,,CayZCN87H4g,UCNNardZimg4BbwZmrTkjPOw, Religion, channel_UCNNardZimg4BbwZmrTkjPOw, video_CayZCN87H4g,Atheist gets same results as Todd White:
https://www.youtube.com/watch?v=jf6qkgliL9k
American Gospel TV:
https://www.youtube.com/@UC6cKwH8WYWgnowOr3469euw
______________________
➡️➡️➡️ Find all of Justin's essential links here: https://linktr.ee/justinpetersmin
,1,Links to some good churches in the DFW area:
Countryside Bible Church
https://countrysidebible.org
Metro Bible Church
https://www.metrobible.org
Trinity Bible Church
https://www.trinitybibledallas.org/about-us
______________________
➡️➡️➡️ Find all of Justin's essential links here: https://linktr.ee/justinpetersmin
,1,Sherman Bible Church:
https://shermanbible.com
Countryside Bible Church in Southlake:
https://countrysidebible.org
______________________
➡️➡️➡️ Find all of Justin's essential links here: https://linktr.ee/justinpetersmin
,1,Watch Full Podcast Here: https://youtu.be/HzgFE8XIVZY
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Listen to the Show on all Podcast Apps "Club Random with Bill Maher" https://podcasts.apple.com/us/podcast/club-random-with-bill-maher/id1613459129
EARLY RELEASE!!! HAPPY NEW YEAR! The great SETH MacFARLANE and Bill discuss the hilarious Norah Jones moment in Seth’s movie, Bill’s article on Seth in Vanity Fair, David Mamet’s gift for Bill, Bill’s childhood snobbiness about animation, Seth’s favorite Superman joke, Seth being compared to Jay Gatsby, when Seth saw Bill’s appearance on the TV show Alice, the time Bill got booed at a Dodger game, how animated characters get away with more, why the guys slowed down Tweeting, the good and the bad of A.I., the time Seth and Bill sang to Jay Leno, and so much more.
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,1,A Google Algorithms Seminar TechTalk, presented by Ziming Liu, 2024-06-04
ABSTRACT: Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.
ABOUT THE SPEAKER: Ziming Liu is a fourth-year PhD student at MIT & IAIFI, advised by Prof. Max Tegmark. His research interests lie in the intersection of AI and physics (science in general):
Physics of AI: “AI as simple as physics”
Physics for AI: “AI as natural as physics”
AI for physics: “AI as powerful as physicists”
He publishes papers both in top physics journals and AI conferences. He serves as a reviewer for Physcial Reviews, NeurIPS, ICLR, IEEE, etc. He co-organized the AI4Science workshops. His research have strong interdisciplinary nature, e.g., Kolmogorov-Arnold networks (Math for AI), Poisson Flow Generative Models (Physics for AI), Brain-inspired modular training (Neuroscience for AI), understanding Grokking (physics of AI), conservation laws and symmetries (AI for physics).
,1,Explore the intertwined histories and cultures of the major religions: Hinduism, Judaism, Buddhism, Christianity and Islam.
--
It's perfectly human to grapple with questions, like 'Where do we come from?' and 'How do I live a life of meaning?' These existential questions are central to the five major world religions -- and that's not all that connects these faiths. John Bellaimey explains the intertwined histories and cultures of Hinduism, Judaism, Buddhism, Christianity and Islam.
Lesson by John Bellaimey, animation by TED-Ed.
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,1,This episode is sponsored by Oracle. AI is revolutionizing industries, but needs power without breaking the bank. Enter Oracle Cloud Infrastructure (OCI): the one-stop platform for all your AI needs, with 4-8x the bandwidth of other clouds. Train AI models faster and at half the cost. Be ahead like Uber and Cohere.
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In episode #159 of Eye on AI, Craig Smith sits down with Peter Chen, the co-founder and CEO of Covariant, in a deep dive into the world of AI-driven robotics.
Peter shares his journey from his early days in China to his pivotal role in shaping the future of AI at Covariant. He discusses the philosophies that guided his work at OpenAI and how these have influenced Covariant's mission in robotics.
This episode unveils how Covariant is harnessing AI to build foundational models for robotics, discussing the intersection of reinforcement learning, generative models, and the broader implications for the field. Peter elaborates on the challenges and breakthroughs in developing AI agents that can operate in dynamic, real-world environments, providing insights into the future of robotics and AI integration.
Join us for this insightful conversation, where Peter Chen maps out the evolving landscape of AI in robotics, shedding light on how Covariant is pushing the boundaries of what's possible.
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(00:00) Preview and Introduction
(03:10) Peter Chen Journey in AI
(09:53) The Evolution of Generative AI and Transformer Models
(12:21) The Concept of World Models in AI
(14:03) Building Robust Role Models in AI
(20:48) Training AI: From Video Analysis to Real-World Interaction
(23:10) The Three Pillars of Building a Robotic Foundation Model
(27:36) Architectural Insights of Covariant's Foundation Model
(33:20) Adapting AI Models to Diverse Hardware
(35:01) The Future of Robotics: Progress and Potential
(38:55) Real-World Application and Future of AI-Controlled Robots
(42:11) Envisioning the Future of Automated Warehouses
(45:51) The Evolution of Robotics: Current Trends and Future Prospects