Professor Jurgen Schmidhuber has been appointed as the director of the artificial intelligence initiative at King Abdullah University of Science and Technology.
The university is making strides in international AI research, education, entrepreneurship, and leadership. It is also striving to embed AI into its other activities.
KAUST President Tony Chan said the appointment of Schmidhuber reflects the university’s and ¶¶Òõ¶ÌÊÓƵ’s commitment to critical AI technology.
Schmidhuber, who obtained a Ph.D. in computer science from the Technical University of Munich in Germany, is a prominent researcher and is considered to be one of the world’s leading scientists of artificial neural networks.
He is the co-founder and chief scientist of NNAISENSE, a company founded in 2014 to build large-scale neural network solutions for industrial process inspection, modeling, and control.
The new director, who has received a number of awards, recently directed the Swiss AI Lab, IDSIA, and was also a professor of AI at the University of Lugano. Schmidhuber has authored more than 350 peer-reviewed papers and is an adviser to various governments on AI strategies.
At KAUST, Schmidhuber is expected to recruit new faculty members, develop educational programs and entrepreneurial activities. Moreover, he is most likely going to involve KAUST in collaborations with key public and private sector institutions in the country.
It is worth noting that the deep-learning neural networks, developed by Schmidhuber’s lab, revolutionized machine learning and AI technology. By the mid-2010s, these circuits of neurons were used by more than three billion devices around the world.
They were used, for instance, to improve speech recognition in all smartphones with an Android operating system and improve service for both Google and Facebook translators.
They were also used in Apple’s personal assistant, Siri, and on the Quicktype application on all iPhones and many other apps.
In 2011, Schmidhuber’s team was the first to win official computer vision contests through deep-learning neural networks with superhuman performance. A year later, the team won a medical imaging contest on cancer detection.
