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Category: robotics/AI – Page 36

The company has been negotiating with both the Austin city authorities and the city’s autonomous vehicle working group since May 2024 regarding the introduction of the Robotaxi service safely. Set for release in June 2025, this fully self-driving fleet is a backup plan to the journey that Tesla is eager to accomplish of manufacturing electric and self-driving vehicles that can revolutionize city transportation.

During the Q4 2024 earnings conference call on January 29, Elon Musk announced the plan for the Robotaxi rollout in Austin. At the end of the interview, Musk further said, “We feel confident in being able to do an initial launch of unsupervised, no one in the car, full self-driving in Austin in June.” He noted that the process would be progressive to avoid risks that are associated with accidents and legal issues.

The future is autonomous & it starts in Austin, this June.

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Imagine navigating a virtual reality with contact lenses or operating your smartphone underwater: This and more could soon be a reality thanks to innovative e-skins.

A research team led by the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) has developed an that detects and precisely tracks magnetic fields with a single global sensor. This artificial skin is not only light, transparent and permeable, but also mimics the interactions of real skin and the brain, as the team reports in the journal Nature Communications.

Originally developed for robotics, e-skins imitate the properties of real skin. They can give robots a or replace lost senses in humans. Some can even detect chemical substances or magnetic fields. But the technology also has its limits. Highly functional e-skins are often impractical because they rely on extensive electronics and large batteries.

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Multimodal large language models (MLLMs) hold promise for a range of medical applications. Here, the authors use MLLMs for 3D brain CT radiology report generation, demonstrating that combining anatomy-aware model fine-tuning with robust evaluation metrics establishes a comprehensive and effective framework.

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In an era where data privacy concerns loom large, a new approach in artificial intelligence (AI) could reshape how sensitive information is processed.

Researchers Austin Ebel and Karthik Garimella, Ph.D. students, and Assistant Professor of Electrical and Computer Engineering Brandon Reagen have introduced Orion, a novel framework that brings fully (FHE) to deep learning—allowing AI models to practically and efficiently operate directly on encrypted data without needing to decrypt it first.

The implications of this advancement, published on the arXiv preprint server and scheduled to be presented at the 2025 ACM International Conference on Architectural Support for Programming Languages and Operating Systems, are profound.

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There remain many questions — how precisely to test prime resonance coupling in the lab, how to formalize “consciousness” in a rigorous physical sense, and how to harness these insights for breakthrough technologies.

Yet the potential is vast. Non-local communication, quantum AI, and a bold reinterpretation of black holes as ultimate observers challenge us to delve deeper and rethink old assumptions.

The journey forward will require experiments that push the boundaries of quantum measurement, investigate subtle anomalies in tunneling and interference, and refine our understanding of how consciousness might operate as an entropic conductor.

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