Yang Fan, co-founder and vice president of Shang Tang Technology, is also a member of the EGO Beijing Branch. As the general manager of the Shangtang Technology Engineering Products Center, he has been instrumental in developing and delivering artificial intelligence solutions across industries such as pan-security smart video, mobile internet, and finance. With over a decade of experience in computer vision algorithm development, product management, project leadership, R&D strategy, and team building, Yang Fan has significantly advanced the commercialization of Shang Tang’s technologies. In 2016 alone, the company secured hundreds of millions of yuan in product orders. That same year, he was recognized as “Outstanding Young Talent in Beijing†for his role in establishing Shang Tang as a leading AI company within just three years.
The following content is based on an interview conducted by InfoQ with Mr. Yang Fan.
“The greatest value of AI lies in its integration with different industries,†Yang Fan emphasizes. Having spent years immersed in computer vision technology, he previously worked at Microsoft, where he focused on the incubation of emerging technologies such as face recognition, image object recognition, and 3D portrait reconstruction. Today, Shang Tang's core technologies are built around face recognition, intelligent monitoring, and image recognition. As a leader in these areas, Yang Fan humorously notes that he has guided the hands of his researchers to bring these innovations to life.
Under his leadership, a team of over 200 engineers has developed AI solutions across multiple sectors, driving significant progress in the application of AI technologies. Yang Fan believes that while AI itself is not new, its recent rapid advancement is due to improved processing capabilities in voice, image, and video data, along with stronger technical foundations. He stresses that AI’s real-world impact depends heavily on the context in which it is applied.
AI integrates various foundational technologies, and its applications span industries like healthcare, finance, security, logistics, and more. For example, AI in healthcare can be seen in smart devices for diagnosis and identification, while in finance, it enhances transaction security and enables precision marketing. The convergence of AI and security leads to intelligent surveillance and robotic solutions. However, Yang Fan argues that talking about AI without considering real-world scenarios is meaningless. While reusable platforms and tools are important, true value only emerges when the technology is applied in specific contexts.
There have been criticisms in the industry regarding the lack of understanding of deep learning principles among many developers. Yang Fan acknowledges that while some may not fully grasp the underlying concepts, his team, along with others, is actively investing in fundamental research. He emphasizes the importance of balancing basic and applied research, as both are essential for long-term progress. Ultimately, enterprises must demonstrate tangible results from their technological advancements.
Face recognition has sparked widespread interest, and people are curious about the technology behind companies like Shang Tang. Yang Fan explains that the company’s face recognition solutions are used in various practical scenarios, including online account verification, mobile phone unlocking, and personal identity authentication. These systems include liveness detection and ID card verification, offering robust security in both digital and physical environments.
Accuracy rates are often highlighted in the industry, but Yang Fan points out that real-world performance varies greatly depending on the scenario. A 99% accuracy rate may seem impressive, but in security settings, factors like poor lighting or angles can challenge even the most advanced systems. He emphasizes that face recognition is far more complex than it appears, and meaningful progress requires deep understanding of each use case.
When evaluating whether an industry is worth pursuing for AI applications, Yang Fan outlines five key criteria: real demand, scalability, data feedback loops, commercial viability, and innovation-driven development. Each factor plays a crucial role in determining the success of AI implementation.
He also highlights the importance of cross-disciplinary talent—individuals who understand both technology and industry needs. Building effective AI solutions requires a blend of expertise, and finding such talent remains one of the biggest challenges in the field.
In conclusion, the successful deployment of visual AI and the cultivation of AI talent are complex and multifaceted tasks. They require not only technical excellence but also a deep understanding of real-world applications and user needs.
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