from artificial intelligence to cyborg intelligence .from artificial intelligence to cyborg...
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One of the primary and utilitarian goals of artificial intelligence research is to develop machines with human-like intelligence. Great prog-ress has been made since the start of AI as a field of study. Generations of
AI thinking, AI schools of thoughts, and AI engineering have given us expert
systems, artificial neural networks, outstanding chess-playing programs such
From Artificial Intelligence to Cyborg IntelligenceDaniel Zeng, University of Arizona and Chinese Academy of SciencesZhaohui Wu, Zhejiang University
as Deep Blue, autonomous vehicles such as Stanley, and human-level per-formance question-answering systems such as Watson. However, realizing human-like intelligent behavior, such as unguided learning, high-level reason-ing and sense-making, and adaptability, still has a long way to go.
Biological and Machine IntelligenceOne dominating research paradigm in AI has been based on the assumption that various aspects of human intelligence can be described and understood well enough to the extent that it can be simulated by computer programs through smart representational frameworks and generic reasoning mechanisms. Despite great progress enabled by this paradigm, its limitations have been well-recognized by the research community. An alternativeor to a large extent, a complementary paradigm (which has almost-as-deep roots and history)is gaining tremendous momentum lately and has attracted much attention. This perspective is based on the realization that varying kinds and degrees of intelligence reside in humans, ani-mals, and other kinds of biological systems. Mimicking and making use of such biological intelligence at different levelshardware design and algorithmic prin-ciples, among othersin a more direct manner, could greatly influence the design of AI systems, opening fresh pathways and application areas for AI.
Biological systems possess all kinds of sensory abilitiesvision, hearing, ol-factory, haptic, and gustatory senses, to name a few. They also adapt to changes in external environments, and are capable of a range of cognitive functions. AI systems could greatly benefit from biological intelligence, solving problems that are still beyond the capabilities of the state of the art. For instance, image understanding is a relatively easy job for humans, yet it still challenges even the most sophisticated AI algorithms. The reCAPCHA approach, as an example of collective intelligence, has demonstrated the power of integrating biologi-cal intelligence and machine intelligence, helping to digitize old printed mate-rial by asking users to decipher scanned words from books that computerized
SEpTEMbEr/ocTobEr 2014 www.computer.org/intelligent 3
optical character recognition failed to recognize.1 In such approaches, how-ever, the linkage between human in-telligence and machine intelligence is loose, in the traditional sense of hu-man-computer interaction. Recent years have seen quantum leaps in research dedicated to this linkage and the enor-mous potential enabled by deeply connecting and integrating biological and machine intelligence.
Cyborg IntelligenceBiological beings and computer sys-tems share some common physical foundations. Communication in both biological nervous systems and com-puter systems, for example, depends on electrical signals. Yet, the gap be-tween these two classes of vastly dif-ferent systems is obvious. Thanks to new developments in neuroimag-ing technologies, such as functional magnetic resonance imaging (fMRI), magneto encephalography (MEG), and positron emission tomography (PET), however, the gap is no lon-ger insurmountable. These technolo-gies allow us to observe, in increasing levels of resolution and fidelity, the brains inner workings, and reveal the brains structure and function. Fur-thermore, progress in brain-machine interfaces (BMIs) in the last decade has made possible direct communica-tion pathways between the brain and man-made systems at the signal level.
These new developments represent significant advances in cyborg intel-ligence.2 Cyborg intelligence aims to integrate AI with biological intelli-gence closely and deeply by connect-ing computer systems and biological systems via BMIs, enhancing strengths and compensating for weaknesses of both systems by combining the bio-logical systems perceptive and cog-nitive abilities with the computer sys-tems computational power. The term cyborg was coined by Manfred Clynes
and Nathan Kline in 1960,3 to de-scribe a being with both organic and synthetic parts. More broadly, cyborgs refer to symbiotic biological-machine systems, consisting of both organic and computing components. Cyborg intel-ligence is a new research paradigm, aiming to combine the best of both machine and biological intelligence.
At the core of cyborg intelligence is the closely-coupled connection of the organic and computing parts. BMIs of-fer a communication pathway in bridg-ing this gap between the two. Such technology is helping us decode think-ing-related signals from the scalp, the dural cortex, and even subcortical ar-eas. It also helps connect the brain directly to the outside world. Neural signals can control machine actuators, and machine-coded sensory informa-tion can be delivered into specific ar-eas of the brain. Through bidirectional BMIs, we can connect biological com-ponents to machine components at multiple levels, building a hybrid intel-ligent system of great promises.
Recent cyborg intelligence research areas have included the following topics:
Animals as sensorsutilization of animals as sensors; for example, dogs olfactory sense.
Animals as actuatorsusing ani-mals as actuators to complete cer-tain actions.
Mind-controlled machinesdecod-ing the human mind to control ex-ternal devices.
Neurochipschips designed to connect to neuronal cells; for ex-ample, memory chips to replace memory cortex for memory resto-ration and enhancement.
Intelligent prosthesisdevices re-placing a missing or damaged body part using the human nerve system and brain interfacing to increase precision and achieve comfort of movements.
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Neuromorphicsanalog, digi-tal, and mixed-mode analog/digital VLSIs and software systems that im-plement models of neural systems (such as perception, motor control, and multisensory integration).
Symbiotic cognitionintegration of biological cognitive functions with computational models of cognition.
Of course, this is just a sample of top-ics in this fi eld. As we can see, cyborg intelligence holds great promise in many practical applications.
At the intellectual level, cyborg in-telligence poses countless interesting and important questions to AI research and could fundamentally change the landscape of AI in several dimensions. This is one emerging area of study that warrants close attention and active participation from AI researchers.
References 1. L. von Ahn et al., ReCAPTCHA: Hu-
man-Based Character Recognition via
Web Security Measures, Science, vol.
321, no. 5895, 2008, pp. 14651468.
2. W. Zhaohui, G. Pan, and N. Zheng,
Cyborg Intelligence, IEEE Intelli-
gent Systems, vol. 28, no. 5, 2013,
3. M.E. Clynes and N.S. Kline, Cyborgs
and Space, Astronautics, Sept. 1960,
pp. 2627, 7476.
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