As artificial intelligence continues to advance and becomes more integrated into sensitive areas like healthcare, education, and everyday life, it's crucial for these systems to be both resilient and robust. This paper shows how resilience is a fundamental characteristic of social robots, which, through it, ensure trust in the robot itself--an essential element especially when operating in contexts with elderly people, who often have low trust in these systems. Resilience is therefore the ability to operate under adverse or stressful conditions, even when degraded or weakened, while maintaining essential operational capabilities.

Artificial intelligence systems could account for nearly half of datacentre power consumption by the end of this year, analysis has revealed. The estimates by Alex de Vries-Gao, the founder of the Digiconomist tech sustainability website, came as the International Energy Agency forecast that AI would require almost as much energy by the end of this decade as Japan uses today. De Vries-Gao's calculations, to be published in the sustainable energy journal Joule, are based on the power consumed by chips made by Nvidia and Advanced Micro Devices that are used to train and operate AI models. The paper also takes into account the energy consumption of chips used by other companies, such as Broadcom. The IEA estimates that all data centres – excluding mining for cryptocurrencies – consumed 415 terawatt hours (TWh) of electricity last year.

Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology

UPenn Wharton School Associate Professor Ethan Mollick weighs in on the Biden White House's new guidelines for artificial intelligence in the workplace on'Fox News Live.' Artificial intelligence systems like ChatGPT could soon run out of what keeps making them smarter -- the tens of trillions of words people have written and shared online. A new study released Thursday by research group Epoch AI projects that tech companies will exhaust the supply of publicly available training data for AI language models by roughly the turn of the decade -- sometime between 2026 and 2032. Comparing it to a "literal gold rush" that depletes finite natural resources, Tamay Besiroglu, an author of the study, said the AI field might face challenges in maintaining its current pace of progress once it drains the reserves of human-generated writing. In the short term, tech companies like ChatGPT-maker OpenAI and Google are racing to secure and sometimes pay for high-quality data sources to train their AI large language models – for instance, by signing deals to tap into the steady flow of sentences coming out of Reddit forums and news media outlets. In the longer term, there won't be enough new blogs, news articles and social media commentary to sustain the current trajectory of AI development, putting pressure on companies to tap into sensitive data now considered private -- such as emails or text messages -- or relying on less-reliable "synthetic data" spit out by the chatbots themselves.

The Financial Times has struck a deal with ChatGPT developer OpenAI that allows its content to be used in training artificial intelligence systems. The FT will receive an undisclosed payment as part of the deal, which is the latest to be agreed between OpenAI and news publishers. Under the arrangement, ChatGPT users will receive summaries and quotes from FT journalism, as well as links to articles, in responses to prompts, where appropriate. John Ridding, the chief executive of the FT Group, said it was "right" that AI companies paid publishers for their material. The New York Times is suing OpenAI and its largest investor, Microsoft, over use of its content to train large language models, the technology that underpins chatbots like ChatGPT.

We live in frightening times. Wars in Ukraine and Gaza could widen, the polar ice caps are melting, and even some scientists developing artificial intelligence systems are worried about unleashing a monster. But those fears all pale in comparison to what really gives Americans the jitters. The calamity that worries them above all else is a total economic collapse in the US. The Pentagon's four legged robot dogs may offer a glimpse of what killer machines will look like An economic meltdown is the top fear for a third of respondents.

The White House revealed it is looking into reports the Israeli army has been using an AI system to populate its'kill list' of alleged Hamas terrorists, hours after President Joe Biden's call with Benjamin Netanyahu. The report cited six Israeli intelligence officers, who admitted to using an AI called'Lavender' to classify as many as 37,000 Palestinians as suspected militants -- marking these people and their homes as acceptable targets for air strikes. White House national security spokesperson John Kirby told CNN on Thursday that the reports had not been verified, but they were investigating. Israel has vehemently denied the AI's role with an army spokesperson describing the system as'auxiliary tools that assist officers in the process of incrimination.' However, during the call Biden reportedly threatened that he would condition the US' support for the attack in Gaza if the Israeli government didn't protect civilians and aid workers from offensive assaults.

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems (as opposed to other technologies) have ubiquitously diffused in our life not only as some beneficial tools to be used by human agents but also are going to be substitutive agents on our behalf, or manipulative minds that would influence human thought, decision, and agency. Trust/distrust in AI plays the role of a regulator and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI.

On March 13, 2024, the European Parliament approved the final version of the European Artificial Intelligence Act (AI Act), and its publication in the Official Journal of the European Union is awaited. The AI Act is a long text comprising 180 recitals, XIII chapters with 113 articles, and XIII annexes. It is an essential legal framework for AI and the first comprehensive legislation on AI.

Artificial intelligence has stoked fears and security concerns for years but a new global resolution to safeguard the public may provide much-needed reassurance. The United Nations General Assembly approved a resolution to shield personal data, monitor AI for potential risks including scams, and protect human rights. The resolution - proposed by the US - is non-binding but was agreed upon by all 193 UN member nations and co-sponsored by non-members from 123 countries, including China and Russia. The resolution comes as high-profile tech moguls have expressed concerns about AI's reliability, including Geoffrey Hinton - the'Godfather of AI' - who said last year that he regretted creating the technology and worries that machines could take over. All 193 UN members signed an AI resolution to protect people's data and human rights The White House called the resolution a'historic step' and Vice President Kamala Harris said she and President Joe Biden are committed to establishing AI safeguards The White House praised the resolution, calling it a'historic step' to ensuring'trustworthy' advancements in AI.