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Life remains an elusive concept. High-level questions like “What is life?” posed by philosophers for centuries and scientists for generations struggle for clear-cut answers in the face of advancement in knowledge regarding our existence’s basic blocks – DNA, RNA, and proteins. Despite achieving precision in manipulating these blocks to engineer genomes or construct biological machines, a universal consensus on life’s fundamental definition remains out of reach. This ambiguity owed largely to the N = 1 problem—the challenge of garnering scientific knowledge from a sample size of one, namely the type of life that exists on Earth.
Artificial Life – ALife
As millions of dollars and extensive labor hours pour into the search for an alternative instance of life, some scientists look to solve the N = 1 predicament by creating a second genesis. Artificial life, or ALife, is the systematic examination of life’s core principles by crafting systems that either demonstrate lifelike behavior or serve as a point of comparison against natural creations. However, the task of creating life from scratch, despite the multitude of attempts, remains an unconquered frontier.
Yet the seeming inability to successfully create artificial life won’t deter individuals such as Takashi Ikegami, a complexity scientist at the University of Tokyo, who insists that, like any other basic science, the quest for ALife is about accumulating knowledge. It’s not necessarily about results or utility.
Creative Open-Ended Evolution
Despite the philosophical challenges, the journey to create artificial life could have practical implications. Open-ended evolution, the phenomenon where a system generates infinite complexity, is a sought-after principle in both fields of artificial intelligence and ALife. Presently, the only system known to exhibit such open-endedness is Earth’s biosphere.
However, the hope to replicate life’s boundless “creativity” in a virtual model through ALife is a goal yet to be achieved. In comparison with the advancements in artificial intelligence, progress in ALife is more challenging to discern. These challenges are largely attributed to the undefined nature of life itself and the lack of consensus among ALife practitioners. Several individual projects have made progress along unique paths, mimicking life’s sprawling evolutionary curves.
Bridging Gaps in Understanding
The interdisciplinary nature of ALife research is evident in the work of Stuart Bartlett and David Louapre, who demonstrated in their study how simple chemical systems can exhibit learning. Bridging the divide between wet and soft ALife, they further pushed our understanding of the conditions necessary for the emergence of complex behavior as exhibited in life.
This idea of bridging gaps is a theme echoed by Chris Kempes, a mathematical biologist at the Santa Fe Institute. Shifting focus from N = 1, he proposed to run detailed statistical analyses of Earth’s biodiversity to reveal shared constraints leading to a set of robust laws governing life, irrespective of the type of biological life involved.
Nonetheless, defining life by its ability to undergo Darwinian evolution—a process iterated in the NSA’s working definition and various other proposed definitions—has its critics. Astrobiologist Carol Cleland from the University of Colorado Boulder, for instance, questioned these build-to-understand efforts. Cleland argued that attempts at a new genesis capture several facets of lifelike behavior but don’t quite measure up to being categorically alive or a unique instance of life synthesized from scratch.
The concept that life’s definition transcends its physical form and is rather embodied in its functional properties has drawn criticism.
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