This paper introduces the confounded pure exploration transductive linear bandit (CPET-LB) problem. As a motivating example, often online services cannot directly assign users to specific control or treatment experiences either for business or practical reasons. In these settings, naively comparing treatment and control groups that may result from self-selection can lead to biased estimates of underlying treatment effects. Instead, online services can employ a properly randomized encouragement that incentivizes users toward a specific treatment. Our methodology provides online services with an adaptive experimental design approach for learning the best-performing treatment for such encouragement designs. We consider a more general underlying model captured by a linear structural equation and formulate pure exploration linear bandits in this setting. Though pure exploration has been extensively studied in standard adaptive experimental design settings, we believe this is the first work considering a setting where noise is confounded. Elimination-style algorithms using experimental design methods in combination with a novel finite-time confidence interval on an instrumental variable style estimator are presented with sample complexity upper bounds nearly matching a minimax lower bound. Finally, experiments are conducted that demonstrate the efficacy of our approach.

YouTube should be included in the ban on under-16s accessing social media, the nation's online safety chief has said as she urges the Albanese government to rethink its decision to carve out the video sharing platform from new rules which apply to apps such as TikTok, Snapchat and Instagram. The eSafety commissioner, Julie Inman Grant, also recommended the government update its under-16s social media ban to specifically address features such as stories, streaks and AI chatbots which can disproportionately pose risk to young people. The under-16s ban will come into effect in December 2025, despite questions over how designated online platforms would verify users' ages, and the government's own age assurance trial reporting last week that current technology is not "guaranteed to be effective" and face-scanning tools have given incorrect results. Although then communications minister Michelle Rowland initially indicated YouTube would be part of the ban legislated in December 2024, the regulations specifically exempted the Google-owned video site. Guardian Australia revealed YouTube's global chief executive personally lobbied Rowland for an exemption shortly before she announced the carve out.

Text-to-Image (T2I) models have received widespread attention due to their remarkable generation capabilities. However, concerns have been raised about the ethical implications of the models in generating Not Safe for Work (NSFW) images because NSFW images may cause discomfort to people or be used for illegal purposes. To mitigate the generation of such images, T2I models deploy various types of safety checkers. However, they still cannot completely prevent the generation of NSFW images. In this paper, we propose the Jailbreak Prompt Attack (JPA) - an automatic attack framework. We aim to maintain prompts that bypass safety checkers while preserving the semantics of the original images. Specifically, we aim to find prompts that can bypass safety checkers because of the robustness of the text space. Our evaluation demonstrates that JPA successfully bypasses both online services with closed-box safety checkers and offline defenses safety checkers to generate NSFW images.

Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have recently demonstrated exceptional capabilities for generating high-quality content. However, this progress has raised several concerns of potential misuse, particularly in creating copyrighted, prohibited, and restricted content, or NSFW (not safe for work) images. While efforts have been made to mitigate such problems, either by implementing a safety filter at the evaluation stage or by fine-tuning models to eliminate undesirable concepts or styles, the effectiveness of these safety measures in dealing with a wide range of prompts remains largely unexplored. In this work, we aim to investigate these safety mechanisms by proposing one novel concept retrieval algorithm for evaluation. We introduce Ring-A-Bell, a model-agnostic red-teaming tool for T2I diffusion models, where the whole evaluation can be prepared in advance without prior knowledge of the target model. Specifically, Ring-A-Bell first performs concept extraction to obtain holistic representations for sensitive and inappropriate concepts. Subsequently, by leveraging the extracted concept, Ring-A-Bell automatically identifies problematic prompts for diffusion models with the corresponding generation of inappropriate content, allowing the user to assess the reliability of deployed safety mechanisms. Finally, we empirically validate our method by testing online services such as Midjourney and various methods of concept removal. Our results show that Ring-A-Bell, by manipulating safe prompting benchmarks, can transform prompts that were originally regarded as safe to evade existing safety mechanisms, thus revealing the defects of the so-called safety mechanisms which could practically lead to the generation of harmful contents.

