Multiwinner voting captures a wide variety of settings, from parliamentary elections in democratic systems to product placement in online shopping platforms. There is a large body of work dealing with axiomatic characterizations, computational complexity, and algorithmic analysis of multiwinner voting rules. Although many challenges remain, significant progress has been made in showing existence of fair and representative outcomes as well as efficient algorithmic solutions for many commonly studied settings. However, much of this work focuses on single-shot elections, even though in numerous real-world settings elections are held periodically and repeatedly. Hence, it is imperative to extend the study of multiwinner voting to temporal settings. Recently, there have been several efforts to address this challenge. However, these works are difficult to compare, as they model multi-period voting in very different ways. We propose a unified framework for studying temporal fairness in this domain, drawing connections with various existing bodies of work, and consolidating them within a general framework. We also identify gaps in existing literature, outline multiple opportunities for future work, and put forward a vision for the future of multiwinner voting in temporal settings.

Predictions of weather hazard require expensive km-scale simulations driven by coarser global inputs. Here, a cost-effective stochastic downscaling model is trained from a high-resolution 2-km weather model over Taiwan conditioned on 25-km ERA5 reanalysis. To address the multi-scale machine learning challenges of weather data, we employ a two-step approach Corrector Diffusion (\textit{CorrDiff}), where a UNet prediction of the mean is corrected by a diffusion step. Akin to Reynolds decomposition in fluid dynamics, this isolates generative learning to the stochastic scales. \textit{CorrDiff} exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes. Case studies of coherent weather phenomena reveal appropriate multivariate relationships reminiscent of learnt physics: the collocation of intense rainfall and sharp gradients in fronts and extreme winds and rainfall bands near the eyewall of typhoons. Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces unique communication security challenges that have yet to be thoroughly addressed in the literature. These challenges primarily originate from the decentralized nature of the aggregation process, the varied roles and responsibilities of the participants, and the absence of a central authority to oversee and mitigate threats. Addressing these challenges, this paper first delineates a comprehensive threat model focused on DFL communications. In response to these identified risks, this work introduces a security module to counter communication-based attacks for DFL platforms. The module combines security techniques such as symmetric and asymmetric encryption with Moving Target Defense (MTD) techniques, including random neighbor selection and IP/port switching. The security module is implemented in a DFL platform, Fedstellar, allowing the deployment and monitoring of the federation. A DFL scenario with physical and virtual deployments have been executed, encompassing three security configurations: (i) a baseline without security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques. The effectiveness of the security module is validated through experiments with the MNIST dataset and eclipse attacks. The results showed an average F1 score of 95%, with the most secure configuration resulting in CPU usage peaking at 68% (+-9%) in virtual deployments and network traffic reaching 480.8 MB (+-18 MB), effectively mitigating risks associated with eavesdropping or eclipse attacks.

We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on Mind2Web, we conduct an initial exploration of using large language models (LLMs) for building generalist web agents. While the raw HTML of real-world websites are often too large to be fed to LLMs, we show that first filtering it with a small LM significantly improves the effectiveness and efficiency of LLMs. Our solution demonstrates a decent level of performance, even on websites or entire domains the model has never seen before, but there is still a substantial room to improve towards truly generalizable agents. We open-source our dataset, model implementation, and trained models (https://osu-nlp-group.github.io/Mind2Web) to facilitate further research on building a generalist agent for the web.

After years of inaction in the U.S. Congress, E.U. tech laws have had wide-ranging implications for Silicon Valley companies. Europe's digital privacy law, the General Data Protection Regulation, has prompted some companies, such as Microsoft, to overhaul how they handle users' data even beyond Europe's borders. Meta, Google and other companies have faced fines under the law, and Google had to delay the launch of its generative AI chatbot Bard in the region due to a review under the law. However, there are concerns that the law created costly compliance measures that have hampered small businesses, and that lengthy investigations and relatively small fines have blunted its efficacy among the world's largest companies.

The Washington Post reports that after a marathon 72-hour debate European Union legislators Friday have reached a historic deal on a broad-ranging AI safety development bill, the most expansive and far-reaching of its kind to date. Details of the deal itself were not immediately available. The proposed regulations would dictate the ways in which future machine learning models can be developed and distributed within the trade bloc, impacting its use in applications ranging from education to employment to healthcare. AI development would be split among four categories, depending on how much societal risk each potentially poses -- minimal, limited, high, and banned. Banned uses would include anything that circumvents the user's will, targets protected groups or provides real-time biometric tracking (like facial recognition).

The European Union today agreed on the details of the AI Act, a far-reaching set of rules for the people building and using artificial intelligence. It's a milestone law that, lawmakers hope, will create a blueprint for the rest of the world. After months of debate about how to regulate companies like OpenAI, lawmakers from the EU's three branches of government--the Parliament, Council and Commission--spent more than 36 hours in total--thrashing out the new legislation between Wednesday afternoon and Friday evening. Lawmakers were under pressure to strike a deal before the EU election campaign starts in the new year. "The EU AI Act is a global first," said European Commission President Ursula von der Leyen on X. "[It is] a unique legal framework for the development of AI you can trust.

AI-enabled military systems have been effective in battle, but some reliability issues still concern troops and their commanders: former Air Force test pilot. Cheap drones equipped with AI can destroy expensive military equipment, and the Pentagon will need to incorporate autonomous technology into its strategy to advance into the next generation of warfare, a former test pilot and military tech company executive told Fox News. "What we've seen in Europe and other theaters is that they've democratized warfare," said EpiSci Vice President of Tactical Autonomous Systems Chris Gentile. "A $1,000 drone can take out a multimillion-dollar asset." The Pentagon has a portfolio of over 800 contracts for AI-enabled projects.

OpenAI's recent drama hasn't only caught UK regulators' attention. Bloomberg reported Friday that the Federal Trade Commission (FTC) is looking into Microsoft's investment in the Sam Altman-led company and whether it violates US antitrust laws. FTC Chair Lina Khan wrote in a New York Times op-ed earlier this year that "the expanding adoption of AI risks further locking in the market dominance of large incumbent technology firms." Bloomberg's report stresses that the FTC inquiry is preliminary, and the agency hasn't opened a formal investigation. But Khan and company are reportedly "analyzing the situation and assessing what its options are." One complicating factor for regulation is that OpenAI is a non-profit, and transactions involving non-corporate entities aren't required by law to be reported.

Tens of millions of people are using AI-powered'nudify' apps, according to a new analysis that shows the dark side of the technology. More than 24 million people visited nudity AI websites in September, which digitally alter images, primarily women, to make them appear naked in the photo using deep-learning algorithms. These algorithms are trained on existing images of women which allows it to overlay realistic images of nude body parts, regardless of whether the photographed person is clothed. Spam ads across major platforms are also directing people to the sites and apps increased by more than 2,000 percent since the beginning of 2023. The rise in nudity-promoted apps is particularly prevalent on social media, including Google's YouTube, Reddit, and X - and 52 Telegram groups were also found to be used to access non-consensual intimate imagery (NCII) services.