What should the manufacturing industry do to prepare for the large model?

Starting in 2024, almost every week, big models have had “breaking” news launched. According to the industry’s information, in the next, there are still some companies released. Many companies M21NRXA-JDS-NS-02 contacted by Peng Xinyu, vice president of Alibaba Group and CEO of Hillhouse Sheep, are anxious that they do not know what to do. Under the continuous extension of anxiety, some social anomalies such as “the end of AI is to sell lessons” have also appeared in society.

Lu Chong admitted that the concept is now much talked about, but the real application needs to be done step by step.

“If it comes down to the enterprise level, there are three things: computing power, M21NRXA-JDS-NS-02 the data of the enterprise, and the talent of the enterprise.” Peng Xinyu analysis said that in terms of computing power, in addition to a few companies such as OpenAI, for most companies, everyone’s starting line is almost the same.

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In terms of data, OpenAI has basically collected all the public data on Earth, but the most valuable data for enterprises is often the enterprise’s own data, which is not collected by OpenAI. Enterprises need to consider how to improve the quality of this data, turn it M21NRXA-JDS-NS-02 into a good asset, and integrate it with the larger model. “I think it’s a real investment that companies can hold onto.”

In terms of talent, for most enterprises, it is not necessary to find a bull in the field of AI, and the key is that such talents can not be found now. Instead, companies need people who understand how to apply big models, how to talk to enterprise workflows, and translate the power of big models into business productivity. “Companies need to catch these people.”

Mr. Wang emphasized the power of industry experts. He took live delivery as an example. The rise of social platforms has liberated individual productivity and changed the operation mode of many traditional industries. But the success of this model often depends on experts who have a deep understanding of a particular industry.” For example, an expert who is well-versed in selling cosmetics may not be good at live-streaming sales of electronics, and vice versa. He said that while technology offers varying degrees of new opportunities in various fields, true expertise and industry understanding are still indispensable.