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[+] 7 points 1 year ago* (1 child)
  • [–] 6 points 1 year ago

    It's vital that America wins by racing ahead and winning developers worldwide," he added.

    "vital that America wins" is main reason China will win. US is only interested in Skynet/surveillance system for Israel first control over population as the customers for AI. Musk needs MechaHitler to destroy Tesla sales, and control a robot army for fascist extremist political power.

    The entire vision of "America must win" is the usual militarist throwing money at oligarchs philosophy. OpenAI confidently declaring US government should guarantee its impossible loans to help it. "winning developers worldwide" = CUDA.. I don't hear Jensen saying he should share CUDA with other US firms: AMD, qualcom, apple, intel.

    Oligarchs are just saying they should get all the money or China(Russia, Iran...) will win is a US governance mantra that ensures their collapse. A country on the verge of collapse is incapable of reaching Skynet. There will be a bubble in everything else, if all US wealth is thrown at Skynet oligarchy. Already, skynet ambitions is forcing high ram prices for everyone else. High electricity prices in US. Mass layoffs.

    China is extremely far ahead in robotics and self driving. Their open LLM models are more useful with more global downloads than US. They are certain to win because they have energy capacity, and a manufacturing sector that is the only consumer of useful AI/robotics. They need tech advancement for survival instead of oligarchist plunder of what is still treated as unlimited money, that still mostly gets funneled through TSMC.

    China has much more research/education levels. It already has all the NVIDIA gpus it wants through black market at prices equal to US, and it packages about half of NVIDIA gpus.

    Where AI can help a human/business user help humanity boost production/productivity, Chinese research efforts will help. Skynet will just destroy and extort humanity for those who control AI to control humanity. It is naturally less trustworthy to rely on the openly evil, than on open models.

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  • [–] 6 points 1 year ago*

    Inshallah Mashallah

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  • [–] 4 points 1 year ago (4 children)

    Ofc they are. They are winning most things right now.

    Chinas "weakness" is being reliant on being a production hub for the rest of the world.

    And Trump tries to MOVE production to the US, so China can take over the dominant role.

    I cant even.

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  • [–] [S] 2 points 1 year ago (2 children)

    It's like watching a grand master play chess with a toddler.

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  • 9 comments in c/technology@lemmy.zip view that post
    [–] 36 points 1 year ago (2 children)

    He just wants to sell chips to china is what I got from that article.

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  • [–] 9 points 1 year ago

    Yeah, he’s desperate to have the US allow him to legally sell the high end GPUs directly to Chinese customers as opposed to the current system they use with lots of middlemen.

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  • [–] 26 points 1 year ago (1 child)

    Despite funneling a huge chunk of the US GDP into it? Kinda feels like maybe AI is a scam, then.

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    [–] 23 points 1 year ago

    Let them? That way they're holding the bag when it implodes.

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  • [–] 11 points 1 year ago (1 child)

    How do you "win" a tech / investment bubble? Is it a race to see whose economy implodes first?

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  • You win by acknowledging that AI/Machine Learning research has existed long before this bubble existed, and is continuing to happen outside of that bubble. Most of what we call AI nowadays is based on neural networks (that's what Geoffrey Hinton and others got a recent nobel prize for), but that's not the only way to go about the problem, and for years now, there have been researchers pointing out problems like hallucinations and diminishing returns from increasing the amount of data you feed to a model.

    An example of one such researcher is Song Chun-Zhu, who has recently moved back to China because he was finding it increasingly difficult to do research he wanted (i.e. outside of the current AI bubble) within the US. That linked article is a bit of a puff piece, in that it is a tad too mythologising of him, but I think he's a good example of what productive AI research looks like — especially because he used to work on the "big data" kind of AI, before realising its inherent limits and readjusting his approach accordingly.

    He's one of the names that's on my watch list because even for people who aren't directly building on his research, he comes up a lot in research that is also burnt out on neural nets

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  • [+] 5 points 1 year ago (1 child)