What’s cooking in the AI labs of Google and Apple?
New rumors claim that Apple’s generative AI technology will be included in Siri not just locally on iPhones, but also integrated into other services. Whereas, Google will upgrade Bard with more ‘advanced’ capabilities.
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One of the biggest concerns about EVs is that the batteries will need replacing after a few years, at great expense. After all, your smartphone battery is likely to have seen better days within as little as three years. But a Tesla researcher is getting ready to kick this idea into touch once and for all, after demonstrating batteries that could potentially outlive most human beings.
Tesla enthusiasts are likely to have heard of Jeff Dahn already. He’s a professor at Dalhousie University and has been a research partner with Tesla since 2016. His focus has been to increase the energy density and lifetime of lithium-ion batteries, as well as reducing their cost. Dahn appears to have hit the motherload along with colleagues on his research team. In a paper published in the Journal of the Electrochemical Society, the group claims to have created a battery design that could last 100 years under the right conditions.
Continue reading “Tesla Researcher Demonstrates 100-Year, 4-Million-Mile Battery” »
😀 They say we could even regenerate human limbs this way aswell as repair human blood vessels.
Cell tubes, made entirely from a patient’s own cells, are just as elastic as blood vessels but much stronger. Skin cells cultured into lumps are skewered on needles on a base, similar to a Kenzan, a tool used in Japanese flower arrangements, and formed into a tube. The technique, called the Kenzan Method, was made possible by a 3D bioprinter. A clinical trial is underway in Japan to transplant these tubes into humans in place of blood vessels. Studies are being done to apply them to nerves and organs.
The required precision to perform quantum simulations beyond the capabilities of classical computers imposes major experimental and theoretical challenges. The key to solving these issues are highly precise ways of characterizing analog quantum sim ulators. Here, we robustly estimate the free Hamiltonian parameters of bosonic excitations in a superconducting-qubit analog quantum simulator from measured time-series of single-mode canonical coordinates. We achieve the required levels of precision in estimating the Hamiltonian parameters by maximally exploiting the model structure, making it robust against noise and state-preparation and measurement (SPAM) errors. Importantly, we are also able to obtain tomographic information about those SPAM errors from the same data, crucial for the experimental applicability of Hamiltonian learning in dynamical quantum-quench experiments. Our learning algorithm is highly scalable both in terms of the required amounts of data and post-processing. To achieve this, we develop a new super-resolution technique coined tensorESPRIT for frequency extraction from matrix time-series. The algorithm then combines tensorESPRIT with constrained manifold optimization for the eigenspace reconstruction with pre-and post-processing stages. For up to 14 coupled superconducting qubits on two Sycamore processors, we identify the Hamiltonian parameters — verifying the implementation on one of them up to sub-MHz precision — and construct a spatial implementation error map for a grid of 27 qubits. Our results constitute a fully characterized, highly accurate implementation of an analog dynamical quantum simulation and introduce a diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.
Submitted 18 Aug 2021 to Quantum Physics [quant-ph]
Subjects: quant-ph cond-mat.quant-gas physics.comp-ph.
One year after initial deliveries of solid-state battery prototypes to its automotive partners, QuantumScape is receiving additional praise from PowerCo – the battery-centric subsidiary of Volkswagen Group – for the potential of its technology. PowerCo recently completed an endurance test with QuantumScape’s solid-state cells and determined they can someday power EVs that can drive 500,000 kilometers with virtually no loss of range.
QuantumScape ($QS) is an advanced battery technology company that has been working for over a decade to develop scalable, energy-dense solid-state battery cells that can one-day power EVs that are safer, charge faster, and drive farther.
During QuantumScape’s tenure in solid-state battery development, Volkswagen Group has been a partner from early on and remains one of the startup’s largest investors. OEMs like Volkswagen have helped empower QuantumScape to continue its development and deliver some of the most promising solid-state battery technology in the industry.
Kepler asserts that its general-purpose Forerunner series showcases advanced capabilities in body movements, precise hand control, and sophisticated visual perception. This positions it as a formidable competitor to Tesla’s Optimus in the realm of humanoid robotics.
The firm claims that its humanoid robot aims to enhance “productivity with cutting-edge technology, hastening the arrival of a ‘three-day work week.’ The shift will enable humans to dedicate more time to meaningful endeavors, such as space exploration,” said Debo Hu, co-founder of the firm, in a statement.
A study found that men were largely unaware of the risks to their fertility from aspects of gym lifestyle including protein supplements, used by 79% of male respondents.
How deeply someone can be hypnotized — known as hypnotizability — appears to be a stable trait that changes little throughout adulthood, much like personality and IQ. But now, for the first time, Stanford Medicine researchers have demonstrated a way to temporarily heighten hypnotizablity — potentially allowing more people to access the benefits of hypnosis-based therapy.
In the new study, published Jan. 4 in Nature Mental Health, the researchers found that less than two minutes of electrical stimulation targeting a precise area of the brain could boost participants’ hypnotizability for about one hour.
“We know hypnosis is an effective treatment for many different symptoms and disorders, in particular pain,” said Afik Faerman, PhD, a postdoctoral scholar in psychiatry and lead author of the study. “But we also know that not everyone benefits equally from hypnosis.”
The required precision to perform quantum simulations beyond the capabilities of classical computers imposes major experimental and theoretical challenges. The key to solving these issues are highly precise ways of characterizing analog quantum sim ulators. Here, we robustly estimate the free Hamiltonian parameters of bosonic excitations in a superconducting-qubit analog quantum simulator from measured time-series of single-mode canonical coordinates. We achieve the required levels of precision in estimating the Hamiltonian parameters by maximally exploiting the model structure, making it robust against noise and state-preparation and measurement (SPAM) errors. Importantly, we are also able to obtain tomographic information about those SPAM errors from the same data, crucial for the experimental applicability of Hamiltonian learning in dynamical quantum-quench experiments. Our learning algorithm is highly scalable both in terms of the required amounts of data and post-processing. To achieve this, we develop a new super-resolution technique coined tensorESPRIT for frequency extraction from matrix time-series. The algorithm then combines tensorESPRIT with constrained manifold optimization for the eigenspace reconstruction with pre-and post-processing stages. For up to 14 coupled superconducting qubits on two Sycamore processors, we identify the Hamiltonian parameters — verifying the implementation on one of them up to sub-MHz precision — and construct a spatial implementation error map for a grid of 27 qubits. Our results constitute a fully characterized, highly accurate implementation of an analog dynamical quantum simulation and introduce a diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.
