Here is the first article of my series concerning famous Data Scientists. Today we will speak of the one and only Geoffrey E. Hinton; also known as; The “Godfather” of deep learning.
So we will follow the following steps.
(1) Who is this Data Scientist?
(2) What is his work about?
(3) How can I benefit of such learning insights in my career path & theme selection?
Who is this Data Scientist?
Being a British-born (December 6, 1947), Geoffrey Everest Hinton is a Canadian cognitive psychologist and computer scientist most noted for his work on artificial neural networks. Author of publications dating from 1986, the latter focuses on neural connectionism, processing, learning, mapping, pattern recognition and inference architecture.
Since 2015 he divides his time working for Google as an Engineering Fellow where he manages Brain Team Toronto which is a new part of the Google Brain Team and the Department of Computer Science of University of Toronto as Emeritus Professor. He is also the Chief Scientific Adviser of the new Vector Institute.
What is his work about?
For his contribution in the following fields: machine learning, neural networks, artificial intelligence, cognitive science and object recognition, Hinton is mainly known for his legacy: backpropagation, Boltzmann machine and deep learning.
By revolutionizing speech recognition and object classification; Hinton received prestigious awards through his career such as: FRS (1998), AAAI Fellow (1990), Rumelhart Prize (2001), IJCAI Award for Research Excellence (2005), IEEE Frank Rosenblatt Award (2014) and BBVA Foundation Frontier.
Famed as The “Godfather” of deep learning, Hinton is among those who turned algorithms into reliable artificial intelligence through constant data feed in.
How can I benefit of such learning insights in my career path & theme selection?
Hinton is an awesome Data Scientist. However, I don’t think that I will study brain related stuff. As I am in fact more a data driven economist guy than a science guy trying to get artificial intelligence on pinnacles. Nevertheless, I think that applying his methodology in my studies and use such concepts as backpropagation and deep learning will of course be in my learning track.
Being a Python learning with a marketing and economic interest I do think that his contributions will indeed feed my thinking.
Conclusion : Hinton’s work could be considered as references for me. In addition, the latter do brings in great insights. However, the artificial intelligence thing, is not my cup of tea. I do think that I may drop in some algorithm but not to his level. Thus, Hinton, could not be considered as a role model for me, but remains an outstanding name in the field of Data Science.