Prof. Dr. Suyanto, S.T., M.Sc.

Prof. Dr. Suyanto, S.T., M.Sc.

Telkom University, Indonesia

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Prof. Dr. Suyanto, S.T., M.Sc. born in Jombang, East Java, Indonesia in 1974. He received a Bachelor of Engineering degree from the Department of Informatics Engineering STT Telkom (now Telkom University), Bandung, in 1998. He got a Master of Science degree in Complex Adaptive Systems from Chalmers University of Technology, Göteborg, Sweden, in 2006. He obtained a Doctor in Computer Science from Gadjah Mada University, Yogyakarta, in 2016. In 2021, he received a professor in Artificial Intelligence. Since 1999 he has been a lecturer and actively conducting research in Artificial Intelligence, Swarm Intelligence, Evolutionary Computation, Machine learning, Advanced Data Science, Computational Linguistics, and Speech Processing. He has produced 106 scientific publications indexed by Scopus with an h-index of 16, making his name on the List of the 2% Most Influential Scientists in the World, published by Stanford University and Elsevier BV in October 2021. In addition, he has registered eight patents, obtained twenty-five copyrights, and published eleven textbooks related to the field of Artificial Intelligence.

Next Generation Artificial Intelligence

John McCarthy proposed artificial intelligence (AI) as a field of research in 1956. Since then, AI has alternated between periods of apathy and sustained interest and funding. In the 1980s, machine learning was developed as a new generation of AI. In 2012, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton revolutionized AI with a deep learning (DL) model named AlexNet, which won the ImageNet object recognition challenge with a significant lower error rate than the runner-up ML-based method. Since then, DL has dominated AI. After a decade, researchers now realize DL has two obvious limitations: reliance on huge amounts of labeled data and the need for high computing power. Therefore, they create new models for the next generation of AI, such as self-supervised deep learning, hybrid symbolic AI and deep learning, neuroscience-based deep learning, etc.