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宇树CEO:GPT-6发布后具身智能突破预期缩短至1-2年

Physical AI + Humanoids are the next progression of AI.

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具身智能是物理AI的核心方向,GPT-6对行业时间表的显著压缩效应值得开发者关注,建议跟进相关技术路线。

Physical AI + Humanoids are the next progression of AI.

物理AI与类人机器人是AI的下一个发展阶段。

$NVDA CES 2025 projections and Elon's prediction on $TSLA + 1 Billion humanoids in the next decade, seems to be speeding up.

$NVDA在CES 2025上的预测以及Elon对$TSLA的预测——未来十年内将推出10亿个类人机器人,这一进程似乎正在加速。

New statement from Unitree CEO after testing GPT-6 on their humanoid program was that:

宇树科技CEO在对其人形机器人程序测试GPT-6后发表的新声明如下:

  • In August, before GPT-6's release, Wang said the industry could take two to three years at its fastest pace, or as long as a decade, to reach its "ChatGPT moment"
  • Now, OpenAI's GPT-6 model prompted expectations that a breakthrough could happen "within 1 or 2 years"
  • He defined that moment as robots being able to complete around 80 per cent of tasks in 80 per cent of unfamiliar environments using voice or text commands.
  • "It's possible that, say, a GPT-7 model released next year could directly crack the problems of embodied intelligence"
  • "The whole embodied intelligence industry has been shaken up after the release of GPT-6"
  • 8月,在GPT-6发布之前,王兴兴表示,行业最快可能需要两到三年,或长达十年,才能迎来其“ChatGPT时刻”。
  • 如今,OpenAI的GPT-6模型引发了预期,认为突破可能“在1到2年内”发生。
  • 他将这一时刻定义为:机器人能够使用语音或文本命令,在80%的不熟悉环境中完成约80%的任务。
  • “有可能,例如明年发布的GPT-7模型可以直接解决具身智能的问题。”
  • “GPT-6发布后,整个具身智能行业都被颠覆了。”

Just looking at some benchmarks on robotic hand manipulation, "On the bowl task Astra placed the block in 19 of 20 trials, against Fable 5.1's 8 of 20".

仅看一些关于机械手操作的基准测试,“在碗任务中,Astra在20次试验中有19次成功放置方块,而Fable 5.1仅为8次”。

But for the puzzle task "Astra completed the insertion 2 times in 20 against Fable 5.1's 2 in 20"".. so we're getting there.

但在拼图任务中,“Astra在20次试验中完成了2次插入,Fable 5.1也是2次”,……说明我们正逐步接近目标。

But going from 2-3 years (potentially to a decade), and compressing that down to 1 or 2 years (to achieve 80%+ of tasks in unfamiliar environemnts) is a significant change in outlook.

然而,从原本可能需要2-3年(甚至长达十年)的时间,压缩至1或2年(即在陌生环境中完成80%以上的任务),这是一个显著的前景变化。

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看懂新闻只是起点——沿量化金融路径,把它变成能交付的工程能力

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