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Oct 4, 2023

Nano-mechanoelectrical approach increases DNA detection sensitivity by 100 times

Posted by in categories: bioengineering, biotech/medical, health, nanotechnology

UMass Amherst researchers have pushed forward the boundaries of biomedical engineering one hundredfold with a new method for DNA detection with unprecedented sensitivity.

“DNA detection is in the center of bioengineering,” says Jinglei Ping, lead author of the paper that appeared in Proceedings of the National Academy of Sciences.

Ping is an assistant professor of mechanical and , an adjunct assistant professor in and affiliated with the Center for Personalized Health Monitoring of the Institute for Applied Life Sciences. “Everyone wants to detect the DNA at a low concentration with a high sensitivity. And we just developed this method to improve the sensitivity by about 100 times with no cost.”

Oct 4, 2023

Mustafa Prize winner: Iran pioneer in nanotechnology, its medical advances amazing

Posted by in categories: biotech/medical, nanotechnology

An internationally-renowned Iranian scientist and this year’s winner of Iran’s prestigious Mustafa Prize for science and technology has hailed the country’s great advances in the fields of nanotechnology and medicine.

“Iran has always been far ahead in the field of nanotechnology,” Omid Farokhzad, who has won the prize for design, development, and clinical translation of novel polymeric nanomedicines used to treat various diseases, especially cancer, said on Monday.

Oct 4, 2023

JWST takes a jab at the mystery of the universe’s expansion rate

Posted by in category: futurism

JWST’s new detections are the most precise of their kind, but they don’t clear up a baffling puzzle about the cosmos.

Oct 4, 2023

Navigating the risks and benefits of AI: Lessons from nanotechnology on ensuring emerging technologies are safe as well as successful

Posted by in categories: nanotechnology, robotics/AI

Two decades ago, the nanotechnology revolution avoided stumbling by bringing a wide range of people to the table to chart its development. The window is closing fast on AI following suit.

Oct 4, 2023

IonQ Announces 2 New Quantum Systems; Suggests Quantum Advantage is Nearing

Posted by in categories: business, particle physics, quantum physics, robotics/AI

It’s been a busy week for IonQ, the quantum computing start-up focused on developing trapped-ion-based systems. At the Quantum World Congress today, the company announced two new systems (Forte Enterprise and Tempo) intended to be rack-mountable and deployable in a traditional data center. Yesterday, speaking at Tabor Communications (HPCwire parent organization) HPC and AI on Wall Street conference, the company made a strong pitch for reaching quantum advantage in 2–3 years, using the new systems.

If you’ve been following quantum computing, you probably know that deploying quantum computers in the datacenter is a rare occurrence. Access to the vast majority NISQ era computers has been through web portals. The latest announcement from IonQ, along with somewhat similar announcement from neutral atom specialist QuEra in August, and increased IBM efforts (Cleveland Clinic and PINQ2) to selectively place on-premise quantum systems suggest change is coming to the market.

IonQ’s two rack-mounted solutions are designed for businesses and governments wanting to integrate quantum capabilities within their existing infrastructure. “Businesses will be able to harness the power of quantum directly from their own data centers, making the technology significantly more accessible and easy to apply to key workflows and business processes,” reported the company. IonQ is calling the new systems enterprise-grade. (see the official announcement.)

Oct 3, 2023

Early detection of pediatric cancer thanks to artificial intelligence

Posted by in categories: biotech/medical, genetics, robotics/AI

Discover how THALES collaborates with the CNRS to identify new genetic markers leading to the development of pediatric cancers, thereby contributing to the improvement of patient care.

Oct 3, 2023

Where did the brain come from?

Posted by in category: neuroscience

Scientists discovered a large repository of brain genes in a sea sponge. Why would an ancient, porous blob of cells contain neural genes?

Oct 3, 2023

GPT-4 “crushes” other LLMs according to new benchmark suite

Posted by in category: robotics/AI

Benchmarks are a key driver of progress in AI. But they also have many shortcomings. The new GPT-Fathom benchmark suite aims to reduce some of these pitfalls.

Benchmarks allow AI developers to measure the performance of their models on a variety of tasks. In the case of language models, for example, answering knowledge questions or solving logic tasks. Depending on its performance, the model receives a score that can then be compared with the results of other models.

These benchmarking results form the basis for further research decisions and, ultimately, investments. They also provide information about the strengths and weaknesses of individual methods.

Oct 3, 2023

Chipotle robots may soon construct your salads and bowls

Posted by in category: robotics/AI

Chipotle announces that robots will soon be assembling bowls and salads.

Oct 3, 2023

Scaling up learning across many different robot types

Posted by in category: robotics/AI

Robots are great specialists, but poor generalists. Typically, you have to train a model for each task, robot, and environment. Changing a single variable often requires starting from scratch. But what if we could combine the knowledge across robotics and create a way to train a general-purpose robot?

Today, we are launching a new set of resources for general-purpose robotics learning across different robot types, or embodiments. Together with partners from 33 academic labs we have pooled data from 22 different robot types to create the Open X-Embodiment dataset. We also release RT-1-X, a robotics transformer (RT) model derived from RT-1 and trained on our dataset, that shows skills transfer across many robot embodiments.

In this work, we show training a single model on data from multiple embodiments leads to significantly better performance across many robots than those trained on data from individual embodiments. We tested our RT-1-X model in five different research labs, demonstrating 50% success rate improvement on average across five different commonly used robots compared to methods developed independently and specifically for each robot. We also showed that training our visual language action model, RT-2, on data from multiple embodiments tripled its performance on real-world robotic skills.