Nanoengineers at the University of California San Diego have developed microscopic robots, called microrobots, that can swim around in the lungs, deliver medication and be used to clear up life-threatening cases of bacterial pneumonia.
In mice, the microrobots safely eliminated pneumonia-causing bacteria in the lungs and resulted in 100% survival. By contrast, untreated mice all died within three days after infection.
The results are published Sept. 22 in Nature Materials.
Jülich researchers have been able to demonstrate an exotic electronic state, so-called Fermi Arcs, for the first time in a 2D material. The surprising appearance of Fermi arcs in such a material provides a link between novel quantum materials and their respective potential applications in a new generation of spintronics and quantum computing. The results have recently been published in Nature Communications.
The newly detected Fermi arcs represent special—arc-like—deviations from the so-called Fermi surface. The Fermi surface is used in condensed matter physics to describe the momentum distribution of electrons in a metal. Normally, these Fermi surfaces represent closed surfaces. Exceptions such as the Fermi arcs are very rare and often are associated with exotic properties like superconductivity, negative magnetoresistance and anomalous quantum transport effects.
Today’s technology challenge is to develop the “on-demand” control of physical properties in materials. However, such experimental tests have been largely limited to bulk materials and are key grand challenges in condensed matter science. With its groundbreaking paradigm, the findings present a promising new frontier for quantum control of topological states in low-dimensional systems by external means—the external magnetic field that offers unprecedented capabilities on 2D materials for artificial intelligence as well as future information processing.
PRESS RELEASE — There has been a lot of buzz about quantum computers and for good reason. The futuristic computers are designed to mimic what happens in nature at microscopic scales, which means they have the power to better understand the quantum realm and speed up the discovery of new materials, including pharmaceuticals, environmentally friendly chemicals, and more. However, experts say viable quantum computers are still a decade away or more. What are researchers to do in the meantime?
A new Caltech-led study in the journal Science describes how machine learning tools, run on classical computers, can be used to make predictions about quantum systems and thus help researchers solve some of the trickiest physics and chemistry problems. While this notion has been shown experimentally before, the new report is the first to mathematically prove that the method works.
“Quantum computers are ideal for many types of physics and materials science problems,” says lead author Hsin-Yuan (Robert) Huang, a graduate student working with John Preskill, the Richard P. Feynman Professor of Theoretical Physics and the Allen V. C. Davis and Lenabelle Davis Leadership Chair of the Institute for Quantum Science and Technology (IQIM). “But we aren’t quite there yet and have been surprised to learn that classical machine learning methods can be used in the meantime. Ultimately, this paper is about showing what humans can learn about the physical world.”
Military robotics technology is not far behind as our world becomes more advanced. If you have seen Corridor Digital’s parody video, you may know what the future will look like. Don’t worry; the realism of that video is a testament to the advancements in visual effects at the Los Angeles production studio, and not necessarily robotics.
But to be honest, we are not far behind, and in this video, we will explore a company and its line of robots that are leading the charge to make soldiers obsolete.
A new longevity focused venture capital fund is preparing to announce its first investments, as it seeks to accelerate commercialisation in the field. Joining the likes of Maximon, Apollo and Korify, New York’s Life Extension Ventures (LifeX) has put together a $100 million fund specifically for companies developing solutions to extend the longevity of both humans and our planet. In a slight twist, the fund is predominantly looking to invest in companies that are leveraging software and data at the heart of their efforts to hasten the adoption of scientific breakthroughs in longevity.
Longevity. Technology: The longevity field is alive with innovation, and developments in AI and Big Data are just some of the software-led technologies driving progress throughout the sector. Co-founded by scientists-turned-entrepreneurs, Amol Sarva and Inaki Berenguer, LifeX Ventures’ investment philosophy draws on their combined experiences building software-led companies across a wide range of sectors. We caught up with Sarva to learn more.
Between them Sarva, a cognitive scientist by training, and Berenguer have led and/or founded several startups, such as CoverWallet, Virgin Mobile USA and Halo Neuroscience. The two have also invested personally in more than 150 startups before their interest turned more recently to longevity.
“Ultimately, there’s going to be a more satisfying and possibly better solution that involves systems that do a better job of understanding the way the world works.”
Along the way, LeCun offers some withering views of his biggest critics, such as NYU professor Gary Marcus — “he has never contributed anything to AI” — and Jürgen Schmidhuber, co-director of the Dalle Molle Institute for Artificial Intelligence Research — “it’s very easy to do flag-planting.”
Beyond the critiques, the more important point made by LeCun is that certain fundamental problems confront all of AI, in particular, how to measure information.
A new report from the University of Technology Sydney (UTS) Human Technology Institute outlines a model law for facial recognition technology to protect against harmful use of this technology, but also foster innovation for public benefit.
Australian law was not drafted with widespread use of facial recognition in mind. Led by UTS Industry Professors Edward Santow and Nicholas Davis, the report recommends reform to modernize Australian law, especially to address threats to privacy and other human rights.
Facial recognition and other remote biometric technologies have grown exponentially in recent years, raising concerns about the privacy, mass surveillance and unfairness experienced, especially by people of color and women, when the technology makes mistakes.
Summary: Most AI models are unable to represent features of human vision, making them worse at recognizing images.
Source: HSE
Researchers from HSE University and Moscow Polytechnic University have discovered that AI models are unable to represent features of human vision due to a lack of tight coupling with the respective physiology, so they are worse at recognizing images.
Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations—all without sacrificing accuracy. The work, published in the September 23 issue of Physical Review Letters, could revolutionize how scientists investigate systems containing many interacting electrons. Moreover, if scalable to other problems, the approach could potentially aid in the design of materials with sought-after properties such as superconductivity or utility for clean energy generation.
“We start with this huge object of all these coupled-together differential equations; then we’re using machine learning to turn it into something so small you can count it on your fingers,” says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute’s Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy.
The formidable problem concerns how electrons behave as they move on a gridlike lattice. When two electrons occupy the same lattice site, they interact. This setup, known as the Hubbard model, is an idealization of several important classes of materials and enables scientists to learn how electron behavior gives rise to sought-after phases of matter, such as superconductivity, in which electrons flow through a material without resistance. The model also serves as a testing ground for new methods before they’re unleashed on more complex quantum systems.