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The Convergence of Machine and Biological Intelligence

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14 Author(s)

To explore the exciting new domain of brain informatics, we invited several well-known experts to discuss the state of the art, the challenges, the opportunities, and the trends. In "Creating Human-Level AI by Educating a Child Machine," Raj Reddy proposes an architecture for a "child machine" that can learn and is teachable. In "Cyborg Intelligence," Zhaohui Wu, Gang Pan, and Nenggan Zheng describe a biological-machine system consisting of both an organic and a computing part. In "Formal Minds and Biological Brains II: From the Mirage of Intelligence to a Science and Engineering of Consciousness," Paul F.M.J. Verschure discusses human-like cognitive architectures and describes the Distributed Adaptive Control (DAC) architecture for perception, cognition, and action. In "The Challenges of Closed-Loop Invasive Brain-Machine Interfaces," Qiaosheng Zhang and Xiaoxiang Zheng discuss the challenges and trends in closed-loop brain-machine interfaces. In "Neural Signal Processing in Brain-Machine Interfaces," Jose C. Principe takes a critical look at the challenges and opportunities of performing computation with pulses, as neurons do. In "Neuroprosthesis Control via a Noninvasive Hybrid Brain-Computer Interface," Alex Kreilinger, Martin Rohm, Vera Kaiser, Robert Leeb, Rüdiger Rupp, and Gernot R. Müller-Putz describe an example of the convergence of biological intelligence and machine intelligence in a hand-elbow neuroprosthesis control unit.

Published in:

Intelligent Systems, IEEE  (Volume:28 ,  Issue: 5 )