In this short consensus paper, we outline risks from upcoming, advanced AI systems. We examine large-scale social harms and malicious uses, as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, we propose urgent priorities for AI R&D and governance.

Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx of approaches have delivered towards different realistic challenges. In this survey, we provide a systematic overview of the important and recent developments of research on federated learning. Firstly, we introduce the study history and terminology definition of this area. Then, we comprehensively review three basic lines of research: generalization, robustness, and fairness, by introducing their respective background concepts, task settings, and main challenges. We also offer a detailed overview of representative literature on both methods and datasets. We further benchmark the reviewed methods on several well-known datasets. Finally, we point out several open issues in this field and suggest opportunities for further research. We also provide a public website to continuously track developments in this fast advancing field: https://github.com/WenkeHuang/MarsFL.

Large language models (LLMs) have been shown to possess impressive capabilities, while also raising crucial concerns about the faithfulness of their responses. A primary issue arising in this context is the management of (un)answerable queries by LLMs, which often results in hallucinatory behavior due to overconfidence. In this paper, we explore the behavior of LLMs when presented with (un)answerable queries. We ask: do models represent the fact that the question is (un)answerable when generating a hallucinatory answer? Our results show strong indications that such models encode the answerability of an input query, with the representation of the first decoded token often being a strong indicator. These findings shed new light on the spatial organization within the latent representations of LLMs, unveiling previously unexplored facets of these models. Moreover, they pave the way for the development of improved decoding techniques with better adherence to factual generation, particularly in scenarios where query (un)answerability is a concern.

Examples of bias and discrimination in AI applications include court decisions [1], job hiring [2], online ads [3], and many other areas prone to bias [4]. These algorithmic decisions have economic and personal implications for individuals. Therefore, Fairness, Accountability, Transparency and Ethics (FATE) in AI must be properly regulated for responsible use cases [5, 6], particularly in high-stakes domains [1, 7, 8, 9, 10, 11, 12]. Studies have shown that machine learning models can discriminate based on race and gender [13, 14, 15]. FATE in AI is intended to address the social issues caused by digital systems, but the current discourse is largely shaped by more economically developed countries (MEDC), raising concerns about neglecting local knowledge, cultural pluralism, and global fairness [16]. As AI systems become more integrated into various products [9, 10, 17, 12, 18, 19], they are a major driver of the fourth industrial revolution (4IR) and transformation [20]. Therefore, it is essential to understand the FATE-related needs of different communities, as AI affects a wide range of people. Ensuring effective transparency cannot be a one-size-fits-all approach [21], as this could disproportionately affect different communities [16, 22]. To this end, more contextualised and interdisciplinary research is needed to inform algorithmic fairness and transparency [23, 24, 25].

The arrival of handwriting recognition technologies offers new possibilities for research in heritage studies. However, it is now necessary to reflect on the experiences and the practices developed by research teams. Our use of the Transkribus platform since 2018 has led us to search for the most significant ways to improve the performance of our handwritten text recognition (HTR) models which are made to transcribe French handwriting dating from the 17th century. This article therefore reports on the impacts of creating transcribing protocols, using the language model at full scale and determining the best way to use base models in order to help increase the performance of HTR models. Combining all of these elements can indeed increase the performance of a single model by more than 20% (reaching a Character Error Rate below 5%). This article also discusses some challenges regarding the collaborative nature of HTR platforms such as Transkribus and the way researchers can share their data generated in the process of creating or training handwritten text recognition models.

Fox News Flash top headlines are here. Check out whats clicking on Foxnews.com. A child psychiatrist in Charlotte, N.C., has been sentenced to 40 years in prison for using artificial intelligence (AI) to create child pornography and secretly recording his 15-year-old cousin as she showered, according to the U.S. Attorney's Office in the Western District of North Carolina. David Tatum, 41, created the AI images by modifying pictures of ex-girlfriends with sexually explicit images of minors which he had obtained online. Tatum digitally altered images from a school dance and a photo commemorating the first day of school to make them sexually explicit, prosecutors said.

In the occupied Palestinian territories, there are cameras everywhere. In Silwan, in occupied East Jerusalem, residents say cameras were installed by Israeli police up and down their streets, peering into their homes. One resident named Sara said she and her family "could be detected as if the cameras were just in our house … we couldn't feel at home in our own house and had to be fully dressed all the time." Surveillance cameras now cover the Damascus Gate, the main entrance into the old city of Jerusalem and one of the only public areas for Palestinians to gather socially and hold demonstrations. It's at that gate that "Palestinians are being watched and assessed at all times", according to an Amnesty International report, Automated Apartheid.

NVIDIA really, really doesn't want to lose access to China's massive AI chip market. The company is developing three new AI chips especially for China that don't run afoul of the latest export restrictions in the US, according to The Wall Street Journal and Reuters. Last year, the US government notified the chipmaker that it would restrict the export of computer chips meant for supercomputers and artificial intelligence applications to Russia and China due to concerns that the components could be used for military purposes. That rule prevented NVIDIA from selling certain A100 and H100 chips in the country, so it designed the A800 and H800 chips specifically for the Chinese market. However, the US government recently issued an updated set of restrictions that puts a limit on how much computing power a chip can have when it's meant for export to the aforementioned countries.

ChatGPT seems to have blindsided us all. In less than a year it has proved that it can make writers redundant, which is one of the reasons why the Writers Guild of America recently went on strike, and why a group of novelists, including Jonathan Franzen, Jodi Picoult and George RR Martin, are pursuing a lawsuit against OpenAI, the company that owns the chatbot. Imitation that appears to be original writing. From my experiments, it's obvious that ChatGPT's current level of literary sophistication is weak – it is cliche-prone and generally unconvincing – but who knows how it will develop? Writers like stretching our imaginations, coming up with ideas, working out storylines and plots, creating believable characters, overcoming creative challenges and working on a full-length piece of work over an extended period of time. Most of us write our books ourselves and while we are influenced by other writers, we're not a chatbot that has been trained on hundreds of thousands of novels for the sole purpose of mimicking human creativity. Imagine a future where those who are most adept at getting AI to write creatively will dominate, while we writers who spend a lifetime devoted to our craft are sidelined. OK, this is a worst‑case scenario, but we have to consider it, because ChatGPT and the other Large Language Models (LLMs) out there have been programmed to imagine a future that threatens many creative professions. ChatGPT is already responding to the questions I ask it in seconds, quite reliably. It is an impressive beast, but one that needs to be tamed.

Initially, those scholars employ these historical elements to forecast whether the criminal would re-offend. Subsequently, the binary outcome of recidivism serves as a proxy variable for recidivism risk. Some computer scientists also employ the probability (or score) assigned by the model for an offender's likelihood of re-offense as a gauge for their recidivism risk (Etzler et al., 2023; Ma et al., 2022; Wang et al., 2022). While such configurations may seem intuitively compelling, they often embody an oversimplified and deterministic viewpoint, which stands in contradiction to contemporary social science theories. Firstly, historical factors alone are insufficient predictors of human actions.