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MIT has launched a new academia and industry partnership called the AI Hardware Program that aims to boost research and development.

“A sharp focus on AI hardware manufacturing, research, and design is critical to meet the demands of the world’s evolving devices, architectures, and systems,” says Anantha Chandrakasan, dean of the MIT School of Engineering, and Vannevar Bush Professor of Electrical Engineering and Computer Science.

The first all-civilian crew arrived at the International Space Station after a historic launch with SpaceX and Axiom Space. The crew will spend eight days partaking in science experiments and philanthropic projects. » Subscribe to NBC News: http://nbcnews.to/SubscribeToNBC
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It relies on a new “freeze-thaw” design. A recent study has just been published by U.S. scientists who have managed to develop an aluminum-nickel molten salt battery that can retain over 90% of its initial capacity over a period of up to 12 weeks. Having an energy density of 260 W/hour per kg, the new battery was built with an aluminum anode and a nickel cathode, immersed in a molten-salt electrolyte.

The breakthrough could have many applications in soft robotics including in the Metaverse.

Today Amazon and The Johns Hopkins University announced the creation of the JHU + Amazon Initiative for Interactive AI (AI2AI). The collaboration will focus on … See more.

Amazon and Johns Hopkins University (JHU) today announced the creation of the JHU + Amazon Initiative for Interactive AI (AI2AI).

The Amazon-JHU collaboration will focus on driving ground-breaking AI advances with an emphasis on machine learning, computer vision, natural language understanding, and speech processing. Sanjeev Khudanpur, an associate professor in the Department of Electrical and Computer Engineering, will serve as the founding director of the initiative.

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In a laboratory under a mountain, physicists are using crystals far colder than frozen air to study ghostly particles, hoping to learn secrets from the beginning of the universe. Researchers at the Cryogenic Underground Observatory for Rare Events (CUORE) announced this week that they had placed some of the most stringent limits yet on the strange possibility that the neutrino is its own antiparticle. Neutrinos are deeply unusual particles, so ethereal and so ubiquitous that they regularly pass through our bodies without us noticing. CUORE has spent the last three years patiently waiting to see evidence of a distinctive nuclear decay process, only possible if neutrinos and antineutrinos are the same particle. CUORE’s new data shows that this decay doesn’t happen for trillions of trillions of years, if it happens at all. CUORE’s limits on the behavior of these tiny phantoms are a crucial part of the search for the next breakthrough in particle and nuclear physics—and the search for our own origins.

“Ultimately, we are trying to understand matter creation,” said Carlo Bucci, researcher at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy and the spokesperson for CUORE. “We’re looking for a process that violates a fundamental symmetry of nature,” added Roger Huang, a postdoctoral researcher at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and one of the lead authors of the new study.

CUORE—Italian for “heart”—is among the most sensitive neutrino experiments in the world. The new results from CUORE are based on a data set ten times larger than any other high-resolution search, collected over the last three years. CUORE is operated by an international research collaboration, led by the Istituto Nazionale di Fisica Nucleare (INFN) in Italy and Berkeley Lab in the US. The CUORE detector itself is located under nearly a mile of solid rock at LNGS, a facility of the INFN. U.S. Department of Energy-supported nuclear physicists play a leading scientific and technical role in this experiment. CUORE’s new results were published today in Nature.

Throughout the past decade, several metaphors and labels have evolved to describe the software that curates the data storage. Some were called warehouses; they generally offered stronger structure and compliance, but they were often unable to manage the larger volumes of information from modern web applications. Another term, the “data lake,” referred to less structured collections that were engineered to scale easily, in part because they enforced fewer rules. Google wants BigLake to offer the control of the best data warehouses with the seemingly endless availability of cloud storage.

“All of these organizations who try to innovate on top of the data lake found it to be, at the end of the day, just a data swamp,” said Kazmaier. “Our innovation at Google Cloud is that we take BigQuery and its unique architecture, its unique Serverless model, its unique storage architecture and a unique compute architecture and [integrate it] with open-source file formats and open-source processing engines.”

The open-source architecture is intended to allow customers to adopt Google’s tools slowly through integration with existing data infrastructure. These open formats simplify sharing information, making it a more welcoming environment.

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Breakthrough techniques in living cells upend field.

Two studies provide a radically new picture of how bacterial cells continually repair damaged sections (lesions) in their DNA.

Led by researchers from NYU Grossman School of Medicine, the work revolves around the delicacy of DNA molecules, which are vulnerable to damage by reactive byproducts of cellular metabolism, toxins, and ultraviolet light. Given that damaged DNA can result in detrimental DNA code changes (mutations) and death, cells evolved to have DNA repair machineries. A major unresolved question in the field, however, is how do these machineries rapidly search for and find rare stretches of damage amid the “vast fields” of undamaged DNA.

In recent years, large neural networks trained for language understanding and generation have achieved impressive results across a wide range of tasks. GPT-3 first showed that large language models (LLMs) can be used for few-shot learning and can achieve impressive results without large-scale task-specific data collection or model parameter updating. More recent LLMs, such as GLaM, LaMDA, Gopher, and Megatron-Turing NLG, achieved state-of-the-art few-shot results on many tasks by scaling model size, using sparsely activated modules, and training on larger datasets from more diverse sources. Yet much work remains in understanding the capabilities that emerge with few-shot learning as we push the limits of model scale.

Last year Google Research announced our vision for Pathways, a single model that could generalize across domains and tasks while being highly efficient. An important milestone toward realizing this vision was to develop the new Pathways system to orchestrate distributed computation for accelerators. In “PaLM: Scaling Language Modeling with Pathways”, we introduce the Pathways Language Model (PaLM), a 540-billion parameter, dense decoder-only Transformer model trained with the Pathways system, which enabled us to efficiently train a single model across multiple TPU v4 Pods. We evaluated PaLM on hundreds of language understanding and generation tasks, and found that it achieves state-of-the-art few-shot performance across most tasks, by significant margins in many cases.

That’s quite the claim.