Google (Doodle) Honors Prof. Lotfi Zadeh Of UC Berkeley (The Father Of Fuzzy Logic), Who Was Also An Inventor In A Cognitive Explainable-AI (Artificial Intelligence) 3D Image Recognition Software Startup ZAC
Google Doodle has honored and celebrated the late Prof. Lotfi Zadeh (of UC Berkeley), the world-renowned mathematician, computer scientist, and engineer (“The Father of Fuzzy Logic“), for the publication of his seminal and revolutionary work on Fuzzy sets in 1965. Prof. Zadeh is known for the invention of Fuzzy Logic and the co-invention of Z-Transform (in 1952), which have thousands of applications and appear in many products. In addition, he has made major contributions in the other scientific and engineering fields, e.g., in the linear system theory, the control systems, Computing with Words (CWW), and Soft Computing. He has won more than 50 prestigious international awards and has been a member of Academy of Sciences in 7 countries. He is also an AI Hall of Fame inductee. Since 2011, he had been involved in Z Advanced Computing, Inc. (ZAC) and was one of the ZAC’s inventors. ZAC is the pioneer Cognitive Explainable-AI (Artificial Intelligence) (Cognitive XAI) technologies, e.g., for the detailed complex 3D Image/ Object Recognition from any view angle.
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“We are very proud of Prof. Zadeh and his accomplishments. His great legacy and impacts in science and engineering continue forever through his students, friends, colleagues, and associates in various companies and universities around the world, and through the thousands of software, products, and appliances that use one of his technologies in many applications and industries. It was a great honor for us to work with him,” emphasized Dr. Bijan Tadayon, CEO of ZAC.
ZAC has had major AI and Machine Learning (ML) breakthrough demos in the recent projects for the US Air Force (USAF) and for Bosch/ BSH (the largest appliance maker in Europe): ZAC has achieved detailed complex 3D Image Recognition using only a few training samples, and using only an average laptop with low power CPU, for both training and recognition. This is in sharp contrast to the other algorithms in industry (such as Deep Convolutional Neural Networks (CNN) or ResNets) that require thousands to billions of training samples, trained on large GPU servers.
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