330130-085-03-00 The talent pool in AI is the number one challenge for enterprises

330130-085-03-00 Bob Gill: Taking the process industry’s first time-series large-scale model TPT, which was released today, as an example, combined with the actual needs and pain points of their respective enterprises, please talk about the typical value and application prospects that generative AI technology may bring to their respective enterprises and even the industry.

Brad Lee: EMQ has been thinking about how to link the physical world and artificial intelligence through software. Our cooperation with CTC on UCS is to realize the transfer of log data between machines through our software. I think the prospect of generative AI is very optimistic, but it is still in the stage of product productivity development, and the future research and development of deep-seated AI as a product itself needs to be continuously explored.

Cui Shan: TPT undoubtedly opens up a new way for plant operation in the process industry. First of all, TPT creates a new mode of supporting multiple application scenarios through one software, which can help enterprises save a lot of software costs. Secondly, through the acquisition and training of massive data, TPT has shown amazing adaptability across devices and working conditions, providing enterprises with more inclusive and more reliable production optimization. With TPT, we believe we can make more of the traditional industrial impossible possible.

Christian H. Sboro: As a petrochemical company, PGN really needs predictive main330130-085-03-00 tenance to ensure the stable and safe operation of its equipment. PGN currently generates time series data, but these data are only recorded as logs and are not fully used. With the advent of TPT, our time series data can be further analyzed to make more accurate predictions about the device.

Bob Gill: The accelerated implementation of AI technology in the future needs to strengthen ecological construction in terms of industry and technology. How to build AI ecology? How do you practice it in your company or field?

Kazuhito Yokoi: The AI ecosystem has some similarities to the open source ecosystem. In the construction of the open source ecosystem, we define the software architecture, and then complete the function implementation with code. Through daily activity communication, new functions and new knowledge are shared among ecological partners. I believe these lessons can be reused in the AI ecosystem.

Brad Lee: The advent of the AI era has prompted many software companies to start thinking about architectural reinvention and optimization, and in this era, EMQ has also formed a partnership with central control technology, which was difficult to achieve 10 years ago. In the age of AI, business models, ecosystems, and partners need to evolve.

Cui Shan: In the era of AI, the construction of an ecosystem is crucial for all enterprises. It is precisely because of these ecological cooperation that central control technology can come to today. In the general trend of artificial intelligence, enterprises can not fight alone. Everything from computing power, data centers, algorithms, modeling, tale330130-085-03-00 nt, and even government policy requires every partner in the ecosystem to work together to create value for the process industry.

Bob Gill: The embrace of AI is no longer a choice question, it’s a requirement. In the future, how do you think enterprises and industries should make better use of the value of AI technology and jointly promote the progress of society?

Brad Lee: My personal view is that AI is disruptive and massive and can completely change the way businesses operate. It’s important for all organizations to be ready for generative AI, including pioneering practices at the data level for operations, management, storage, and use cases.

Christian H. Sboro: Technological innovation and enterprise evolution go hand in hand. Data preparation, operational processes, personnel reserves, and ways of thinking within the enterprise all need to be prepared to develop in tandem with technological innovation. For enterprises, there needs to be a consensus on the use of AI technology from the top to the bottom operators.

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