About Prof Tsilidzi Marwala

Professor Tshilidzi Marwala was born on 28 July 1971 in Venda South Africa and is currently the Deputy Vice-Chancellor: Research, Internationalisation and the Library at the University of Johannesburg. Marwala was previously a Dean of Engineering at the University of Johannesburg, a Professor of Electrical Engineering, the Carl and Emily Fuchs Chair of Systems and Control Engineering as well as the DST/NRF South Africa Research Chair of Systems Engineering at the University of the Witwatersrand.

He was a professor extraordinaire at the University of Pretoria. He is the youngest recipient of the Order of Mapungubwe and was the first African Engineer to be awarded the President Award by the National Research Foundation of South Africa. He holds a Bachelor of Science in Mechanical Engineering (Magna Cum Laude) from Case Western Reserve University (USA), a Master of Engineering from the University of Pretoria, a PhD in Engineering from University of Cambridge and was a post-doctoral research associate at the Imperial College (London). He has held visiting positions at Harvard University and Wolfson College, Cambridge. He matriculated at Mbilwi Secondary School, attended the 1989 London International Youth Science Forum and completed Sixth Form at St John's College, Johannesburg.

He has supervised 45 masters and 19 PhD students to completion, holds 3 USA patents, and has published over 300 technical papers and 9 books. He is a fellow of TWAS, The World Academy of Sciences, Academy of Science of South Africa and African Academy of Sciences as well as a senior member of the IEEE and a distinguished member of the Association for Computing Machinery.

His 9 books are (1) Computational Intelligence for Modelling Complex Systems. Delhi: Research India Publications (2007); (2) Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques. Pennsylvania: IGI Global (2009); (3) Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics. Heidelberg: Springer (2010); (4) with Monica Lagazio, Militarized Conflict Modeling Using Computational Intelligence. Heidelberg: Springer (2011); (5) Condition Monitoring Using Computational Intelligence Methods. Heidelberg: Springer (2012); (6) Economic Modeling Using Artificial Intelligence Methods. Heidelberg: Springer (2013); (7) Artificial Intelligence Techniques for Rational Decision Making. Heidelberg: Springer (2014); (8) Causality, Correlation, and Artificial Intelligence for Rational Decision Making. Singapore: World Scientific (2015); as well as (9) with Ilyes Boulkaibet and Sondipon Adhikari, Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering. London: John Wiley & Sons (2016).

“The Fourth Industrial Revolution: Artificial Intelligence and Society”

 The first industrial revolution, which occurred in Britain in the 17th and 18th century, was an epoch in economic development, which ushered the use of machines in production, and they were powered by steam engines. The second industrial revolution gave us electricity, automobiles and airplanes. The third industrial revolution gave us the digital age as well as the Internet and was spurred by the discovery of the transistor.

The fourth industrial revolution is characterized by making systems as well as machines intelligent and connected. The underlying technology of the fourth industrial revolution is artificial intelligence (AI). Artificial intelligence is a paradigm where physical and social phenomena are programmed to solve complex problems. AI enables machines to learn, adapt, evolve and optimize and has had a profound impact in diverse fields such as engineering, medical sciences and social sciences.

Some of the AI paradigms that are discussed in this talk include machine learning algorithms such as neural networks which have been inspired by the mechanism in which the human brain works, evolutionary programming techniques which include genetic algorithms which are inspired by the mechanism of natural evolution, as well as social programming such as particle swarm optimization which is inspired by the way in which birds flock together.

This talk discusses how these algorithms have been applied to monitor key equipment, to model HIV risk, to build better finite element models and to rewrite economic theories.