The term Singularity refers to the theory that — in conformance with the way technology has been advancing— ultra-sophisticated technology is inevitably likely to emerge. Technology so advanced, it would surpasses human intelligence and capability.

Singularity has become a common theme in pop culture and science fiction and it certainly hasn’t been ignored by academics. When it comes to the debate over whether the moment of singularity will actually arrive… the jury’s still out.

A recent article by Tony Prescott, Professor of Cognitive Neuroscience at the University of Sheffield, takes a look at this issue.  Before presenting his personal opinion, Prescott raises an important question: How should Artificial Intelligence (AI) really be compared to human intelligence? If you ask Prescott, human intelligence encompasses much more than the raw brain power of the average individual— something that evolved to its current capacity around 100,000 years ago. For this reason, Prescott maintains that we should actually be comparing AI to our collective human intelligence. “After all, as an entity, AI can stretch across multiple machines. Likewise, the human race amounts to much more than the sum of its parts when we share our capabilities. And why strip us humans of our intelligence-enhancing artifacts?… intelligence-boosting technologies have hugely expanded our ability to apply shared knowledge and control our environment according to our goals…cultural and scientific development have led to a larger, longer-lived and better-educated human species.”

This notion of collective intelligence is something cyberneticist, Francis Heylighen, refers to as “Global Brainpower.” And according to Prescott, this communal strength makes the possibility of singularity happening far less likely. Additionaly he argues that while humans are working hard to make machines more advanced, they’re still far from being on par with many human qualities.

Humans are experts in the seemingly simply skills of acting in and understanding our world. Robots however, are not— the clumsy maneuvres at robo cup tournaments certainly highlight this fact. And in the long run, Prescott argues that there is no economic incentive for replacing this aspect of human intelligence “machines will continue to be engineered to take on the tasks we do poorly, rather than the ones we do well. Like symbiotic systems in nature, the future partnership of people with intelligent machines will be successful because its two halves complement, rather than copy, each other.”

Tony Prescott is also the Director of the Sheffield Centre for Robotics (SCentRo), Director of the Active Touch Laboratory, Co-Director of the Adaptive Behaviour Research Group and a Visiting Fellow at Bristol Robotics Laboratory. You can read the full story about his take on super-intelligent machines HERE.

Electro sensors inspired by fish who navigate their way through murky waters, robots that dance with the honeybees, and artificial muscles and blood vessels making their way into modern medicine. These are just a few of the research topics that were discussed at this year’s Living Machines Conference that took place from 9th to the 12th of July in Barcelona, Spain.

Chairs of the session  Paul Verschure, from Pompeu Fabra University and Tony Prescott from the University of Sheffield, welcomed delegates to one of Barcelona’s architectural gems; Antoni Gaudí’s La Pedrera building.

During 4 consecutive days, leading scientists in the fields of Biomimetics and Biohybryd systems gathered for pre-conference workshops, lectures, poster sessions, exhibitions and open panel sessions to present their work and discuss issues related to the development of real-word technologies inspired by biological systems.

The first day finished off with a panel-lead discussion centred on the question: why study nature? Co-chair Tony Prescott got dialogue flowing by providing two general reasons: to build technologies that could be useful in solving current challenges, and to better understand nature itself.  While the speakers and audience engaged in the discussion agreed that these are likely the main motives, other interesting opinions surged through out the conversation.

According to Barry Trimmer who specializes in Neurobiology at the University of Tufts,  by attempting to understand nature’s complexity, a biomimetic approach may allow us to bypass the limits of human creativity.

Toshio Fukuda who specializes in Micro-Nano Systems Engineering at Nagoya University is often inspired by particular functions or geometric shapes found in nature to help make devices such as the artificial blood vessels he works on more efficient.

Conversely, as a mechanical engineer specialized in aerodynamics, David Lentink  is not so much interested in biomimetics as an outfit for a design, but rather in specific principles which might make sense from an engineering point of view ¨ We don’t want to look at the final detail of a bird wing to make an aircraft because it’s simply too complex, but some of the principles are extremely useful and they allow scientists to really think outside the box.¨

While a biomimetic approach often involves studying some of the most puzzling aspects of nature scientists have yet to wrap their heads around, there are still many things nature can’t do. ¨ Biological systems satisfy many constraints at one time so they may not be optimal for any one function that we may want to imitate. Flight is a great example because we can do things by optimizing that birds just can’t do and we can exceed the capabilities of birds with jets and planes that we build,¨explained Frank Grasso, director of the Biomimetic and Cognitive Robotics lab at Brooklyn College, New York.

However, Dieter Braun, who specializes in Systems Biophysics at Ludwig Maximilians University, pointed out that it’s really a two-way learning stream and just because ¨evolution did not invent the bicycle¨ nature still has plenty of tricks to teach us and we need not be afraid of its complexity.

Check back to find out more on what what was shared during the 2012 Living Machines Conference; proceedings from the conference will be published in Springer Lecture Notes in Computer Science (LNAI/LNCS).