AI 'Next Wave' turns algorithms into productivity

• Top universities at home and abroad receive AI offers
• Digital Twin, See the efficiency and future of AI
• Leader fully trusts and explores new methods
• To argue with customers and insist on better technology

At the moment I chose to pursue industrial AI, I was filled with awe.

In 2018, at the age of 24, I was still studying abroad in Germany. I developed a strong interest in AI during my graduate studies in Germany, from Tongji University in Shanghai to RWTH Aachen University. Even when studying building facilities and sustainable energy, I self learn AI and keep up with the latest developments in AI technology. At that time, I thought, 'It must be difficult to intern at Siemens.'

Unexpectedly, one year later, I not only joined Siemens, but also became a member of the Industrial Artificial Intelligence field.

My first project after joining left a deep impression on me. I need to train robots to recognize real objects corresponding to digital models, which is commonly known as digital twin based machine learning. My team and I are using visual AI solutions to quickly "educate" the robotic arm to identify and sort different types of parts, greatly improving the accuracy and efficiency of automatic sorting. Since then, I have had a clear feeling that industrial AI can play a larger role and is a very meaningful thing, which can directly turn algorithms into productivity.

During this process, I felt the trust of my seniors and the support of the team. I have been focusing on natural language processing and lack experience in visual recognition and industrial applications. Dr. Xiaofeng, the team leader (we all call him Brother Feng), was a pioneer in the AI industry around the turn of the millennium. Even so, Feng Ge still gave me full trust, allowing me to quickly gain the opportunity to stand on my own from a novice. He has many years of experience in AI practice and often clears the clouds for me at critical moments.

In the garbage automatic sorting project of Siemens Chengdu Digital Factory (SEWC), I suggest trying to use visual recognition technology based on small sample learning, and working with the team to customize a highly flexible and intelligent AI solution for SEWC. In the future, this will become a relatively mature AI module, which will be gradually applied in multiple industrial scenarios in China and Germany.

I have the perseverance to be a technician. Feng often says that now is a good time to do AI. Sufficient authorization and trust allow me to try with confidence. The mature platform and cutting-edge technological resources have provided me with the strongest support for technological change. During the process of communicating with clients, it is inevitable to have disputes. But we insist on making better technology and products that are more suitable for customer needs.

In my heart, an excellent algorithm is like a precious artwork, and those who understand it will fully recognize its value. Of course, it's not easy to implement, but with platforms, resources, and guidance from mentors, time will naturally speak.