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.

Amazon’s sponsorship of AI2AI, which will be housed in JHU’s Whiting School of Engineering, underscores its commitment to partnering with academia to address the most complex challenges in Al, democratizing access to the benefits of Al innovations, and broadening participation in research from diverse, interdisciplinary scholars, and other innovators.

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.