Generative AI is speeding up human-like robot development. What that means for jobs

In Beijing, the rise of ChatGPT-like artificial intelligence is accelerating research and pushing humanoid robots closer to reality, particularly in China, a hub for global manufacturing. While OpenAI’s technology isn’t officially accessible in China, local firms like Baidu have introduced similar chatbots and AI models.

In the realm of robotics, the advancement of generative AI is enhancing machines’ ability to understand and interact with their surroundings. Li Zhang, COO of LimX Dynamics in Shenzhen, noted that AI has significantly expedited their research and development efforts, shortening the timeline for producing humanoid robots capable of both factory work and household assistance from eight to ten years to an estimated five to seven years.

Various companies, including OpenAI and Tesla, are investing in humanoid robot startups, recognizing the potential in this emerging market. Chinese President Xi Jinping’s recent visit to Shanghai showcased a locally developed humanoid robot, highlighting China’s commitment to advancing robotics technology.

While AI progress is significant, replacing human labor entirely remains a challenge due to mechanical limitations. Although generative AI doesn’t directly impact robotic motion, it can facilitate advanced task planning, according to Eric Xia, a partner at Future Capital, an investor in LimX.

The shift towards using robots in factories could accelerate as costs decrease. Steve Hoffman, chairman of Founders Space, is collaborating with a Chinese startup called Fastra to mass-produce robots within a year. He believes that integrating generative AI into robotics can enhance efficiency and reduce costs, making robots more accessible for widespread adoption.

In sectors like pharmaceutical research, generative AI is streamlining processes and cutting costs without diminishing human involvement. Alex Zhavoronkov, CEO of Insilico Medicine, highlights how AI has enabled their company to dramatically reduce the number of experiments needed for drug development, ultimately accelerating the pace of innovation in the industry.

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