ByteDance, Alibaba Group and Tencent Holdings among those ordering the most sophisticated AI semiconductors available under US export rules
Category: robotics/AI – Page 596
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.
AI is changing the game for plant proteins
Artificial Intelligence (AI) is set to transform how we develop future protein foods, by identifying less resource-intensive plant crop varieties, and the most productive ways to farm them and turn them into new products.
Untethered subcentimeter flying robots
A 9.4-mm untethered flying robot achieves controllable flights, such as hovering, collision survival, and route adjustment.
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.
Researchers teach LLMs to solve complex planning challenges
A new large language model framework teaches LLMs to use an optimization solving algorithm to resolve complex, multistep planning tasks. With the LLMFP framework, someone can input a natural language description of their problem and receive a plan to reach their desired goal.
Scientists discover major differences in how humans and AI ‘think’ — and the implications could be significant
Study finds that AI fundamentally lacks the human capability to make creative mental connections, raising warning signs for how we deploy AI tools.
AI-powered forecasting model proves more accurate than traditional systems at predicting the weather
The European Center for Medium-Range Weather Forecasts (ECMWF) has fully operationalized its AI Forecasting System (AIFS), achieving up to 20% greater accuracy than traditional methods, particularly in tropical cyclone tracking.
MIT introduced a smart assistant for LLM
Large language models (LLMs) show promise in tackling planning problems, but there’s a balance between flexibility and complexity. While LLMs can act as zero-shot planners, they struggle with complex tasks involving multiple constraints or long-term goals.
Many frameworks that address these challenges require task-specific preparation, such as tailored examples and predefined validators, which limits their ability to adapt to different tasks.