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At last year’s CES, Nvidia-watchers had plenty to celebrate at CES this week, with news that the company\u2019s latest GPU, Vera Rubin, is now fully in production. Those powerful AI chips\u2014the picks and shovels of the AI boom\u2014are, after all, what helped make Nvidia the world\u2019s most valuable company.<\/p>\n
But in his keynote address, CEO Jensen Huang once again made clear that Nvidia does not see itself as simply a chip company. It is also a software company, with its reach extending across nearly every layer of the AI stack\u2014and with a major bet on physical AI: AI systems that operate in the real world, including robotics and self-driving cars.<\/p>\n
In a press release touting Nvidia\u2019s CES announcements, a quote attributed to Huang declared that \u201cthe ChatGPT moment for robotics is here.\u201d Breakthroughs in physical AI\u2014models that understand the real world, reason, and plan actions\u2014\u201care unlocking entirely new applications,\u201d he said.<\/p>\n
In the keynote itself, however, Huang was more measured, saying the ChatGPT moment for physical AI is \u201cnearly here.\u201d It might sound like splitting hairs, but the distinction matters\u2014especially given what Huang said at last year\u2019s CES, when he introduced Nvidia\u2019s Cosmos world platform and described robotics\u2019 \u201cChatGPT moment\u201d as merely \u201caround the corner.\u201d<\/p>\n
So has that moment really arrived, or is it still stubbornly out of reach?<\/p>\n
Huang himself seemed to acknowledge the gap. \u201cThe challenge is clear,\u201d he said in yesterday\u2019s keynote. \u201cThe physical world is diverse and unpredictable.\u201d<\/p>\n
Nvidia is also no flash in the pan when it comes to physical AI. Over the past decade, the company has laid the groundwork by developing an ecosystem of AI software, hardware, and simulation systems for robots and autonomous vehicles. But it has never been about building its own robots or AVs. As Rev Lebaredian, Nvidia\u2019s vice president of simulation technology, told Fortune<\/em> last year, the strategy is still about supplying the picks and shovels.<\/p>\n There\u2019s no doubt that Nvidia has progressed in that regard over the past year. On the self-driving front, today it unveiled the Alpamayo family of open AI models, simulation tools and datasets meant to help AVs safely operate across a range of rare, complex driving scenarios, which are considered the some of the toughest challenges for autonomous systems to safely master. <\/p>\n Nvidia also released new Cosmos and GR00T open models and data for robot learning and reasoning, and touted companies including Boston Dynamics, Caterpillar, Franka Robots, Humanoid, LG Electronics and NEURA Robotics, which are debuting new robots and autonomous machines built on Nvidia technologies.<\/p>\n Even with increasingly capable models, simulation tools, and computing platforms, Nvidia is not building the self-driving cars or the robots themselves. Automakers still have to turn those tools into systems that can safely operate on public roads\u2014navigating regulatory scrutiny, real-world driving conditions, and public acceptance. Robotics companies, meanwhile, must translate AI into machines that can reliably manipulate the physical world, at scale, and at a cost that makes commercial sense.<\/p>\n That work\u2014integrating hardware, software, sensors, safety systems, and real-world constraints\u2014remains enormously difficult, slow, and capital-intensive. And it\u2019s far from clear that faster progress in AI alone is enough to overcome those hurdles. After all, the ChatGPT moment wasn\u2019t just about the model under the hood. Those had existed for several years. It was about the user experience and a company that was able to capture lightning in a bottle.\u00a0<\/p>\n Nvidia has captured lightning in a bottle before\u2014GPUs turned out to be the unlikely but perfect engine for modern AI. Whether that kind of luck can be repeated in physical AI, a far messier and less standardized domain, is still an open question.<\/p>\n This story was originally featured on Fortune.com<\/p>\n #year #Nvidias #Jensen #Huang #ChatGPT #moment #robotics #corner<\/p>\n","protected":false},"excerpt":{"rendered":" At last year’s CES, Nvid… <\/p>\n","protected":false},"author":1,"featured_media":9883,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2],"tags":[725,7475,5555,5554,3097,1056,955,951,85],"_links":{"self":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts\/9882"}],"collection":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=9882"}],"version-history":[{"count":0,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts\/9882\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/media\/9883"}],"wp:attachment":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9882"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9882"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9882"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}