Every day it seems we are hearing of new advances made by AIs thanks to Machine Learning, from improving healthcare to beating us at poker, it is often easy to forget that, behind every successful robot, there’s a clever human.
The swift pace of change we are seeing today is due to a concerted effort across industry and academia to find practical uses for the ever-growing amount of data we are generating and collecting.
So, in this post I am going to highlight some of the current movers ‘n’ shakers, whose breakthroughs in machine learning are proving to be fundamental to developing the digital tools and technologies making AI possible, from social networks to self-driving cars, to the industrial internet.
Ng has just resigned from his post as chief data scientist at Chinese online giant Baidu. As well as that he is the founder of the online training resource Coursera and associate professor at Stanford University’s computer science department.
Before joining Baidu he formed Google’s Brain AI research division and his work has focused on deep learning. At Stanford he has led projects including the development of the Stanford Artificial Intelligence Robot (STAIR) as well as algorithms to build 3D digital models from a single flat photographic image.
Professor at the Universite De Montreal’s department of computer science, Bengio is noted for his research into artificial neural networks and deep learning. He has stated that the overriding ambition behind his research is to understand “principles of learning that yield intelligence.” Among other principles of AI and ML, much of his published work concerns auto-encoders which are used for encoding or formatting unstructured data, to make it understandable by computers via unsupervised machine learning.
As director of AI research at Facebook since 2013, LeCunn has received recognition for pioneering work in the field of computer vision – teaching machines to “see” in the same way we do by recognising objects and to go on to learn, by classifying them. He is also considered one of the founders of the convolutional neural network model which aims to create algorithms which ingest and interpret information in the same way as a biological organism like an eye or a brain. He is a founding director NYU centre for Data Science.
Hassabis is a co-founder of Deep Mind, a UK AI startup acquired by Google in 2014. His work has focused on the development of artificial neural networks by combining machine learning with neuroscience. Deep Mind’s most publicised success so far has been its AlphaGo AI which last year became the first computer programme to beat a professional human Go player. Before this breakthrough, even the best AIs were only capable of playing at the level of human amateurs and could be defeated by most people with practice.
Hinton splits his time between working as an Engineering Fellow at Google and professor at the University of Toronto’s Department of Computer Science. As far back as 1992 he was publishing papers on the use of artificial neural networks to simulate human processing of information in machines. Hinton initially trained in psychology before gaining his PhD in AI and applying his understanding of human cognitive processes to computers. You can read a good number of his widely-cited publications here.
Although currently taking a sabbatical to work as chief of AI and ML at Google Cloud, Li is associate professor at Stanford University where she heads up the Stanford Artificial Intelligence Lab as director, as well as the Stanford Vision Lab.
She has authored over 100 scientific articles on subjects ranging from computational neuroscience to visual recognition and Big Data and her work on the ImageNet project – a database of images designed for training deep learning image recognition algorithms – is widely recognised.