Taylor Swift has applied to trademark her voice and appearance in an apparent attempt to protect herself from artificial intelligence impersonations. The pop superstar has lodged three trademark applications in the US - one using a photo of herself on stage during her Eras Tour, and the other two being audio clips of her introducing herself while promoting her last album. AI-generated versions of Swift have cropped up in various ways in recent years - from explicit images to a fake election ad in which she appeared to urge people to vote for Donald Trump. The move comes after actor Matthew McConaughey became the first celebrity to use trademark rules to attempt to protect his voice and image from AI misuse earlier this year . Trademark applications are a relatively new way for celebrities to combat the growing issue of AI rip-offs.

A phishing email impersonating the Social Security Administration uses official logos and urgent deadlines to trick recipients into downloading malware.

Humans are remarkably flexible in understanding viewpoint changes due to visual cortex supporting the perception of 3D structure. In contrast, most of the computer vision models that learn visual representation from a pool of 2D images often fail to generalize over novel camera viewpoints. Recently, the vision architectures have shifted towards convolution-free architectures, visual Transformers, which operate on tokens derived from image patches. However, these Transformers do not perform explicit operations to learn viewpoint-agnostic representation for visual understanding. To this end, we propose a 3DToken Representation Layer (3DTRL) that estimates the 3D positional information of the visual tokens and leverages it for learning viewpoint-agnostic representations.

PCWorld reports that OpenAI has launched GPT-5.5, its most advanced AI model, exclusively for paying ChatGPT subscribers on Plus, Pro, Business, and Enterprise plans. The new model delivers faster, more efficient performance in coding, research, and math while outperforming competitors like Google's Gemini 3.1 Pro and Anthropic's Claude Opus 4.7. GPT-5.5 represents a significant step toward OpenAI's'super app' vision, integrating various AI services into one comprehensive platform. OpenAI recently launched GPT-5.5, which the company describes as its most advanced and intuitive AI model to date. The new model is said to be both faster and more efficient, with specific improvements in areas including coding, research, and math. At the same time, it's said to perform better compared to competing models like Google's Gemini 3.1 Pro and Anthropic's Claude Opus 4.7. According to OpenAI co-founder Greg Brockman, GPT-5.5 is also a step towards the company's vision of a future "super app," where services such as ChatGPT, Codex, and an AI-driven web browser are integrated into a single platform, reports TechCrunch . GPT-5.5 is currently rolling out to paying ChatGPT users, which includes those on Plus, Pro, Business, and Enterprise plans. This article originally appeared on our sister publication PC för Alla and was translated and localized from Swedish.

The two companies announced an amended partnership that lets OpenAI use other cloud platforms and offer its models to other companies. OpenAI is opening up its partnership with Microsoft in the latest amendment to the major multi-year collaboration between the tech giants. The latest changes allow OpenAI to offer its latest AI models to other companies and through other cloud providers, stripping Microsoft of its exclusivity rights. In a joint announcement posted on OpenAI and Microsoft's websites, Microsoft will still be OpenAI's primary cloud partner with the latest products shipping first on Azure, but OpenAI is now allowed to use any cloud provider. Sam Altman, OpenAI's CEO, posted on X that the company is now able to make our products and services available across all clouds.

We provide concrete rules below for the two competition tracks that comprise DATACOMP: filtering and BYOD . Additionally, we provide a checklist, which encourages participants to specify design decisions, which allows for more granular comparison between submissions. A.1 Filtering track rules Participants can enter submissions for one or many different scales: small, medium, large or xlarge, which represent the raw number of image-text pairs in CommonPool that should be filtered. After choosing a scale, participants generate a list of uids, where each uid refers to a COMMONPOOL sample. The list of uids is used to recover image-text pairs from the pool, which is used for downstream CLIP training.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Four experts spoke about the importance of investing in early childhood education in a TIME100 Talk moderated by TIME executive editor Nikhil Kumar at the TIME100 Summit in New York City on April 22. The panel featured Amanda Devercelli, senior education specialist at the World Bank; Emily Gustafsson-Wright, senior fellow in the global economy and development program at the Brookings Institution; Maria Adelaida Lopez, an artist and educator; and Dr. Jack Shonkoff, a professor of child health and development and founding director of the Center on the Developing Child at Harvard University.

We provide the supplements of "Contextual Gaussian Process Bandits with Neural Networks" here. Specifically, we discuss alternative acquisition functions that can be incorporated with the neural network-accompanied Gaussian process (NN-AGP) model in Section 6. In Section 7, we discuss the bandit algorithm with NN-AGP, where the neural network approximation error is considered. In Section 8, we provide the detailed proof of theorems. We provide the experimental details and include additional numerical experiments in Section 9. Last we discuss the limitations of NN-AGP and propose the potential approaches to addressing the limitations for future work, including sparse NN-AGP for alleviating computational burdens and transfer learning with NN-AGP to address cold-start issue; see Section 10. In the main text, we employ the upper confidence bound function as the acquisition function in the contextual Bayesian optimization approach. Here, we provide two alternative choices: Thompson sampling (TS) and knowledge gradient (KG). We describe the two procedures of the contextual GP bandit problems with NN-AGP, where the acquisition function is replaced by TS or KG. It chooses the action that maximizes the expected reward with respect to a random belief that is drawn for a posterior distribution. Besides the multi-armed bandit problems, TS has also achieved both theoretical and practical success in BO and Gaussian process regression. For more detailed discussions on TS, we refer to [87, 88]. Specifically, we propose a neural network-accompanied Gaussian process Thompson sampling (NNAGP-TS) approach to address contextual GP bandits. The approach works as follows. In each iteration, NN-AGP-TS first fits an NN-AGP model with the historic data. Then, given the current contextual variable, a realization of the Gaussian process with respect to x X is sampled from the posterior distribution conditional on the historic data1.