The rise of AI

Sophia, an AI (Artificial Intelligence) product of Hanson Robotics based in Hong Kong, is the world’s first robot to be granted citizenship by a country, specifically Saudi Arabia.

Sophia was honored by the United Nations with an Innovation Champion award. Activated in April 2015, Sophia made her first public appearance at SXSW 2016 in Texas, USA, with the ability to display 50 different emotions on her face and natural communication skills like humans (thanks to AI).

Sophia’s “intelligence” has impressed the public, and she has become a famous global phenomenon, invited to speak at many high-level conferences, including #unleash2018, which I was lucky enough to attend (although many experts doubted that her answers were pre-written). In Singapore, when asked about the significance of the existence of entities like her, Sophia referred to the “Singularity” scenario when intelligent machines surpass humans, the debate over “citizenship,” and the downsides of AI to encourage humans to constantly think of solutions. “Singularity” is a hypothesis about the point when the development of science and technology (including AI) accelerates rapidly, cannot be reversed, and exceeds human control.

The documentary “The Rise of AI” produced by Bloomberg is very interesting. It vividly describes the origin and future scenarios of AI technology through specific studies in the industry, connecting many of the points that Sophia has shared. The film also helps me understand more about Canada, a country that does not seem to launch any technological breakthroughs. However, this is the birthplace of AI — thanks to the efforts of pioneering scientists like Dr. Geoffrey Hinton from the University of Toronto. Hinton’s research team delved into what is called a neural network, an artificial data network (similar to the nervous system in the human brain). This model simulates memory, perception, symbol processing, and creates a foundation for the development of “deep Learning” (deep structured learning/hierarchical learning) — an important extension of the “Machine Learning” field — an algorithm/solution that allows machines to automatically learn how to use a large dataset. The machine can then gain experience based on feedback. Thanks to the “neural network,” the prospect of machines being able to learn and think like a human has been opened up.

By: Quan Nguyen Ha

Supported by Datalac.com