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Category: robotics/AI – Page 117

Liquid-crystal platform overcomes optical losses in photonic circuits

Photonic circuits, which manipulate light to perform various computational tasks, have become essential tools for a range of advanced technologies—from quantum simulations to artificial intelligence. These circuits offer a promising way to process information with minimal energy loss, especially in fields like quantum computing where complex systems are simulated to test theories of quantum mechanics.

However, the growth in circuit size and complexity has historically led to a rise in optical losses, making it challenging to scale these systems for large-scale applications, such as multiphoton quantum experiments or all-optical AI systems.

As reported in Advanced Photonics, researchers at the University of Naples Federico II have now developed a new approach to address this problem. Using a liquid-crystal (LC)-based platform, the team designed an optical processor capable of handling hundreds of optical modes in a compact, two-dimensional setup. This breakthrough offers a solution to a key limitation in traditional , where losses increase as the number of modes grows.

Who’s to blame when AI makes a medical error?

Assistive artificial intelligence technologies hold significant promise for transforming health care by aiding physicians in diagnosing, managing, and treating patients. However, the current trend of assistive AI implementation could actually worsen challenges related to error prevention and physician burnout, according to a new brief published in JAMA Health Forum.

The brief, written by researchers from the Johns Hopkins Carey Business School, Johns Hopkins Medicine, and the University of Texas at Austin McCombs School of Business, explains that there is an increasing expectation of physicians to rely on AI to minimize medical errors. However, proper laws and regulations are not yet in place to support physicians as they make AI-guided decisions, despite the fierce adoption of these technologies among health care organizations.

The researchers predict that will depend on whom society considers at fault when the fails or makes a mistake, subjecting physicians to an unrealistic expectation of knowing when to override or trust AI. The authors warn that such an expectation could increase the risk of burnout and even errors among physicians.

Everything Just CHANGED in the global automobile industry

Tesla is preparing to launch its robo taxi in June, leveraging its unique autonomy and data advantages to navigate challenges such as new tariffs and production shifts, while positioning itself for significant growth amid declining competitor viability ## Questions to inspire discussion ## Tesla’s Robo Taxi Service.

🚕 Q: When and where is Tesla launching its robo taxi service? A: Tesla’s robo taxi service is set to launch in Austin, Texas in June 2025, with plans for a nationwide rollout in the US later that year.

🏎️ Q: What vehicles will be eligible for Tesla’s robo taxi service? A: The service will be available on all vehicles equipped with Full Self-Driving (FSD) capability, including existing Model 3 and Model Y, not just the upcoming Cybertruck.

💰 Q: How will Tesla’s robo taxi network economics work? A: The economics will be based on cost per mile, factoring in low capital costs of Tesla EVs and low power consumption of their onboard autonomy systems.

📊 Q: What competitive advantage does Tesla have in the robo taxi market? A: Tesla’s existing fleet of billions of miles of deployed vehicles and hundreds of thousands of users provide a massive data advantage for improving and assessing the service. ## Tariffs and Supply Chain.

🏭 Q: What is Tesla’s supply chain strategy? A: Tesla aims to build cars where sold for environmental reasons, which is considered best practice in network design but extremely difficult to implement.

Continue reading “Everything Just CHANGED in the global automobile industry” | >

Tesla Officially Confirms Robotaxi Autonomous Rides Will Begin in Texas This June

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.

A lighter, smarter magnetoreceptive electronic skin

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.

Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation

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.