Youtube Link: https://youtu.be/q-L9f2iz6sg
No-code AI is by no means perfect today. This is because the ‘AlphaCode AI’ engine developed by DeepMind is still evaluated as “half-successful”. But I am optimistic about the future of “No-Code AI”. For example, DeepMind's 'Alpha Code AI' no-code model mentioned above was developed using the 'Github library' and a collection of coding problems and their solutions.
‘No-code AI’ will help popularize AI, that is, the “breadth” of AI, but it will also contribute to the “depth” of AI. Experts can save time writing code, so they can spend more time analyzing and solving problems. DeepMind’s ‘Alpha Code AI’ suggests the direction of ‘No-Code AI’. If you input a ‘problem’ in the language of an ordinary person, the ‘solution’ that is the result of the coding will come out automatically.
The left side of the figure in the video is input and the right side is output. However, coding does not work even if you input the problem you want to solve. Of course, this is coding in computer language, but it can’t provide the solution your computer needs. Only coded documents are generated as output. Therefore, in addition to describing the problem, the "problem" also needs to be analyzed, and the description of solving the problem must be provided to complete the coding of the problem.
When unfamiliar concepts and logic appear, the resulting coding is bound to be very sloppy. As such, ‘No-Code AI’ seems easy to ordinary people, but it is not so easy to use. Above all, we need STEM education and abundant domain knowledge.
About the author Dr. Phil Yang: Chairman of CKT Group; CEO of MAILab; China Qingdao Municipal Government Economic Ambassador; Advisor, Tsinghua University Foundation, China; Adjunct Professor, Tsinghua University, China; International President of Jilin Vocational University, China; New York State University, Korea Branch, Chair Professor; Director, Konkuk University's Chinese Enterprise Research Institute
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