The ChatGPT-maker is releasing its "best model yet" as it faces new pressures from Google and other AI competitors. OpenAI has introduced GPT-5.2, its smartest artificial intelligence model yet, with performance gains across writing, coding, and reasoning benchmarks. The launch comes just days after CEO Sam Altman internally declared a "code red," a company-wide push to improve ChatGPT amid intense competition from rivals. "We announced this code red to really signal to the company that we want to marshall resources in one particular area, and that's a way to really define priorities," said OpenAI's CEO of applications, Fidji Simo, in a briefing with reporters on Thursday. "We have had an increase in resources focused on ChatGPT in general."

To leverage user behavior data from the Internet more effectively in recommender systems, this paper proposes a novel collaborative filtering (CF) method called Local Collaborative Filtering (LCF). LCF utilizes local similarities among users and integrates their data using the law of large numbers (LLN), thereby improving the utilization of user behavior data. Experiments are conducted on the Steam game dataset, and the results of LCF align with real-world needs.

Social media platforms have become valuable tools for understanding public health challenges by offering insights into patient behaviors, medication use, and mental health issues. However, analyzing such data remains difficult due to the prevalence of informal language, slang, and coded communication, which can obscure the detection of opioid misuse. This study addresses the issue of opioid-related user behavior on social media, including informal expressions, slang terms, and misspelled or coded language. We analyzed the existing Bidirectional Encoder Representations from Transformers (BERT) technique and developed a BERT-BiLSTM-3CNN hybrid deep learning model, named SABIA, to create a single-task classifier that effectively captures the features of the target dataset. The SABIA model demonstrated strong capabilities in capturing semantics and contextual information. The proposed approach includes: (1) data preprocessing, (2) data representation using the SABIA model, (3) a fine-tuning phase, and (4) classification of user behavior into five categories. A new dataset was constructed from Reddit posts, identifying opioid user behaviors across five classes: Dealers, Active Opioid Users, Recovered Users, Prescription Users, and Non-Users, supported by detailed annotation guidelines. Experiments were conducted using supervised learning. Results show that SABIA achieved benchmark performance, outperforming the baseline (Logistic Regression, LR = 0.86) and improving accuracy by 9.30%. Comparisons with seven previous studies confirmed its effectiveness and robustness. This study demonstrates the potential of hybrid deep learning models for detecting complex opioid-related behaviors on social media, supporting public health monitoring and intervention efforts.

User attributes are essential in multiple stages of modern recommendation systems and are particularly important for mitigating the cold-start problem and improving the experience of new or infrequent users. We propose Behavior-based User Segmentation (BUS), a novel tree-based data structure that hierarchically segments the user universe with various users' categorical attributes based on the users' product-specific engagement behaviors. During the BUS tree construction, we use Normalized Discounted Cumulative Gain (NDCG) as the objective function to maximize the behavioral representativeness of marginal users relative to active users in the same segment. The constructed BUS tree undergoes further processing and aggregation across the leaf nodes and internal nodes, allowing the generation of popular social content and behavioral patterns for each node in the tree. To further mitigate bias and improve fairness, we use the social graph to derive the user's connection-based BUS segments, enabling the combination of behavioral patterns extracted from both the user's own segment and connection-based segments as the connection aware BUS-based recommendation. Our offline analysis shows that the BUS-based retrieval significantly outperforms traditional user cohort-based aggregation on ranking quality. We have successfully deployed our data structure and machine learning algorithm and tested it with various production traffic serving billions of users daily, achieving statistically significant improvements in the online product metrics, including music ranking and email notifications. To the best of our knowledge, our study represents the first list-wise learning-to-rank framework for tree-based recommendation that effectively integrates diverse user categorical attributes while preserving real-world semantic interpretability at a large industrial scale.

