A team of researchers from South Korea has developed a thin, robotic actuator based on the movements of real human muscles.
Actuators are, effectively, the ‘muscles’ of robotic machines. They are devices that convert stored energy into physical motion.
The new system was inspired by human muscle proteins, a press statement reveals. It could eventually allow for human-robot interactions, and may even enable robots to perform delicate surgical procedures.
Questions to inspire discussion.
🛣️ Q: What specific improvements can we expect from the new FSD model? A: The new model will see and avoid potholes, drive better in parking lots, find parking spaces more efficiently, figure out pickup and drop-off zones for robotaxis, and handle high chaos situations like crowded areas more effectively.
Safety and Regulations.
🚦 Q: How does FSD’s safety compare to human drivers? A: Tesla’s FSD technology is already much safer than humans with the current Version 4, which has 8 cameras and 10x better parameters than previous versions, and it’s expected to improve further with future updates.
📊 Q: How significant are the improvements in the new FSD model? A: While the model has 10x better parameters, the features may not be 10x better, but improvements could be greater than 10x due to hard-to-measure benefits like reduced driving stress and increased safety.
🚫 Q: What’s limiting FSD’s full potential? A: Regulations currently hold FSD back from reaching its full potential, despite its ability to drive faster and handle high chaos situations more effectively.
Researchers from Lawrence Livermore National Laboratory (LLNL), in collaboration with Harvard University, Caltech, Sandia National Laboratories, and Oregon State University, have unveiled a groundbreaking innovation in materials science: a programmable soft material capable of bending, bouncing, and absorbing energy on demand. This new material, described in the journal Advanced Materials, could pave the way for next-generation protective gear, aerospace structures, and adaptive robotic systems.
OpenAI released a keenly awaited new generation of its hallmark ChatGPT on Thursday, touting “significant” advancements in artificial intelligence capabilities as a global race over the technology accelerates.
ChatGPT-5 is rolling out free to all users of the AI tool, which is used by nearly 700 million people weekly, OpenAI said in a briefing with journalists.
Co-founder and chief executive Sam Altman touted this latest iteration as “clearly a model that is generally intelligent.”
A machine learning method developed by researchers from the Institute of Science Tokyo, the Institute of Statistical Mathematics, and other institutions accurately predicts liquid crystallinity of polymers with 96% accuracy. They screened over 115,000 polyimides and selected six candidates with a high probability of exhibiting liquid crystallinity. Upon successful synthesis and experimental analyses, these liquid crystalline polyimides demonstrated thermal conductivities up to 1.26 W m⁻¹ K⁻¹, accelerating the discovery of efficient thermal materials for next-generation electronics.
Finding new polymer materials that can efficiently dissipate heat while maintaining high reliability is one of the biggest challenges in modern electronics. One promising solution is liquid crystalline polyimides, a special class of polymers whose molecules naturally align into highly ordered structures.
These ordered chains create pathways for heat flow, making liquid crystalline polyimides highly attractive for thermal management in semiconductors, flexible displays, and next-generation devices. However, designing these polymers has long relied on trial and error because researchers lacked clear design rules to predict whether a polymer would form a liquid crystalline phase.
PCI-SIG today announced the PCI Express (PCIe) 8.0 specification will double the data of the PCIe 7.0 specification to 256.0 GT/s and is planned for release to members by 2028. “Following this year’s release of the PCIe 7.0 specification, PCI-SIG is excited to announce that the PCIe 8.0 specification will double the data rate to 256 GT/s, maintaining our tradition of doubling bandwidth every three years to support next-generation applications,” said Al Yanes, PCI-SIG President and Chairperson. “With the increasing data throughput required in AI and other applications, there remains a strong demand for high performance. PCIe technology will continue to deliver a cost-effective, high-bandwidth, and low-latency I/O interconnect to meet industry needs.”
In a groundbreaking step forward for polymer science and electronics cooling technology, researchers from Japan have leveraged artificial intelligence to identify a new class of liquid crystalline polyimides with remarkably high thermal conductivity. Their work, recently published in npj Computational Materials, combines data science, chemistry, and machine learning to accelerate the search for next-generation materials capable of efficiently dissipating heat in compact, high-performance electronics.
CRISPR technology has revolutionized biology, largely because of its simplicity compared to previous gene editing techniques. However, it still takes weeks to learn, design, perform, and analyze CRISPR experiments; first-time CRISPR users often end up with low editing efficiencies and even experts can make costly mistakes.
In a new study, researchers from Stanford University, Princeton University, and the University of California, Berkeley, teamed up with Google DeepMind to create CRISPR-GPT, an artificial intelligence (AI) tool that can guide researchers through every aspect of CRISPR editing from start to finish in as little as one day.1 The results, published in Nature Biomedical Engineering, demonstrate that researchers with no previous CRISPR experience could achieve up to 90 percent efficiency in their first shot at gene editing using the tool.
CRISPR-GPT is a large language model (LLM), a type of AI model that uses text-based input data. Led by Le Cong of Stanford University and Mengdi Wang of Princeton University, the team trained the model on over a decade of expert discussions, as well as established protocols and peer-reviewed literature. They designed it to cover gene knockout, base editing, prime editing, and epigenetic editing systems, and benchmarked the tool against almost 300 test questions and answers.