| Название | : | Deep learning requires fundamentally new kinds of hardware | Jim Keller and Lex Fridman |
| Продолжительность | : | 7.08 |
| Дата публикации | : | |
| Просмотров | : | 13 rb |
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Can someone explain like I'm 5: "the future of software is data networksthe programs"brIs he literally saying the future of software is basically pools of already functioning data networks, networked together to form a larger solution? Like, his example of pixels running their programs, but the pixels themselves have no idea what the other pixels are doingand this will solve thatbrbrDid I understand that correctly? Comment from : smudge |
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what hes trying to say is the Von neumann architecture will not natively work with neural networks Comment from : James Coda |
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So this is the other way around Classically coders have to optimize their code to specific hardware But here they are optimizing hardware to run their (pytorch) programs most efficiently Comment from : Raman Monorath |
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Tenstorrent chip sounds like parallel processing running, interrelated, spatial threads Getting closer to how the human mind works it's only a matter of time now, bois Tenstorerent --> realizing the capability for Software 20 --> Skynet Comment from : Bill |
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Excellent sound tangible A tactical tactile sphere of coherence within symbiosis in biomorphic an molecular structures such as spectrums tone an form throughout (Singularity) Upgrade ! Comment from : Doug Baker |
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Fun fact: brbrbrbrJim Keller is brother in law of Jordan B Peterson Comment from : Miroslav Danilov |
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i learn more listening to mr keller than of my school Comment from : Tony Ly |
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Anyway we already live in a world of talking sand Magic eh? Comment from : Hoa Xuan |
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The problem with not getting the basics down 1oo With not doing the scientific methodology 100 from the ground up is that very expensive, elaborate hardware gets built When something far simpler would be better Comment from : Hoa Xuan |
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A fast Hadamard tranform chip could be made using a few thousand transistors Even a neural network chip based on that would need less transistors (56000) than a Z80 CPU from the 1970s Eg Fast Transform fixed filter bank neural networks Comment from : Hoa Xuan |
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AI is going to be born in Canada Comment from : Darcy Mcnabb |
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My favourite topic AI Accelerators and ML on Chip Comment from : Bhuvanesh sk |
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I don’t even know what just happened Comment from : Mark Jackson |
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I feel dumber just from listening to him talk the IQ of everyone listening just dropped 50 points brbrbrJk Comment from : drhilltube |
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Jim and Lex always have really great discussions Comment from : John Kost |
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It's great explanation ! Comment from : Saumya |
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Brain emulation for deep learning!!!! Comment from : RD |
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Jensen says you just need to buy more 3090s Comment from : Bob Too |
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