Despite their remarkable successes, state-of-the-art large language models (LLMs), including vision-and-language models (VLMs) and unimodal language models (ULMs), fail to understand precise semantics. For example, semantically equivalent sentences expressed using different lexical compositions elicit diverging representations. The degree of this divergence and its impact on encoded semantics is not very well understood.

Vision-language models (VLMs) have made significant progress in recent visualquestion-answering (VQA) benchmarks that evaluate complex visio-linguistic reasoning. However, are these models truly effective? In this work, we show that VLMs still struggle with natural images and questions that humans can easily answer, which we term natural adversarial samples. We also find it surprisingly easy to generate these VQA samples from natural image-text corpora using offthe-shelf models like CLIP and ChatGPT. We propose a semi-automated approach to collect a new benchmark, NaturalBench, for reliably evaluating VLMs with 10,000 human-verified VQA samples.

Gaze following and social gaze prediction are fundamental tasks providing insights into human communication behaviors, intent, and social interactions. Most previous approaches addressed these tasks separately, either by designing highly specialized social gaze models that do not generalize to other social gaze tasks or by considering social gaze inference as an ad-hoc post-processing of the gaze following task. Furthermore, the vast majority of gaze following approaches have proposed models that can handle only one person at a time and are static, therefore failing to take advantage of social interactions and temporal dynamics. In this paper, we address these limitations and introduce a novel framework to jointly predict the gaze target and social gaze label for all people in the scene. It comprises (i) a temporal, transformer-based architecture that, in addition to frame tokens, handles personspecific tokens capturing the gaze information related to each individual; (ii) a new dataset, VSGaze, built from multiple gaze following and social gaze datasets by extending and validating head detections and tracks, and unifying annotation types. We demonstrate that our model can address and benefit from training on all tasks jointly, achieving state-of-the-art results for multi-person gaze following and social gaze prediction. Our annotations and code will be made publicly available.

Each of our estimators is based on a simple, general approach to designing differentially private mechanisms, but with novel technical steps to make the estimator private and sample-efficient. Our first estimator samples a point with approximately maximum Tukey depth using the exponential mechanism, but restricted to the set of points of large Tukey depth. Proving that this mechanism is private requires a novel analysis. Our second estimator perturbs the empirical mean of the data set with noise calibrated to the empirical covariance, without releasing the covariance itself. Its sample complexity guarantees hold more generally for subgaussian distributions, albeit with a slightly worse dependence on the privacy parameter. For both estimators, careful preprocessing of the data is required to satisfy differential privacy.

Artificial intelligence can do just as well as humans, if not better, when it comes to persuading others in a debate, and not just because it cannot shout, a study has found. Experts say the results are concerning, not least as it has potential implications for election integrity. "If persuasive AI can be deployed at scale, you can imagine armies of bots microtargeting undecided voters, subtly nudging them with tailored political narratives that feel authentic," said Francesco Salvi, the first author of the research from the Swiss Federal Institute of Technology in Lausanne. He added that such influence was hard to trace, even harder to regulate and nearly impossible to debunk in real time. "I would be surprised if malicious actors hadn't already started to use these tools to their advantage to spread misinformation and unfair propaganda," Salvi said.

This paper provides a theoretical study of deep neural function approximation in reinforcement learning (RL) with the ϵ-greedy exploration under the online setting. This problem setting is motivated by the successful deep Q-networks (DQN) framework that falls in this regime. In this work, we provide an initial attempt on theoretical understanding deep RL from the perspective of function class and neural networks architectures (e.g., width and depth) beyond the "linear" regime. To be specific, we focus on the value based algorithm with the ϵ-greedy exploration via deep (and two-layer) neural networks endowed by Besov (and Barron) function spaces, respectively, which aims at approximating an α-smooth Q-function in a d-dimensional feature space.

In addition to gobbling up most of the internet, ChatGPT now wants access to your OneDrive and SharePoint files, too. One of the earliest uses of AI was to summarize documents and folders of documents, and there's only so many times you can ask it whether Spider-Man would beat Wonder Woman in a fair fight. It would be more productive for AI to collate and make sense of your own personal information, assuming you want to grant access to it. According to OpenAI, ChatGPT can now connect to your OneDrive or SharePoint document libraries, assuming you're a paid ChatGPT Plus, Pro, or Team user who lives outside the EEA, Switzerland, and the UK (via Windows Central). You'll obviously have to connect ChatGPT and give it permission to start poring over your cloud documents.

A study that used artificial intelligence–generated content to "participate" in online discussions and test whether AI was more successful at changing people's minds than human-generated content has caused an uproar because of ethical concerns about the work. This week some of the unwitting research participants publicly asked the University of Zürich (UZH), where the researchers behind the experiment hold positions, to investigate and apologize. "I think people have a reasonable expectation to not be in scientific experiments without their consent," says Casey Fiesler, an expert on internet research ethics at the University of Colorado Boulder. A university statement emailed to Science says the researchers--who remain anonymous--have decided not to publish their results. The university will investigate the incident, the statement says.

A group of researchers covertly ran a months-long "unauthorized" experiment in one of Reddit's most popular communities using AI-generated comments to test the persuasiveness of large language models. The experiment, which was revealed over the weekend by moderators of r/changemyview, is described by Reddit mods as "psychological manipulation" of unsuspecting users. "The CMV Mod Team needs to inform the CMV community about an unauthorized experiment conducted by researchers from the University of Zurich on CMV users," the subreddit's moderators wrote in a lengthy post notifying Redditors about the research. "This experiment deployed AI-generated comments to study how AI could be used to change views." The researchers used LLMs to create comments in response to posts on r/changemyview, a subreddit where Reddit users post (often controversial or provocative) opinions and request debate from other users.

Reddit users who were unwittingly subjected to an AI-powered experiment have hit back at scientists for conducting research on them without permission – and have sparked a wider debate about such experiments. The social media site Reddit is split into "subreddits" dedicated to a particular community, each with its own volunteer moderators. Members of one subreddit called r/ChangeMyView, because it invites people to discuss potentially contentious issues, were recently informed by the moderators that researchers at the University of Zurich, Switzerland, had been using the site as an online laboratory. The team's experiment seeded more than 1700 comments generated by a variety of large language models (LLMs) into the subreddit, without disclosing they weren't real, to gauge people's reactions. These comments included ones mimicking people who had been raped or pretending to be a trauma counsellor specialising in abuse, among others.