Elon Musk and Sam Altman are going to court over OpenAI's future Elon Musk says he's suing to save the company's mission. The case could have huge consequences for OpenAI and the AI race. After a yearslong legal feud, Elon Musk and OpenAI CEO Sam Altman are heading to trial this week in Northern California in a case that could have sweeping consequences. Ahead of OpenAI's highly anticipated IPO, the court could rule on whether the company is allowed to exist as a for-profit enterprise and might even oust its current executive leadership, including Altman. Musk is suing OpenAI, alleging that Altman and OpenAI president Greg Brockman deceived him into bankrolling the company in its early days by promising to maintain it as a nonprofit dedicated to developing AI that benefits humanity, only to later restructure the company to operate a for-profit subsidiary. Musk cofounded OpenAI with Altman and others in 2015, but he left in 2018 after a bitter power struggle.

Elon Musk Boosts New Yorker's Sam Altman Exposé on X as Trial Begins The move comes as the trial for Elon Musk's lawsuit against OpenAI kicks off in federal court in Oakland. Elon Musk is boosting a post on X promoting The New Yorker's extensive investigation into Sam Altman's allegedly deceptive behavior, WIRED has confirmed. The move comes just as Musk's lawsuit against OpenAI and Altman heads to a jury trial in a federal courtroom on Monday morning. People scrolling X on Monday reported seeing an April 6 post from Ronan Farrow, a coauthor on the New Yorker article, promoting the investigation. A pop-up on the post on X's mobile app says it was boosted by @elonmusk, who also owns the platform.

Understanding the behavior of non-human primates is crucial for improving animal welfare, modeling social behavior, and gaining insights into distinctively human and phylogenetically shared behaviors. However, the lack of datasets on non-human primate behavior hinders in-depth exploration of primate social interactions, posing challenges to research on our closest living relatives. To address these limitations, we present ChimpACT, a comprehensive dataset for quantifying the longitudinal behavior and social relations of chimpanzees within a social group. Spanning from 2015 to 2018, ChimpACT features videos of a group of over 20 chimpanzees residing at the Leipzig Zoo, Germany, with a particular focus on documenting the developmental trajectory of one young male, Azibo.

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.

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.

Attention over learned object embeddings enables complex visual reasoning.

Recent work has demonstrated the effectiveness of formulating decision making as supervised learning on offline-collected trajectories. Powerful sequence models, such as GPT or BERT, are often employed to encode the trajectories. However, the benefits of performing sequence modeling on trajectory data remain unclear. In this work, we investigate whether sequence modeling has the ability to condense trajectories into useful representations that enhance policy learning. We adopt a two-stage framework that first leverages sequence models to encode trajectory-level representations, and then learns a goal-conditioned policy employing the encoded representations as its input.