On April 27, Daniel Zhang, Alibaba‘s Chairman and CEO and CEO of Alibaba Cloud Intelligence, revealed on the sixth Digital China Summit that Alibaba Cloud engineers are experimenting with integrating the Qianwen large-scale mannequin into industrial robots. Just by coming into a sentence in human language into the DingTalk chat field, distant management of robotic work could be achieved.
An illustration video launched by Alibaba Cloud on the occasion confirmed this experimental achievement. After an engineer instructed the robotic by the DingTalk chat field saying “I’m thirsty, find something for me to drink,” Qianwen instantly understood the instruction and replied: “Okay, I’ll see what there is to drink.” Then the large-scale mannequin robotically wrote a set of code and despatched it to a robotic. The robotic started recognizing its environment and located a bottle of water on a close-by desk earlier than finishing a sequence of actions equivalent to transferring, grabbing and delivering it easily to the engineer.
Zhang mentioned that “manufacturing is an important battlefield for AI large-scale models. The biggest opportunity in cloud computing, AI and physical world machine integration over the next ten years lies here. Robots fetching water is just a start. Intelligent robots capable of direct dialogue with humans will change entire factory operations.”
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At current, the management of robots is closely depending on codes. Operating robots necessitates people to study complicated programming languages or purchase information about robotic techniques. Nevertheless, large-scale fashions like GPT supply novel potentialities for breaking down obstacles between people and AI by using multi-modal language fashions as a way of communication between people and robots.
In addition, large-scale fashions not solely assist purposes but additionally take part in improvement phases inside robotics fields.
According to an Alibaba Cloud engineer interviewed throughout this occasion, when growing robots, engineers can use Qianwen’s large-scale model-generated code directions for growing and debugging robotic features whereas even creating some new options for them.
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