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Musk and Altman's bitter feud over OpenAI to be laid bare in court
The tech titans are slated to duke it out in court. The tech titans are slated to duke it out in court. Musk and Altman's bitter feud over OpenAI to be laid bare in court Tesla chief believes Altman broke company's founding agreement - and legal battle promises to be explosive T he bitter rivalry between two of the tech world's most powerful men arrives in court this week, as Elon Musk's lawsuit against Sam Altman and OpenAI heads to trial in Oakland, California. The case is set to feature some of the biggest names in Silicon Valley, and its outcome could affect the course of the AI boom. Musk's suit, filed in 2024, focuses on the formative years of OpenAI when he, Altman and others co-founded the artificial intelligence company as a nonprofit with a grand purpose.
Cannes AI film festival raises eyebrows – and questions about future
A still from animated film La Sélection Mécanique, directed by Jules Blachier. A still from animated film La Sélection Mécanique, directed by Jules Blachier. While emerging technology is banned from the Palme d'Or, an upstart movement is gaining investment and attention I n Cannes' darkened screening rooms, the supposed future of cinema flickered into life this week and it was strange. The first edition of the World AI film festival (WAIFF) showcased visions of men with fish scales erupting from their necks and seaweed from their mouths, a heroine with a heart beating outside her body and so many massed armies of AI-generated tanned men sweeping across battlefields that David Lean would have blushed. Last week the Cannes film festival, entering its 76th year, banned the emerging technology from its Palme d'Or competition, insisting "AI imitates very well but it will never feel deep emotions".
Modality-Agnostic Topology Aware Localization
This work presents a data-driven approach for the indoor localization of an observer on a 2D topological map of the environment. State-of-the-art techniques may yield accurate estimates only when they are tailor-made for a specific data modality like camera-based system that prevents their applicability to broader domains. Here, we establish a modality-agnostic framework (called OT-Isomap) and formulate the localization problem in the context of parametric manifold learning while leveraging optimal transportation. This framework allows jointly learning a lowdimensional embedding as well as correspondences with a topological map. We examine the generalizability of the proposed algorithm by applying it to data from diverse modalities such as image sequences and radio frequency signals. The experimental results demonstrate decimeter-level accuracy for localization using different sensory inputs.