Feedback was amazed to hear that dog ownership could cause a hurricane across the other side of the world. Or are we barking up the wrong tree? Kristian Steensen Nielsen seems like a sensible type. A researcher at the Copenhagen Business School in Denmark, he studies "the role of behavior change in mitigating climate change and conserving biodiversity". In other words, how can we make our lives more environmentally friendly, and how and when do those changes scale up to become truly effective?

It's one of those topics that comes up over drinks or dinner at the moment: whether or not you think AI is going to steal your job. So far, I've felt relatively confident that while AI could no doubt have a fair crack at writing a newspaper opinion column, there is something I do as part of my work that AI cannot: reporting. Except now, it seems, AI is claiming to be doing that as well. Last week, it was revealed that at least six reputable publications have had to take down published articles because it turned out that they were probably pieces of fiction written by AI and then passed off by somebody as works of journalism under the name of Margaux Blanchard. One of these was a piece for Wired titled They Fell in Love Playing Minecraft.

Multiple news organisations have taken down articles written by an alleged freelance journalist that now appear to have been generated by AI. On Thursday, Press Gazette reported that at least six publications, including Wired and Business Insider, have removed articles from their websites in recent months after it was discovered that the stories – written under the name of Margaux Blanchard – were AI-generated. Wired published a story titled "They Fell in Love Playing Minecraft. A few weeks later, the outlet took down the story, stating in an editor's note: "After an additional review of the article … Wired editorial leadership has determined this article does not meet our editorial standards." The story cited a "Jessica Hu", an alleged 34-year-old "ordained officiant based in Chicago" who reportedly "made a name for herself as a'digital celebrant', specialising in ceremonies across Twitch, Discord and VRChat", according to Press Gazette, which reviewed the Wired article. Both the Press Gazette and the Guardian were not able to verify the identity of Hu. Press Gazette further reported that in April, Business Insider published two essays by Blanchard titled: "Remote work has been the best thing for me as a parent but the worst as a person" and "I had my first kid at 45.

Collaborative Problem-Solving (CPS) markers capture key aspects of effective teamwork, such as staying on task, avoiding interruptions, and generating constructive ideas. An AI system that reliably detects these markers could help teachers identify when a group is struggling or demonstrating productive collaboration. Such a system requires an automated pipeline composed of multiple components. In this work, we evaluate how CPS detection is impacted by automating two critical components: transcription and speech segmentation. On the public Weights Task Dataset (WTD), we find CPS detection performance with automated transcription and segmentation methods is comparable to human-segmented and manually transcribed data; however, we find the automated segmentation methods reduces the number of utterances by 26.5%, impacting the the granularity of the data. We discuss the implications for developing AI-driven tools that support collaborative learning in classrooms.

Our goal is to develop an AI Partner that can provide support for group problem solving and social dynamics. In multi-party working group environments, multimodal analytics is crucial for identifying non-verbal interactions of group members. In conjunction with their verbal participation, this creates an holistic understanding of collaboration and engagement that provides necessary context for the AI Partner. In this demo, we illustrate our present capabilities at detecting and tracking nonverbal behavior in student task-oriented interactions in the classroom, and the implications for tracking common ground and engagement.

The creators of artificial intelligence need to abandon their "tech utopian" mindset, according to the terror watchdog, amid fears that the new technology could be used to groom vulnerable individuals. Jonathan Hall KC, whose role is to review the adequacy of terrorism legislation, said the national security threat from AI was becoming ever more apparent and the technology needed to be designed with the intentions of terrorists firmly in mind. He said too much AI development focused on the potential positives of the technology while neglecting to consider how terrorists might use it to carry out attacks. "They need to have some horrible little 15-year-old neo-Nazi in the room with them, working out what they might do. You've got to hardwire the defences against what you know people will do with it," said Hall.

We consider the contextual bandit problem on general action and context spaces, where the learner's rewards depend on their selected actions and an observable context. This generalizes the standard multi-armed bandit to the case where side information is available, e.g., patients' records or customers' history, which allows for personalized treatment. We focus on consistency -- vanishing regret compared to the optimal policy -- and show that for large classes of non-i.i.d. contexts, consistency can be achieved regardless of the time-invariant reward mechanism, a property known as universal consistency. Precisely, we first give necessary and sufficient conditions on the context-generating process for universal consistency to be possible. Second, we show that there always exists an algorithm that guarantees universal consistency whenever this is achievable, called an optimistically universal learning rule. Interestingly, for finite action spaces, learnable processes for universal learning are exactly the same as in the full-feedback setting of supervised learning, previously studied in the literature. In other words, learning can be performed with partial feedback without any generalization cost. The algorithms balance a trade-off between generalization (similar to structural risk minimization) and personalization (tailoring actions to specific contexts). Lastly, we consider the case of added continuity assumptions on rewards and show that these lead to universal consistency for significantly larger classes of data-generating processes.

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. My partner and I (man and woman in our mid-30s) want to open profiles on an adult dating site (Feeld, probably?) to connect with couples and singles. We've had ethically non-monogamous encounters at adult resorts, but haven't tried a dating site to meet people closer to home in hopes of landing on more "social swinging" relationships. There are a wealth of swinging/lifestyle podcasts with episodes about dating profiles, and omitting your face from "public" photos on the site (that is, visible to all members) is uniform advice. Of course, most often this is to avoid being identified on the site.

Asking questions is one of the most crucial pedagogical techniques used by teachers in class. It not only offers open-ended discussions between teachers and students to exchange ideas but also provokes deeper student thought and critical analysis. Providing teachers with such pedagogical feedback will remarkably help teachers improve their overall teaching quality over time in classrooms. Therefore, in this work, we build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. Compared with traditional methods, our approach not only avoids cumbersome feature engineering, but also adapts to the task of multi-class question detection in real education scenarios. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions. We conducted extensive experiments on the question detection tasks in a real-world online classroom dataset and the results demonstrate the superiority of our model in terms of various evaluation metrics.

This paper studies Positive-Unlabeled Classification, the problem of semi-supervised binary classification in the case when Negative (N) class in the training set is contaminated with instances of Positive (P) class. We develop a novel method (DEDPUL) that simultaneously solves two problems concerning the contaminated Unlabeled (U) sample: estimates the proportions of the mixing components (P and N) in U, and classifies U. By conducting experiments on synthetic and real-world data we favorably compare DEDPUL with current state-of-the-art methods for both problems. We introduce an automatic procedure for DEDPUL hyperparameter optimization. Additionally, we improve two methods in the literature and achieve DEDPUL level of performance with one of them.