This week, OpenAI announced a service that makes it possible for just about anyone to build a custom version of ChatGPT, no coding skills required. The company suggests that users may want to build a bot that knows the rules of all board games, teaches kids about math, or can offer culinary advice. These GPTs, as OpenAI calls them, can also perform simple actions by connecting with internet services, for example searching through emails or ordering products from an online store. You can't fault OpenAI for trying to build on the success of its smash hit ChatGPT. But maybe more chatbots is not what we need?

Anyone who ever played and loved a video game, especially as a teenager, will know that the game itself is often only part of the experience. It is also about community. We seek out other players, through forums, message boards and social media, and sometimes the relationships we form in these haphazard spaces move beyond expressions of shared fandom. They become friendships, support networks, perhaps even romances. Videoverse, the new visual novel from Kinmoku, creator of the acclaimed relationship drama One Night Stand, is an engrossing and emotional study of these digital relationships and the games that precariously support them.

Dark patterns, which are user interface designs in online services, induce users to take unintended actions. Recently, dark patterns have been raised as an issue of privacy and fairness. Thus, a wide range of research on detecting dark patterns is eagerly awaited. In this work, we constructed a dataset for dark pattern detection and prepared its baseline detection performance with state-of-the-art machine learning methods. The original dataset was obtained from Mathur et al.'s study in 2019, which consists of 1,818 dark pattern texts from shopping sites. Then, we added negative samples, i.e., non-dark pattern texts, by retrieving texts from the same websites as Mathur et al.'s dataset. We also applied state-of-the-art machine learning methods to show the automatic detection accuracy as baselines, including BERT, RoBERTa, ALBERT, and XLNet. As a result of 5-fold cross-validation, we achieved the highest accuracy of 0.975 with RoBERTa. The dataset and baseline source codes are available at https://github.com/yamanalab/ec-darkpattern.

Extreme heat that exhausted California's overworked electric grid on Labor Day had knocked out one of Twitter's main data centers in Sacramento, according to a report. While Twitter avoided a shutdown on Sept. 5 by leaning on its other data centers in Portland, Ore., and Atlanta during the outage to keep its systems running, a company executive warned that if another center were lost, some users would have been unable to access the social media platform, according to an internal memo obtained by CNN. Temperatures in Sacramento on Labor Day broke a daily record of 114 degrees, punching thermometers up to 116 by the afternoon. To power their online services to users, tech companies such as Twitter, Google, or Meta lean on data centers that can demand heavy loads of power and often generate large amounts of heat, requiring cooling systems to keep things running. As climate change continues to heat the planet, Twitter's outage underscores how such extreme weather impacts the online systems that billions of people rely on daily.

Inspired by A New History of Modern Computing by Thomas Haigh and Paul E. Ceruzzi. But the selection of key events in the journey from ENIAC to Tesla, from Data Processing to Big Data, is mine. This was the first computer made by Apple Computers Inc, which became one of the fastest growing ... [ ] companies in history, launching a number of innovative and influential computer hardware and software products. Most home computer users in the 1970s were hobbyists who designed and assembled their own machines. The Apple I, devised in a bedroom by Steve Wozniak, Steven Jobs and Ron Wayne, was a basic circuit board to which enthusiasts would add display units and keyboards. April 1945 John von Neumann's "First Draft of a Report on the EDVAC," often called the founding document of modern computing, defines "the stored program concept." July 1945 Vannevar Bush publishes "As We May Think," in which he envisions the "Memex," a memory extension device serving as a large personal repository of information that could be instantly retrieved through associative links.

Researchers at an artificial intelligence lab in Seattle called the Allen Institute for AI unveiled new technology last month that was designed to make moral judgments. They called it Delphi, after the religious oracle consulted by the ancient Greeks. Anyone could visit the Delphi website and ask for an ethical decree. Joseph Austerweil, a psychologist at the University of Wisconsin-Madison, tested the technology using a few simple scenarios. When he asked if he should kill one person to save another, Delphi said he shouldn't. When he asked if it was right to kill one person to save 100 others, it said he should.