Unitree’s G1 demonstrates a new level of robotic agility with a complex movement following an AI software update.
Category: robotics/AI – Page 99
Super-resolution imaging technology reveals inner workings of living cells
A breakthrough in imaging technology promises to transform our understanding of the inner workings of living cells, and provide insights into a wide range of diseases.
The study, recently published in the journal Nature Communications, unveils an innovative approach that combines super-resolution imaging with artificial intelligence and deep learning to reveal subcellular structures and dynamics. It was led by researchers from Peking University, Ningbo Eastern Institute of Technology and the University of Technology Sydney.
“It’s like taking an airplane over a city at night and watching all the live interactions,” said UTS Distinguished Professor Dayong Jin. “This cutting-edge technology will open new doors in the quest to understand the intricate world within our cells.”
When AI builds AI: The next great inventors might not be human
In the paper accompanying the launch of R1, DeepSeek explained how it took advantage of techniques such as synthetic data generation, distillation, and machine-driven reinforcement learning to produce a model that exceeded the current state-of-the-art. Each of these approaches can be explained another way as harnessing the capabilities of an existing AI model to assist in the training of a more advanced version.
DeepSeek is far from alone in using these AI techniques to advance AI. Mark Zuckerberg predicts that the mid-level engineers at https://fortune.com/company/facebook/” class=””>Meta may soon be replaced by AI counterparts, and that Llama 3 (his company’s LLM) “helps us experiment and iterate faster, building capabilities we want to refine and expand in Llama 4.” https://fortune.com/company/nvidia/” class=””>Nvidia CEO Jensen Huang has spoken at length about creating virtual environments in which AI systems supervise the training of robotic systems: “We can create multiple different multiverses, allowing robots to learn in parallel, possibly learning in 100,000 different ways at the same time.”
This isn’t quite yet the singularity, when intelligent machines autonomously self-replicate, but it is something new and potentially profound. Even amidst such dizzying progress in AI models, though, it’s not uncommon to hear some observers talk about the potential slowing of what’s called the “scaling laws”—the observed principles that AI models increase in performance in direct relationship to the quantity of data, power, and compute applied to them. The release from DeepSeek, and several subsequent announcements from other companies, suggests that arguments of the scaling laws’ demise may be greatly exaggerated. In fact, innovations in AI development are leading to entirely new vectors for scaling—all enabled by AI itself. Progress isn’t slowing down, it’s speeding up—thanks to AI.
The Power Of AI In Your Workflow: Copilot Explained | Satya Nadella
Satya Nadella, CEO of Microsoft, shares the groundbreaking potential of AI Copilot — a powerful tool that’s transforming how we work. From streamlining everyday tasks to revolutionizing healthcare workflows, AI Copilot is designed to seamlessly integrate with the tools we already use, like Teams, Word, and Excel.
Satya Nadella explains how AI Copilot is helping doctors prepare for high-stakes meetings, automatically generating agendas, summaries, and even PowerPoint presentations. Plus, see how it empowers professionals to gather the latest insights, collaborate with teams, and create smarter workflows with ease.
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Cocoa extract fails to prevent age-related vision loss, clinical trial finds
Brigham and Women’s Hospital-led research reports no significant long-term benefit of cocoa flavanol supplementation in preventing age-related macular degeneration (AMD). The paper is published in the journal JAMA Ophthalmology.
AMD is a progressive retinal disease and the most common cause of severe vision loss in adults over age 50. AMD damages the macula, the central part of the retina responsible for sharp, detailed vision. While peripheral sight is typically preserved, central vision loss can impair reading, driving, facial recognition, and other quality of life tasks. Abnormalities of blood flow in the eye are associated with the occurrence of AMD.
Cocoa flavanols are a group of naturally occurring plant compounds classified as flavonoids, found primarily in the cocoa bean. These bioactive compounds have been studied for their vascular effects, including improved endothelial function and enhanced nitric oxide production, which contribute to vasodilation and circulatory health. Previous trials have shown that moderate intake of cocoa flavanols may lower blood pressure, improve lipid profiles, and reduce markers of inflammation, suggesting a role in mitigating cardiovascular and related vascular conditions.
Taking a responsible path to AGI
We’re exploring the frontiers of AGI, prioritizing readiness, proactive risk assessment, and collaboration with the wider AI community.
Artificial general intelligence (AGI), AI that’s at least as capable as humans at most cognitive tasks, could be here within the coming years.
Integrated with agentic capabilities, AGI could supercharge AI to understand, reason, plan, and execute actions autonomously. Such technological advancement will provide society with invaluable tools to address critical global challenges, including drug discovery, economic growth and climate change.
The Fluid Architecture of Cognitive Possibility
This article isn’t about whether AI is conscious. It’s about how it behaves—or, more precisely, how it performs something that resembles thinking within a completely different geometric, structural, and temporal reality. It’s a phenomenon we’ve yet to fully name, but we can begin to describe it—not as a function of symbolic logic or linear deduction, but as something more amorphous, more dynamic. Something I call the fluid architecture of cognitive possibility.
Traditional human thought is sequential. We move from premise to conclusion, symbol to symbol, with language as the scaffolding of cognition. We think in lines. We reason in steps. And it feels good—there’s comfort in the clarity of structure, in the rhythm of deduction.
But LLMs don’t think that way.