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UC San Diego Researchers DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
UC San Diego researchers have developed a new framework called DYffusion for spatiotemporal forecasting using a diffusion model. The framework incorporates a temporal inductive bias to reduce learning times and memory requirements. It produces accurate probabilistic ensemble predictions for high-dimensional data and outperforms traditional Gaussian diffusion models. The researchers also compare the computational requirements and…
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Bundesliga Match Facts Shot Speed – Who fires the hardest shots in the Bundesliga?
The Bundesliga has introduced a new metric called Shot Speed to provide insights into the velocity behind soccer shots. Shot speed is calculated using event data and optical tracking data to determine the maximum speed the ball reaches during its flight. This metric not only enhances our understanding of the game but also highlights memorable…
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Google AI Introduces Spectron: The First Spoken Language AI Model that is Trained End-to-End to Directly Process Spectrograms as Both Input and Output
Google AI has introduced a new spoken language model called “Spectron” that processes spectrograms as both input and output. Spectrograms are visual representations of the spectrum of frequencies of a signal. The model uses pre-trained encoders and decoders to transcribe and generate text and speech continuations, improving the quality of synthesized speech. However, the model…
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Apple Researchers Introduce Matryoshka Diffusion Models(MDM): An End-to-End Artificial Intelligence Framework for High-Resolution Image and Video Synthesis
Apple researchers have introduced Matryoshka Diffusion Models (MDM), a family of diffusion models designed for high-resolution image and video synthesis. MDM utilizes a Nested UNet architecture in a multi-resolution diffusion process to process and produce images with varying levels of detail. The training plan progresses gradually to higher resolutions, demonstrating robust zero-shot generalization and high-quality…
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Infinitely scalable storage for Kubernetes
This text discusses the installation and use of Rook Ceph as a replicated storage class for Kubernetes clusters. It provides step-by-step instructions on how to deploy Rook Ceph, create storage classes, deploy a file-sharing app, and test the resiliency of the storage solution. The article concludes by highlighting the scalability and reliability of Rook Ceph…
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Cracking the Code LLMs
This article discusses the evolution of Large Language Models (LLMs) for code, from RNNs to Transformers. It covers the development of models like Code2Vec, CodeBERT, Codex, CodeT5, PLBART, and the latest model, Code Llama. These models have advanced code understanding and generation tasks, improving programming efficiency.
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A New AI Research from China Introduces GLM-130B: A Bilingual (English and Chinese) Pre-Trained Language Model with 130B Parameters
Researchers from Tsinghua University and Zhipu.AI have released an open-source bilingual language model called GLM-130B with 130B parameters. GLM-130B outperforms GPT-3 and PaLM on various benchmarks, achieving a zero-shot accuracy of 80.2% on LAMBADA. The researchers also shared their training process and experiences, highlighting their commitment to transparency in language model development.
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AI should be better understood and managed — new research warns
According to an academic, Artificial Intelligence (AI) and algorithms have the potential to fuel racism, political instability, polarization, and radicalization. These technologies, which are not limited to national security agencies, can contribute to political violence and pose a threat to national security.
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Researchers from UC Berkeley and Stanford Introduce the Hidden Utility Bandit (HUB): An Artificial Intelligence Framework to Model Learning Reward from Multiple Teachers
The HUB framework, developed by researchers from UC Berkeley and Stanford, addresses the challenge of integrating human feedback into reinforcement learning systems. It introduces a structured approach to teacher selection, actively querying teachers to enhance the accuracy of utility function estimation. The framework has shown promise in real-world domains such as paper recommendations and COVID-19…
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Nanowire ‘brain’ network learns and remembers ‘on the fly’
A physical neural network has achieved a milestone in machine intelligence by learning and retaining information in a manner similar to human brain neurons. This breakthrough paves the way for the development of efficient and low-energy machine intelligence for complex real-world learning and memory tasks.