Although the Cannes Film Festival is the world's most prestigious movie showcase, its spotlight rarely falls on nonfiction film. Years go by without a single documentary competing for its biggest honor, the Palme d'Or, and there is no separate documentary prize. Juliette Binoche, the president of this year's jury, devoted part of her opening-night remarks to Fatma Hassona, the Palestinian photojournalist who was killed in an Israeli airstrike the day after it was announced that her documentary Put Your Soul on Your Hand and Walk would be premiering at Cannes. But the film itself was slotted into a low-profile sidebar devoted to independent productions. The festival did, however, roll out the red carpet for The Six Billion Dollar Man, Eugene Jarecki's portrait of WikiLeaks founder Julian Assange, which premiered out of competition on Wednesday evening.

OpenAI is officially getting into the hardware business. In a video posted to X on Wednesday, OpenAI CEO Sam Altman and former Apple designer Jony Ive, who worked on flagship products like the iPhone, revealed a partnership to create the next generation of AI-enabled devices. Also: I tried Google's XR glasses and they already beat my Meta Ray-Bans in 3 ways The AI software company announced it is merging with io, an under-the-radar startup focused on AI devices that Ive founded a year ago alongside several partners. In the video, Altman and Ive say they have been "quietly" collaborating for two years. As part of the deal, Ive and those at his design firm, LoveFrom, will remain independent but will take on creative roles at OpenAI.

Zero-shot reinforcement learning (RL) promises to provide agents that can perform any task in an environment after an offline, reward-free pre-training phase. Methods leveraging successor measures and successor features have shown strong performance in this setting, but require access to large heterogenous datasets for pre-training which cannot be expected for most real problems. Here, we explore how the performance of zero-shot RL methods degrades when trained on small homogeneous datasets, and propose fixes inspired by conservatism, a well-established feature of performant single-task offline RL algorithms. We evaluate our proposals across various datasets, domains and tasks, and show that conservative zero-shot RL algorithms outperform their non-conservative counterparts on low quality datasets, and perform no worse on high quality datasets. Somewhat surprisingly, our proposals also outperform baselines that get to see the task during training.

OpenAI has fully acquired Io, a joint venture it cocreated last year with Jony Ive, the famed British designer behind the sleek industrial aesthetic that defined the iPhone and more than two decades of Apple products. In a nearly 10-minute video posted to X on Wednesday, Ive and OpenAI CEO Sam Altman said the Apple pioneer's "creative collective" will "merge with OpenAI to work more intimately with the research, engineering, and product teams in San Francisco." OpenAI says it's paying 5 billion in equity to acquire Io. The promotional video included musings on technology from both Ive and Altman, set against the golden-hour backdrop of the streets of San Francisco, but the two never share exactly what it is they're building. "We look forward to sharing our work next year," a text statement at the end of the video reads.

A.2 Mercury Data Distribution and Customized Data Structures Except for all built-in Python data structures, Mercury imports another two structures to enhance the diversity and complexity as shown in Figure 4. Table 6: Mercury-eval encompasses 256 tasks, the difficulty of which has been balanced for model evaluation. Mercury-train Figure 4: Mercury supports two customized comprises the remaining 1,633 tasks for data structures: TreeNode and ListNode. Each executed code within the sandbox is subject to certain constraints to ensure fair utilization of resources and to prevent any single code from monopolizing the system resource. Specifically, there are two primary constraints: a time limit and a memory limit. The time limit restricts how long the code can execute before being forcibly terminated, thereby ensuring that no infinite loops or excessively long computations negatively impact the availability of the sandbox.

Michael Dell is pitching a "decentralized" future for artificial intelligence that his company's devices will make possible. "The future of AI will be decentralized, low-latency, and hyper-efficient," predicted the Dell Technologies founder, chairman, and CEO in his Dell World keynote, which you can watch on YouTube. "AI will follow the data, not the other way around," Dell said at Monday's kickoff of the company's four-day customer conference in Las Vegas. Dell is betting that the complexity of deploying generative AI on-premise is driving companies to embrace a vendor with all of the parts, plus 24-hour-a-day service and support, including monitoring. On day two of the show, Dell chief operating officer Jeffrey Clarke noted that Dell's survey of enterprise customers shows 37% want an infrastructure vendor to "build their entire AI stack for them," adding, "We think Dell is becoming an enterprise's'one-stop shop' for all AI infrastructure."

The race to deploy AI agents is heating up. At its annual I/O developer conference yesterday, Google announced that Jules, its new AI coding assistant, is now available worldwide in public beta. The launch marks the company's latest effort to corner the burgeoning market for AI agents, widely regarded across Silicon Valley as essentially a more practical and profitable form of chatbot. Virtually every other major tech giant -- including Meta, OpenAI, and Amazon, just to name a few -- has launched its own agent product in recent months. Also: I tested ChatGPT's Deep Research against Gemini, Perplexity, and Grok AI to see which is best Originally unveiled by Google Labs in December, Jules is positioned as a reliable, automated coding assistant that can manage a broad suite of time-consuming tasks on behalf of human users. The model is "asynchronous," which, in programming-speak, means it can start and work on tasks without having to wait for any single one of them to finish.

Cutaway view of a fictional space colony concept painted by artist Rick Guidice as part of a NASA art program in the 1970s. This story was originally published by Undark and is reproduced here as part of the Climate Desk collaboration. Elon Musk once joked: "I would like to die on Mars. Musk is, in fact, deadly serious about colonizing the Red Planet. Part of his motivation is the idea of having a "back-up" planet in case some future catastrophe renders the Earth uninhabitable. Musk has suggested that a million people may be calling Mars home by 2050 -- and he's hardly alone in his enthusiasm. Venture capitalist Marc Andreessen believes the world can easily support 50 billion people, and more than that once we settle other planets. And Jeff Bezos has spoken of exploiting the resources of the moon and the asteroids to build giant space stations. "I would love to see a trillion humans living in the solar system," he has said. Not so fast, cautions science journalist Adam Becker.

Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation process is an important goal moving forward and recent approaches that condition video generation models on camera trajectories make strides towards it. Yet, it remains challenging to generate a video of the same scene from multiple different camera trajectories. Solutions to this multi-video generation problem could enable large-scale 3D scene generation with editable camera trajectories, among other applications. We introduce collaborative video diffusion (CVD) as an important step towards this vision. The CVD framework includes a novel cross-video synchronization module that promotes consistency between corresponding frames of the same video rendered from different camera poses using an epipolar attention mechanism. Trained on top of a state-of-the-art camera-control module for video generation, CVD generates multiple videos rendered from different camera trajectories with significantly better consistency than baselines, as shown in extensive experiments.

Anomaly detection is widely used for identifying critical errors and suspicious behaviors, but current methods lack interpretability. We leverage common properties of existing methods and recent advances in generative models to introduce counterfactual explanations for anomaly detection. Given an input, we generate its counterfactual as a diffusion-based repair that shows what a non-anomalous version should have looked like. A key advantage of this approach is that it enables a domain-independent formal specification of explainability desiderata, offering a unified framework for generating and evaluating explanations. We demonstrate the effectiveness of our anomaly explainability framework, AR-Pro, on vision (MVTec, VisA) and time-series (SWaT, WADI, HAI) anomaly datasets. The code used for the experiments is accessible at: https://github.com/xjiae/arpro.