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Computer Vision Extracts Lightning From Footage – Hackaday

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… but we can capture it in various photography thanks to this project which leverages machine learning to pull out the best frames of lightning.




Introducing AI-driven acoustic synthesis for AR and VR

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Whether it's mingling at a party in the metaverse or watching a home movie in your living room while wearing augmented reality (AR) glasses, acoustics play a role in how these moments will be experienced. We are building for mixed reality and virtual reality experiences like these, and we believe AI will be core to delivering sound quality that realistically matches the settings people are immersed in. Today, Meta AI researchers, in collaboration with an audio specialist from Meta's Reality Labs and researchers from the University of Texas at Austin, are open-sourcing three new models for audio-visual understanding of human speech and sounds in video that are designed to push us toward this reality at a faster rate. We need AI models that understand a person's physical surroundings based on both how they look and how things sound. For example, there's a big difference between how a concert would sound in a large venue versus in your living room.


How Artificial Intelligence Is Advancing Structural Proteomics

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OG: Biological processes are inherently very complicated and have their own mysteries, even for domain experts. For instance, to understand how interacting proteins attach to each other, humans or computers have to try out all possible attachment combinations in order to find the most plausible one. Intuitively, having two three-dimensional objects with very irregular surfaces, one has to rotate them and try to dock them in all possible ways until one can find two complementary regions on both surfaces that would match very well in terms of their geometric and chemical patterns. This is a very time-consuming process for both manual approaches and computational ones. Moreover, biologists are interested in discovering new interactions across a very large set of proteins such as the 20,000-sized human proteome.


Is Lifelong #machinelearning, a paradigm for continuous learning? - Pinaki Laskar on LinkedIn

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Is Lifelong #machinelearning, a paradigm for continuous learning? Real ML is Lifelong ML. Real ML is about lifelong and constant learning with the memory (knowledge base). Human beings always retain and accumulate the knowledge learned in the past and use it in future learning. Over time we learn more and become more knowledgeable, and more effective at learning.


Amazon.com: Deep Reinforcement Learning eBook : Plaat, Aske: Kindle Store

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These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.



Can reinforcement learning solve the NP-Hard problems?

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For an algorithm to be termed "efficient", its execution time must be constrained by a polynomial function of the input size. It was realised early on that not all issues could be handled thus rapidly, but it was difficult to determine which ones could and which couldn't. Some so-called NP-hard issues are thought to be impossible to answer in polynomial time. NP-hard stands for non-deterministic polynomial-time hardness. This article will be focused on understanding some NP-hard problems and trying to solve them with Reinforcement Learning. Following are the topics to be covered.


Data Engineer (Acuant/GBG Americas) (3194)

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With a rich heritage of more than 30 years, offices in 16 locations worldwide, and more than 1,000 team members globally, GBG proudly stands as the global technology specialist in fraud, location, and identity data intelligence. We support businesses and enable customer growth by protecting companies and governments to combat fraud and cybercrime, lower the cost of compliance, and improve customer digital onboarding experience in today's digital economy. Acuant is a fast-growing, leading provider of identity verification technology. Our Trusted Identity Platform enables businesses worldwide to fight fraud while effectively addressing evolving security concerns in our increasingly digital economy. Powered by proprietary technology, our platform provides the leading homegrown, end-to-end identity platform that was purposefully built to cover the complete customer journey.