Download From Artificial Intelligence to Cyborg Intelligence

A Letter from the Editor
From Artificial
Intelligence to
Cyborg Intelligence
Daniel Zeng, University of Arizona and Chinese Academy of Sciences
Zhaohui Wu, Zhejiang University
Editor: Daniel Zeng,
University of Arizona and
Chinese Academy of Sciences,
[email protected]
O
ne 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 e­ xpert
­systems, artificial neural networks, outstanding chess-playing programs such
as “Deep Blue,” autonomous vehicles such as “Stanley,” and human-level performance question-answering systems such as “Watson.” However, realizing
human-like intelligent behavior, such as unguided learning, high-level reasoning and sense-making, and adaptability, still has a long way to go.
Biological and Machine Intelligence
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One 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 alternative—or 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, animals, and other kinds of biological systems. Mimicking and making use of such
biological intelligence at different levels—hardware design and algorithmic principles, among others—in 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 abilities—vision, hearing, olfactory, 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 biological intelligence and machine intelligence, “helping to digitize old printed material by asking users to decipher scanned words from books that computerized
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optical character recognition failed to
recognize.”1 In such approaches, however, the linkage between human intelligence and machine intelligence is
loose, in the traditional sense of human-computer interaction. Recent years
have seen quantum leaps in research
dedicated to this linkage and the enormous potential enabled by deeply
connecting and integrating biological
and machine intelligence.
Cyborg Intelligence
Biological beings and computer systems share some common physical
foundations. Communication in both
biological nervous systems and computer systems, for example, depends
on electrical signals. Yet, the gap between these two classes of vastly different systems is obvious. Thanks
to new developments in neuroimaging technologies, such as functional
magnetic resonance imaging (fMRI),
magneto encephalography (MEG),
and positron emission tomography
(PET), however, the gap is no longer insurmountable. These technologies allow us to observe, in increasing
levels of resolution and fidelity, the
brain’s inner workings, and reveal the
brain’s structure and function. Furthermore, progress in brain-machine
interfaces (BMIs) in the last decade
has made possible direct communication pathways between the brain and
man-made systems at the signal level.
These new developments represent
significant advances in cyborg intelligence.2 Cyborg intelligence aims to
integrate AI with biological intelligence closely and deeply by connecting computer systems and biological
systems via BMIs, enhancing strengths
and compensating for weaknesses of
both systems by combining the biological systems’ ­
perceptive and cognitive a­ bilities with the computer systems’ computational power. The term
cyborg was coined by Manfred C
­ lynes
september/october 2014
and Nathan Kline in 1960,3 to describe 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 intelligence 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 offer a communication pathway in bridging this gap between the two. Such
technology is helping us decode thinking-related signals from the scalp, the
dural cortex, and even subcortical areas. It also helps connect the brain
directly to the outside world. Neural
­
signals can control machine actuators,
and machine-coded sensory information can be delivered into specific areas of the brain. Through bidirectional
BMIs, we can connect biological components to machine components at
multiple levels, building a hybrid intelligent system of great promises.
Recent cyborg intelligence research
areas have included the following topics:
• Animals as sensors—utilization of
animals as sensors; for example,
dog’s olfactory sense.
• Animals as actuators—using animals as actuators to complete certain actions.
• Mind-controlled machines—decoding the human mind to control external devices.
• Neurochips—chips designed to
connect to neuronal cells; for example, memory chips to replace
memory cortex for memory restoration and enhancement.
• Intelligent prosthesis—devices replacing 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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• Neuromorphics—analog, digital, and mixed-mode analog/digital
VLSIs and software systems that implement models of neural systems
(such as perception, motor control,
and multisensory integration).
• Symbiotic cognition—integration of
biological cognitive functions with
computational models of cognition.
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.
2. W. Zhaohui, G. Pan, and N. Zheng,
“Cyborg Intelligence,” IEEE Intelligent Systems, vol. 28, no. 5, 2013,
pp. 31–33.
3. M.E. Clynes and N.S. Kline, “Cyborgs
and Space,” Astronautics, Sept. 1960,
pp. 26–27, 74–76.
References
Of course, this is just a sample of topics in this field. As we can see, cyborg
intelligence holds great promise in
many practical applications.
stay
on
the
1. L. von Ahn et al., “ReCAPTCHA: Human-Based Character Recognition via
Web Security Measures,” Science, vol.
321, no. 5895, 2008, pp. 1465–1468.
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