Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network traffic, the centralization of massive data in a single data center raises issues regarding confidentiality, privacy and data transfer demands. To address these challenges, federated learning (FL) emerges as an appealing ML training framework which offers high accurate predictions through parallel distributed computations. However, the environmental impact of these methods is often overlooked, which calls into question their sustainability. In this paper, we address the trade-off between accuracy and energy consumption in FL by proposing a novel sustainability indicator that allows assessing the feasibility of ML models. Then, we comprehensively evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint, which make them impractical for real-world applications.

The Directorate General for Parliamentary Research Services of the European Parliament has prepared a report to the Members of the European Parliament where they enumerate seven main risks of Artificial Intelligence (AI) in medicine and healthcare: patient harm due to AI errors, misuse of medical AI tools, bias in AI and the perpetuation of existing inequities, lack of transparency, privacy and security issues, gaps in accountability, and obstacles in implementation. In this study, we propose fourteen functional requirements that AI systems may implement to reduce the risks associated with their medical purpose: AI passport, User management, Regulation check, Academic use only disclaimer, data quality assessment, Clinicians double check, Continuous performance evaluation, Audit trail, Continuous usability test, Review of retrospective/simulated cases, Bias check, eXplainable AI, Encryption and use of field-tested libraries, and Semantic interoperability. Our intention here is to provide specific high-level specifications of technical solutions to ensure continuous good performance and use of AI systems to benefit patients in compliance with the future EU regulatory framework.

A moderately detailed consideration of interactive LLMs as cognitive systems is given, focusing on LLMs circa mid-2023 such as ChatGPT, GPT-4, Bard, Llama, etc.. Cognitive strengths of these systems are reviewed, and then careful attention is paid to the substantial differences between the sort of cognitive system these LLMs are, and the sort of cognitive systems human beings are. It is found that many of the practical weaknesses of these AI systems can be tied specifically to lacks in the basic cognitive architectures according to which these systems are built. It is argued that incremental improvement of such LLMs is not a viable approach to working toward human-level AGI, in practical terms given realizable amounts of compute resources. This does not imply there is nothing to learn about human-level AGI from studying and experimenting with LLMs, nor that LLMs cannot form significant parts of human-level AGI architectures that also incorporate other ideas. Social and ethical matters regarding LLMs are very briefly touched from this perspective, which implies that while care should be taken regarding misinformation and other issues, and economic upheavals will need their own social remedies based on their unpredictable course as with any powerfully impactful technology, overall the sort of policy needed as regards modern LLMs is quite different than would be the case if a more credible approximation to human-level AGI were at hand.

Unsupported and unfalsifiable claims we encounter in our daily lives can influence our view of the world. Characterizing, summarizing, and -- more generally -- making sense of such claims, however, can be challenging. In this work, we focus on fine-grained debate topics and formulate a new task of distilling, from such claims, a countable set of narratives. We present a crowdsourced dataset of 12 controversial topics, comprising more than 120k arguments, claims, and comments from heterogeneous sources, each annotated with a narrative label. We further investigate how large language models (LLMs) can be used to synthesise claims using In-Context Learning. We find that generated claims with supported evidence can be used to improve the performance of narrative classification models and, additionally, that the same model can infer the stance and aspect using a few training examples. Such a model can be useful in applications which rely on narratives , e.g. fact-checking.

Text summarization has a wide range of applications in many scenarios. The evaluation of the quality of the generated text is a complex problem. A big challenge to language evaluation is that there is a clear divergence between existing metrics and human evaluation. A document summary's quality can be assessed by human annotators on various criteria, both objective ones like grammar and correctness, and subjective ones like informativeness, succinctness, and appeal. Most of the automatic evaluation methods like BLUE/ROUGE may be not able to adequately capture the above dimensions. In this paper, we propose a new evaluation framework based on LLMs, which provides a comprehensive evaluation framework by comparing generated text and reference text from both objective and subjective aspects. First, we propose to model objective and subjective dimensions of generated text based on roleplayers prompting mechanism. Furthermore, we introduce a context-based prompting mechanism that is able to generate dynamic roleplayer profiles based on input context. Finally, we design a multi-roleplayer prompting technology based on batch prompting and integrate multiple outputs into the final evaluation results. Experimental results on three real datasets for summarization show that our model is highly competitive and has a very high consistency with human annotators.

Prime Minister Benjamin Netanyahu is starting a US trip in California to talk about technology and artificial intelligence with billionaire businessman Elon Musk. The Israeli leader posted Monday on Musk's social media platform X, formerly known as Twitter, that he plans to talk with the Tesla CEO "about how we can harness the opportunities and mitigate the risks of AI for the good of civilization." Netanyahu's high-profile visit to the San Francisco Bay Area comes at a time when Musk is facing accusations of tolerating anti-Semitic messages on his social media platform, while Netanyahu is confronting political opposition at home and abroad. Protesters gathered early Monday outside the Fremont, California factory where Tesla makes its cars. The video livestream kicked off shortly before 9:30am with Netanyahu and the Tesla CEO.

For a while now, Washington has been wrestling with two big forces shaping technology: social media and artificial intelligence. Who should do it--and how? Currently, Congress is considering a bill that would regulate how social media companies treat minors: the Kids Online Safety Act. Although it has bipartisan support, KOSA is not without controversy. Several critics have called it "government censorship." One group, the Electronic Frontier Foundation, says it is "one of the most dangerous bills in years."

People should not assume a positive outcome from the artificial intelligence boom, the UK's competition watchdog has warned, citing risks including a proliferation of false information, fraud and fake reviews as well as high prices for using the technology. The Competition and Markets Authority said people and businesses could benefit from a new generation of AI systems but dominance by entrenched players and flouting of consumer protection law posed a number of potential threats. The CMA made the warning in an initial review of foundation models, the technology that underpins AI tools such as the ChatGPT chatbot and image generators such as Stable Diffusion. The emergence of ChatGPT in particular has triggered a debate over the impact of generative AI – a catch-all term for tools that produce convincing text, image and voice outputs from typed human prompts – on the economy by eliminating white-collar jobs in areas such as law, IT and the media, as well as the potential for mass-producing disinformation targeting voters and consumers. The CMA chief executive, Sarah Cardell, said the speed at which AI was becoming a part of everyday life for people and businesses was "dramatic", with the potential for making millions of everyday tasks easier as well as boosting productivity – a measure of economic efficiency, or the amount of output generated by a worker for each hour worked.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Australia's government said Monday the online dating industry must improve safety standards or be forced to make changes through legislation, responding to research that says three-in-four Australian users suffer some form of sexual violence through the platforms. Communications Minister Michelle Rowland said popular dating companies such as Tinder, Bumble and Hinge have until June 30 to develop a voluntary code of conduct that addresses user safety concerns. The code could include improving engagement with law enforcement, supporting at-risk users, improving safety policies and practices, and providing greater transparency about harms, she said.

DeepMind cofounder Mustafa Suleyman wants to build a chatbot that does a whole lot more than chat. In a recent conversation he had with our senior AI editor Will Douglas Heaven, he explained his view that generative AI is just a phase. What's next, he says, is interactive AI: bots that can carry out tasks you set for them by calling on other software and people to get stuff done. He also calls for robust regulation--and doesn't think that'll be hard to achieve. While many will scoff at Suleyman's brand of techno-optimism--even naïveté--he is not the only one talking up a future filled with ever more autonomous software. And, unlike most people, he has a new billion-dollar company, Inflection, with a roster of top-tier talent.