While training fair machine learning models has been studied extensively in recent years, most developed methods rely on the assumption that the training and test data have similar distributions. In the presence of distribution shifts, fair models may behave unfairly on test data. There have been some developments for fair learning robust to distribution shifts to address this shortcoming. However, most proposed solutions are based on the assumption of having access to the causal graph describing the interaction of different features. Moreover, existing algorithms require full access to data and cannot be used when small batches are used (stochastic/batch implementation). This paper proposes the first stochastic distributionally robust fairness framework with convergence guarantees that do not require knowledge of the causal graph. More specifically, we formulate the fair inference in the presence of the distribution shift as a distributionally robust optimization problem under $L_p$ norm uncertainty sets with respect to the Exponential Renyi Mutual Information (ERMI) as the measure of fairness violation. We then discuss how the proposed method can be implemented in a stochastic fashion. We have evaluated the presented framework's performance and efficiency through extensive experiments on real datasets consisting of distribution shifts.

The Chan Zuckerberg Initiative (CZI), the philanthropic organization created in 2015 by Priscilla Chan and her husband Mark Zuckerberg, announced a bold new generative AI initiative today. The group is funding and building a high-end GPU cluster that will use AI to create predictive models of healthy and diseased cells; it hopes they'll help researchers better understand the human body's cells and cellular reactions. The group believes the collection of computers will help it achieve its incredibly lofty goal of helping to "cure, prevent, or manage all diseases by the end of this century." "Researchers are gathering more data than ever before about the trillions of cells within our bodies, and it's too complex for our brains to grapple with," Jeff MacGregor, CZI vice president of communications, wrote in an emailed statement to Engadget. He lists an example of imaging one cell at nanometer resolution, which would use the same amount of data as 83,000 photos on a smartphone.

Soon after China's artificial intelligence rules came into effect last month, a series of new AI chatbots began trickling onto the market, with government approval. The rules have already been watered down from what was initially proposed, and so far, China hasn't enforced them as strictly as it could, experts say. China's regulatory approach will likely have huge implications for the technological competition between the country and its AI superpower rival the U.S. The Cyberspace Administration of China's (CAC) Generative AI Measures, which came into effect on Aug. 15, are some of the strictest in the world. They state that the generative AI services should not generate content "inciting subversion of national sovereignty or the overturn of the socialist system," or "advocating terrorism or extremism, promoting ethnic hatred and ethnic discrimination, violence and obscenity, as well as fake and harmful information." Preventing AI chatbots from spewing out unwanted or even toxic content has been a challenge for AI developers around the world.

Alison Lomax's presence on the video streaming platform she runs is relatively scant compared with the YouTubers with whom she spends much of her time. But what clips exist succinctly chart the marketing tech revolution she's been navigating: there's a badly framed 12 minutes from 2014 of Lomax lecturing on the rise of influencers working with brands; in another she describes how TV companies woke up to the potential of partnering with YouTube in 2016; and there's her on stage at London's podcast show this year, discussing YouTube's imminent relaunch into the booming audio format. Now, Lomax stands at the "inflection point" of the next hot technology: the generative artificial intelligence behind chatbots such as ChatGPT and image generators such as MidJourney. YouTube, launched in 2005, is no stranger to AI: it is used in its recommendation algorithm; to moderate content; and, latterly, for automatic language translation. "We're committed to embracing AI in a bold way," says Lomax. "But we have to do it really responsibly."

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. An internet watchdog is sounding the alarm over the growing trend of sex offenders collaborating online to use open source artificial intelligence to generate child sexual abuse material. "There's a technical community within the offender space, particularly dark web forums, where they are discussing this technology," Dan Sexton, the chief technology officer at the Internet Watch Foundation (IWF), told The Guardian in a report last week. "They are sharing imagery, they're sharing [AI] models. Sexton's organization has found that offenders are increasingly turning to open source AI models to create illegal child sexual abuse material (CSAM) and distribute it online. Unlike closed AI models such as OpenAI's Dall-E or Google's Imagen, open source AI technology can be downloaded and adjusted by users, according to the report. Sexton said the ability to use such technology has spread among offenders, who take to the dark web to create and distribute realistic images. An internet watchdog is sounding the alarm over the growing trend of sex offenders collaborating online to use open source artificial intelligence to generate child sexual abuse material. "The content that we've seen, we believe is actually being generated using open source software, which has been downloaded and run locally on people's computers and then modified.

In this paper we consider the setting where machine learning models are retrained on updated datasets in order to incorporate the most up-to-date information or reflect distribution shifts. We investigate whether one can infer information about these updates in the training data (e.g., changes to attribute values of records). Here, the adversary has access to snapshots of the machine learning model before and after the change in the dataset occurs. Contrary to the existing literature, we assume that an attribute of a single or multiple training data points are changed rather than entire data records are removed or added. We propose attacks based on the difference in the prediction confidence of the original model and the updated model. We evaluate our attack methods on two public datasets along with multi-layer perceptron and logistic regression models. We validate that two snapshots of the model can result in higher information leakage in comparison to having access to only the updated model. Moreover, we observe that data records with rare values are more vulnerable to attacks, which points to the disparate vulnerability of privacy attacks in the update setting. When multiple records with the same original attribute value are updated to the same new value (i.e., repeated changes), the attacker is more likely to correctly guess the updated values since repeated changes leave a larger footprint on the trained model. These observations point to vulnerability of machine learning models to attribute inference attacks in the update setting.

For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are artificial entities that sense their environment, make decisions, and take actions. Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks. Actually, what the community lacks is a general and powerful model to serve as a starting point for designing AI agents that can adapt to diverse scenarios. Due to the versatile capabilities they demonstrate, large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI), offering hope for building general AI agents. Many researchers have leveraged LLMs as the foundation to build AI agents and have achieved significant progress. In this paper, we perform a comprehensive survey on LLM-based agents. We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. Building upon this, we present a general framework for LLM-based agents, comprising three main components: brain, perception, and action, and the framework can be tailored for different applications. Subsequently, we explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation. Following this, we delve into agent societies, exploring the behavior and personality of LLM-based agents, the social phenomena that emerge from an agent society, and the insights they offer for human society. Finally, we discuss several key topics and open problems within the field. A repository for the related papers at https://github.com/WooooDyy/LLM-Agent-Paper-List.

Machine learning methods are increasingly adopted in communications problems, particularly those arising in next generation wireless settings. Though seen as a key climate mitigation and societal adaptation enabler, communications related energy consumption is high and is expected to grow in future networks in spite of anticipated efficiency gains in 6G due to exponential communications traffic growth. To make meaningful climate mitigation impact in the communications sector, a mindset shift away from maximizing throughput at all cost and towards prioritizing energy efficiency is needed. Moreover, this must be adopted in both existing (without incurring further embodied carbon costs through equipment replacement) and future network infrastructure, given the long development time of mobile generations. To that end, we present summaries of two such problems, from both current and next generation network specifications, where probabilistic inference methods were used to great effect: using Bayesian parameter tuning we are able to safely reduce the energy consumption of existing hardware on a live communications network by $11\%$ whilst maintaining operator specified performance envelopes; through spatiotemporal Gaussian process surrogate modeling we reduce the overhead in a next generation hybrid beamforming system by over $60\%$, greatly improving the networks' ability to target highly mobile users such as autonomous vehicles. The Bayesian paradigm is itself helpful in terms of energy usage, since training a Bayesian optimization model can require much less computation than, say, training a deep neural network.

It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model.

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.