastructures from AI-induced incidents in the (near) future. It is the first of a set of articles intended as White Papers informing the audience on the standard development. Upcoming articles will focus on regulatory initiatives, technology evolution and the role of AI in specific domains.

This paper addresses the critical challenge of building consumer trust in AI-powered customer engagement by emphasising the necessity for transparency and accountability. Despite the potential of AI to revolutionise business operations and enhance customer experiences, widespread concerns about misinformation and the opacity of AI decision-making processes hinder trust. Surveys highlight a significant lack of awareness among consumers regarding their interactions with AI, alongside apprehensions about bias and fairness in AI algorithms. The paper advocates for the development of explainable AI models that are transparent and understandable to both consumers and organisational leaders, thereby mitigating potential biases and ensuring ethical use. It underscores the importance of organisational commitment to transparency practices beyond mere regulatory compliance, including fostering a culture of accountability, prioritising clear data policies and maintaining active engagement with stakeholders. By adopting a holistic approach to transparency and explainability, businesses can cultivate trust in AI technologies, bridging the gap between technological innovation and consumer acceptance, and paving the way for more ethical and effective AI-powered customer engagements. KEYWORDS: artificial intelligence (AI), transparency

As ML is still a (relatively) recent field of study, especially outside the realm of abstract mathematics, few works have been led on the political aspect of LLMs, and more particularly about the alignment process, and its political dimension. This process can be as simple as prompt engineering, but also very deep and affect completely unrelated questions. For example, politically directed alignment has a very strong impact on an LLM's embedding space, and the relative position of political notions in such a space. Using special tools to evaluate general political bias and analyze the effects of alignment, we can gather new data to understand its causes and possible consequences on society. Indeed, leading a socio-political approach we can hypothesize that most big LLMs are aligned on what Marxist philosophy calls the 'dominant ideology'. As AI's role in political decision-making, at the citizen's scale but also in government agencies, such biases can have huge effects on societal change, either by creating a new and insidious pathway for societal uniformization or by allowing disguised extremist views to gain traction on the people.

Despite its significant benefits in enhancing the transparency and trustworthiness of artificial intelligence (AI) systems, explainable AI (XAI) has yet to reach its full potential in real-world applications. One key challenge is that XAI can unintentionally provide adversaries with insights into black-box models, inevitably increasing their vulnerability to various attacks. In this paper, we develop a novel explanation-driven adversarial attack against black-box classifiers based on feature substitution, called XSub. The key idea of XSub is to strategically replace important features (identified via XAI) in the original sample with corresponding important features from a "golden sample" of a different label, thereby increasing the likelihood of the model misclassifying the perturbed sample. The degree of feature substitution is adjustable, allowing us to control how much of the original samples information is replaced. This flexibility effectively balances a trade-off between the attacks effectiveness and its stealthiness. XSub is also highly cost-effective in that the number of required queries to the prediction model and the explanation model in conducting the attack is in O(1). In addition, XSub can be easily extended to launch backdoor attacks in case the attacker has access to the models training data. Our evaluation demonstrates that XSub is not only effective and stealthy but also cost-effective, enabling its application across a wide range of AI models.

Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration type, expand several organizations (for example, in e-Government), adding complexity and various challenges both for their implementation and for their discovery, prediction, and analysis of their execution. Predictive process monitoring is based on exploiting execution data from past instances to predict the execution of current cases. It is possible to make predictions on the next activity and remaining time, among others, to anticipate possible deviations, violations, and delays in the processes to take preventive measures (e.g., re-allocation of resources). In this work, we propose an extension for collaborative processes of traditional process prediction, considering particularities of this type of process, which add information of interest in this context, for example, the next activity of which participant or the following message to be exchanged between two participants.