Artificial Life (Alife)


Artificial life (often abbreviated ALife or A-Life) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American computer scientist, in 1986. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate some aspects of biological phenomena. The term "artificial intelligence" is often used to specifically refer to soft alife

Artificial life studies the logic of living systems in artificial environments. The goal is to study the phenomena of living systems in order to come to an understanding of the complex information processing that defines such systems.

Also sometimes included in the umbrella term Artificial Life are agent based systems which are used to study the emergent properties of societies of agents.

Artificial life (commonly Alife or alife) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate biological phenomena. The term "artificial life" is often used to specifically refer to soft alife.

At present, the commonly accepted definition of life does not consider any current alife simulations and softwares to be alive, and they do not constitute part of the evolutionary process of any ecosystem. However, different opinions about artificial life's potential have arisen:

Techniques

Artificial Life  Wikipedia

Artificial intelligence (AI)




In the News ...


First Synthetic Yeast Chromosome Paves Way for Designer Genomes   Live Science - March 28, 2014

A chunk of the genetic blueprint for yeast has been created and pieced together from scratch, paving the way for "designer" organisms that could produce new medicines, food products and biofuels, the creators say. Researchers took tiny snippets of man-made DNA and joined them together to create a synthetic version of a chromosome, the structure that contains DNA inside cells, from brewer's yeast. The ability to create such chromosomes is a major step for the field of synthetic biology, which aims to engineer microbes to produce useful products. The work also brings scientists closer to creating synthetic plants and animals.


Scientists Move Closer to Inventing Artificial Life   National Geographic - March 29, 2014
In a biological first, an international team has inserted a man-made chromosome into brewer's yeast, producing a life form that thrives and successfully passes the designer genes on to its offspring. The "synthetic" biology advance - the first synthesis of a working artificial chromosome in an organism more complex than a bacterium - opens the door wider to man-made microbes that may someday be designed to manufacture better fuels, food, and medicines.


Scientists hail synthetic chromosome advance   BBC - March 28, 2014
Scientists have created the first synthetic chromosome for yeast in a landmark for biological engineering.




  'Artificial life' breakthrough announced by scientists   BBC - May 20, 2010

Scientists in the US have succeeded in developing the first synthetic living cell. The researchers constructed a bacterium's "genetic software" and transplanted it into a host cell. The resulting microbe then looked and behaved like the species "dictated" by the synthetic DNA.




A Grand Unified Theory of Artificial Intelligence   PhysOrg - March 30, 2010
In the 1950s and '60s, artificial-intelligence researchers saw themselves as trying to uncover the rules of thought. But those rules turned out to be way more complicated than anyone had imagined. Since then, artificial-intelligence (AI) research has come to rely, instead, on probabilities -- statistical patterns that computers can learn from large sets of training data. The probabilistic approach has been responsible for most of the recent progress in artificial intelligence, such as voice recognition systems, or the system that recommends movies to Netflix subscribers. But Noah Goodman, an MIT research scientist whose department is Brain and Cognitive Sciences but whose lab is Computer Science and Artificial Intelligence, thinks that AI gave up too much when it gave up rules. By combining the old rule-based systems with insights from the new probabilistic systems, Goodman has found a way to model thought that could have broad implications for both AI and cognitive science.




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