Machine Learning And Wireless Technology To Merge Cyber And Physical Worlds In The Near Future

Doha, Qatar, March 24, 2016:  The Computing and Information Technology session took place today during Qatar Foundation’s Research and Development (QF R&D) Annual Research Conference 2016 (ARC’16). The discussion highlighted the fundamental significance of handling big data, in light of the rapid growth of wireless technology in the past five years, leading to a 74% growth in global mobile data in 2015. 

With the proliferation of technologies like Google Glass, holographic wrist phones, 3D conferencing and smart homes, wireless technology is set to continue its rapid growth. Attendees heard how, in the near future, tactile internet will merge the cyberworld with the physical world, and 5G will revolutise wireless technology.
Machine Learning was labelled as the ‘brain’ of big data due to the significance of its applications in the field of medicine. The technology is already being utilised in patient diagnosis, White Blood Cell classification, data mining of medical records, and Brain Activity Map analysis. 

The panel discussion, entitled ‘Big Data for Sustainable Development’, featured presentations from industry experts Khaled Ben Letaief, Provost of Hamad bin Khalifa University (HBKU); Ahmed Elmagarmid, Executive Director of QCRI, and Yasir S. Abu-Mostafa, Professor of Electrical Engineering and Computer Science at the California Institute of Technology, USA. The session was moderated by Mounir Hamdi, Dean of the College of Science and Engineering at HBKU. 

The main underlying topic of discussion throughout the session was the future of intelligent machines and, according to Dr Ahmed Elmagarmid, Executive Director of Qatar Computing Research Institute (QCRI), there is a pressing need to rely on the deliverabilty and integrity of big data, thereby making it essential to follow efficient algorithms and principles that can handle big data. 

The session ended on a vital note, emphasising that the patterns detected by machines through Big Data analyses need to be consistent and reliable.