The rapid growth of submissions to top-tier Artificial Intelligence (AI) and Machine Learning (ML) conferences has prompted many venues to transition from closed to open review platforms. Some have fully embraced open peer reviews, allowing public visibility throughout the process, while others adopt hybrid approaches, such as releasing reviews only after final decisions or keeping reviews private despite using open peer review systems. In this work, we analyze the strengths and limitations of these models, highlighting the growing community interest in transparent peer review. To support this discussion, we examine insights from Paper Copilot, a website launched two years ago to aggregate and analyze AI / ML conference data while engaging a global audience. The site has attracted over 200,000 early-career researchers, particularly those aged 18-34 from 177 countries, many of whom are actively engaged in the peer review process. Drawing on our findings, this position paper advocates for a more transparent, open, and well-regulated peer review aiming to foster greater community involvement and propel advancements in the field.

There has been a lot of talk about AI chatbots over the past few years, but how much are they actually used? OpenAI's CEO Sam Altman shared in a tweet (spotted by MSPoweruser) some figures that blew us away. ChatGPT apparently has 300 million weekly active users, and the AI chatbot receives over 1 billion messages every day. Altman also boasts that over 1.3 million developers in the US alone have built upon OpenAI for various tools and services. Maybe that isn't too surprising when you consider how ChatGPT can improve day-to-day life.

Meta's blowout year continues after the company reported another stellar financial quarter on Wednesday. But shares fell in after-hours trading after the company missed Wall Street expectations for daily active users. Wall Street analysts had high expectations for the Instagram and WhatsApp parent company, projecting an 18% jump in sales year over year. The company reported 40.6bn in sales, a 19% increase year over year that outpaced investor expectations of 40.19bn. Meta, which saw a 25% jump in its share price over the past two months, reported 6.03 in earnings per share (EPS), surpassing Wall Street's expectations of an EPS of 5.29.

This paper studies the problem of federated training of a deep neural network (DNN) over a random access channel (RACH) such as in computer networks, wireless networks, and cellular systems. More precisely, a set of remote users participate in training a centralized DNN model using SGD under the coordination of a parameter server (PS). The local model updates are transmitted from the remote users to the PS over a RACH using a slotted ALOHA protocol. The PS collects the updates from the remote users, accumulates them, and sends central model updates to the users at regular time intervals. We refer to this setting as the RACH-FL setting. The RACH-FL setting crucially addresses the problem of jointly designing a (i) client selection and (ii) gradient compression strategy which addresses the communication constraints between the remote users and the PS when transmission occurs over a RACH. For the RACH-FL setting, we propose a policy, which we term the ''age-of-gradient'' (AoG) policy in which (i) gradient sparsification is performed using top-K sparsification, (ii) the error correction is performed using memory accumulation, and (iii) the slot transmission probability is obtained by comparing the current local memory magnitude minus the magnitude of the gradient update to a threshold. Intuitively, the AoG measure of ''freshness'' of the memory state is reminiscent of the concept of age-of-information (AoI) in the context of communication theory and provides a rather natural interpretation of this policy. Numerical simulations show the superior performance of the AoG policy as compared to other RACH-FL policies.

In late May, in a 58m Bel Air hilltop mansion, influencers, reality stars and other Angelenos milled around Netflix-branded TV screens displaying choices to be made: Are you a Gemini or a Capricorn? What color are your eyes? The party marked the launch of the streaming giant's latest offering: a slate of Choose Your Own Adventure-style mobile games inspired by its most popular reality television shows, and attendees were selecting the traits of their digital avatars. "I better be a character!" Selling Sunset star Jason Oppenheim exclaimed as he paused near the top of a staircase that led to a reflecting pool with the Netflix logo floating in it.

Elon Musk has announced new changes to social media platform X (formerly Twitter) that will allow certain accounts to unlock free premium features. Posting on the platform Thursday, the 52-year old tech billionaire, and TIME's 2021 Person of the Year, said: "Going forward, all X accounts with over 2500 verified subscriber followers will get Premium features for free and accounts with over 5000 will get Premium for free." Previously, X Premium features would cost a user 8 per month and include the ability to share longer posts and video uploads, have larger reply prioritization, and see fewer adverts on their timeline. Meanwhile X Premium users have all the features of Premium with no adverts in the For You and Following timelines, as well as access to generative artificial intelligence chatbot Grok. These models are the only way users can now display a blue checkmark that once denoted a verified account before the Tesla and SpaceX CEO acquired Twitter Inc for 44bn in April 2022.