What if one of the most important assumptions in modern cosmology is wrong?
Priya Natarajan discusses new observational hints that dark energy may not be constant over cosmic time — a possibility that would have major implications if confirmed.
0:00 Is Dark Energy Really Constant?
1:30 New Hints From Observations.
2:23 Why Changing Dark Energy Matters.
4:14 What Changing Dark Energy Would Mean.
6:44 What Evidence Would Convince Us?
Priyamvada \
Drawing from historical examples, Carmen Kivisild puts forth a bold vision for why wet lab automation is the future of biology. I’ve had similar hopes/thoughts for how biology will progress and I greatly enjoyed how this essay put words to these concepts.
Like Grace Hopper killed the punch card. The story of how “scientist” gets redefined.
A new 3D image projection system from researchers at the UCLA Samueli School of Engineering and the California NanoSystems Institute (CNSI) marks a major step toward overcoming a longstanding problem for holographic technology.
Professor Aydogan Ozcan led the work reported in a recent paper published in Light Science and Applications, which describes the team’s new snapshot 3D image projection system, solving one of the technology’s most daunting issues.
“These results establish the diffractive 3D display system as a compact and scalable framework for depth-resolved snapshot 3D image projection, with potential applications in holographic displays, AR/VR interfaces, and volumetric optical computing,” the authors write.
Matt White is the AI CTO at the Linux Foundation. He just got back from visiting DeepSeek, Moonshot, Zhipu, Qwen, and Minimax in China.
In this episode:
What he saw inside China’s top AI labs that the West doesn’t know.
4 Chinese breakthroughs US labs are quietly building on.
Why export controls backfired and forced China to innovate faster.
150 humanoid robot startups, China builds the body, America builds the brain.
The biggest mistake enterprises make with AI
Where he’d put $1M in AI right now.
Chapters.
00:00 — Intro.
00:54 — Inside China’s AI Labs.
09:43 — DeepSeek’s Culture & the Distillation Debate.
17:03 — Open Source Models vs Commercial APIs.
27:28 — How Startups Should Choose Their AI Stack.
41:17 — Agentic AI Safety & Multi-Agent Systems.
53:08 — Enterprise AI Mistakes & Where to Start.
01:08:26 — The Future of Agentic Commerce & One-Person Companies.
Follow Matt White: / mdwdata.
Follow Anthony Sar: / anthonysar.
Imagine a surgeon being able to “step inside” a digital version of a patient’s body — studying organs, tissues, and complex structures, rehearsing procedures, and evaluating possible approaches before performing the actual operation.
The integration of robotic surgical assistants (RSAs) in operating rooms offers substantial advantages for both surgeons and patient outcomes. Currently operated through teleoperation by trained surgeons at a console, these surgical robot platforms provide augmented dexterity that has the potential to streamline surgical workflows and alleviate surgeon workloads. Exploring visual behavior cloning for next-generation surgical assistants could further enhance the capabilities and efficiency of robotic-assisted surgeries.
This post introduces two template frameworks for robotic surgical assistance: Surgical First Interactive Autonomy Assistant (SuFIA) and Surgical First Interactive Autonomy Assistant – Behavior Cloning (SuFIA-BC). SuFIA uses natural language guidance and large language models (LLMs) for high-level planning and control of surgical robots, while SuFIA-BC enhances the dexterity and precision of robotic surgical assistants through behavior cloning (BC) techniques. These frameworks explore the recent advances in both LLMs and BC techniques and tune them to excel to the unique challenges of surgical scenes.
This research aims to accelerate the development of surgical robotic assistants, with the eventual goal of alleviating surgeon fatigue, enhancing patient safety, and democratizing access to high-quality healthcare. SuFIA and SuFIA-BC advance this field by demonstrating their capabilities across various surgical subtasks in simulated and physical settings. Moreover, the photorealistic assets introduced in this work enable the broader research community to explore surgical robotics—a field that has traditionally faced significant barriers to entry due to limited data accessibility, the high costs of expert demonstrations, and the expensive hardware required.
Casey Harrell uses his implants to talk to friends and family, read to his young daughter, and perform his job.
In a first of its kind procedure, a man left paralyzed after a spinal cord injury was able to walk again. Doctors implanted what they call a \.
Aging is a universal biological process, yet the reasons why some individuals live significantly longer and healthier lives have long puzzled scientists. Among the genes linked to exceptional longevity, FOXO3 consistently stands out as one of the most influential “master controllers” of cellular resilience. This single transcription factor integrates signals from stress, metabolism, DNA repair, and stem cell biology, orchestrating a vast genetic program that determines how cells survive, adapt, or age [1].
In recent years, interest in FOXO3 has surged across aging research, regenerative medicine, oncology, and precision therapeutics. Variants of the FOXO3 gene are strongly associated with centenarian populations worldwide, while disruptions in its regulatory network contribute to multiple disorders, including cancer, neurodegeneration, metabolic decline, and tissue degeneration. With advances in computational biology and pathway analysis, it is now possible to map FOXO3’s complex signaling network and uncover new therapeutic strategies.
This blog post explores FOXO3’s multifaceted biological roles, its influence on disease, and what our curated data from TRANSFAC®, TRANSPATH®, and HumanPSD™ reveals about the FOXO3 regulatory network. The goal is to provide a scientifically rich yet accessible overview that sparks curiosity among researchers studying aging, longevity, and systems-level biology.