Category Archives: Biology
The Edge of Life
In 1971, computer networks were a relatively new concept, and so it occurred to Bob Thomas, an engineer working at BBN Tech that it would be neat to write a program than could traverse these networks on its own, forever copying itself onto other machines. The “Creeper” was born. Unknowing users would login to their terminals and be presented with the text “I’m the creeper, catch me if you can!”. After logging in, the program would attempt to connect to other machines on the network and move itself to a new system. Although the term “computer virus” had not yet been coined, Bob Thomas had had managed to create the first one. He later wrote the complimentary “Reaper” program to seek out and shut down any Creeper programs out in the network.
Frederick Cohen’s research at USC in 1983 brought the idea of a computer virus to the mainstream. He developed a program that attempted to spread by attaching itself to commonly used programs on a shared computer system. Within minutes, Cohen’s program would spread through the file system and gain complete control. Cohen believed that his program was alive in the literal sense because it met the requirements for life: it was a pattern capable of reproducing, it could make use of the metabolism of its host (the computer), and it adapted to its environment, installing itself opportunistically.
The “Strong Claim” of Artificial Life says that any definition of life that includes all biological forms will necessarily include some computer-based systems as well, and these systems must therefore be considered actually alive. When the field of Artificial Life was formed in 1987, scientists agreed that computer viruses were indeed the most likely candidate to meet the strong claim. The opposite “weak claim” states that no matter how lifelike programs become, they will only ever be simulations of life, not instances of it.
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Flock of Boids
Large flocks of birds appear to display a collective intelligence as they fly in an organized fashion with an apparently singular motive. Starlings in particular have been observed in flocks reaching hundreds of thousands of individuals generating amazing patterns in their group movement as shown in the video below.
Artificial Life researchers get very excited when complexity can be shown to emerge from very simple rules. One of the early examples of this was developed by Craig Reynolds as part of the 1987 SIGGRAPH conference with a program he called BOIDS. With BOIDS he demonstrated that flocking behavior can emerge naturally if every individual BOID followed three simple rules:
- separation: steer to avoid collisions
- alignment: head in the same direction as your neighbors
- cohesion: head towards the center
BOIDS is an excellent example of emergent behavior, and since been adapted for use in computer graphics and special effects, its first use being the 1992 film “Batman Returns” to render bat swarms and penguin flocks.
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Tierra
Tom Ray was a tropical biologist who conducted research in the Costa Rican rain forest from 1974 to 1989. His research focused on the ecologies and evolution of various species living there. Eventually he realized he there was a problem with studying evolution in the wild: it occurs far too slowly to actually observe it. He decided therefore to study evolution in a much faster medium, the digital computer. In 1991, he joined forces with the Santa Fe Institute in New Mexico to develop an evolutionary software platform called Tierra.
Genetic Algorithms, programs that simulate evolution to solve a specific problem, had already been well established, but Tierra was different. It wasn’t optimizing anything in particular. Small chunks of machine code were simply left on their own to replicate and compete for access to the CPU, and that was all. Occasional mutations in the copying process allowed evolution to take place. But, this wasn’t a simulation of evolution, these entities were actually evolving. What emerged from Tierra surprised Tom and most of the Santa Fe research team.
The first thing Tom noticed was that these replicating programs became smaller and smaller. A smaller program could replicate faster and so had an advantage over others. Some became so small that they evolved into parasites, tricking other programs into doing the copying for them. The hosts then evolved mechanisms to resist parasites. Some of the host programs were even able to trick the parasites into helping them. Eventually, a form of cooperation emerged where programs helped each other replicate. Then, free-riders emerged who took advantage of this group trust. All of this robust behavior, previously only observed in nature, emerged from Tierra automatically.
Tierra was groundbreaking for the field of Artificial Life, and inspired many systems like it afterwards, including the very robust evolutionary platform, Avida. Most importantly, it gave a demonstration of real evolution occuring in a medium other than nature.
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Nature’s Best
When building a robot that emulates an existing animal or other life form, engineers must accept the humble truth: nature always does it better. You just can’t compete with billions of years of evolution. Consider the agility of a simple house fly, or the fact that certain moths can identify pheromones when there are only a few molecules present. Some researchers have therefore concluded that it is far more effective to try and interface with some of nature’s technology instead of building it from scratch.
Jose Delgado pioneered the research into electrical brain stimulation at Yale in the 1940s by inserting electrodes into cats and monkeys to evoke responses in the brain. In a famous media stunt in 1963, he fitted a bull with a remote control device which stimulated a specific part of its brain. He stepped into the ring with the bull and let it charge him. At the last second, he activated the stimulator, stopping the bull in its tracks.
Since then, the military has gained interest in this technology as it could be used on insects for surveillance purposes. Various DARPA funded projects have succeeded in controlling insects and beetles, getting their wings to flap at different speeds based on a computer controlled electrodes inserted into specific parts of their brains. The big problem is trying to design components small enough that can be inserted into an insect’s body without disrupting its flight.
Chuck Higgins from the University of Arizona thinks a better approach is to turn the problem inside out by embedding insects into the robot. If a robot could leverage the chemical sniffing ability of a moth, for example, then it could be used by the military for detecting explosives. Higgins and his team have been able to build a robot that can read visual information directly from its passenger Hawk Moth’s brain using electrodes and amplifiers and send this information back into the robot’s control system. In the future, he’d like to integrate with other parts of the moth brain, including the olfactory system which would give his robots one of the most sophisticated sensory systems available.
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Hive Mind
A “super-organism” is a group of organisms that act collectively to ensure their own survival. For example, a beehive consisting of thousands of individual bees can be considered a single organism. The hive has a life of its own, usually living 8-10 years, while the individual bees that comprise it live only 1-2 months each. A hive also exhibits division of labor, similar to the organs of an animal, where groups of bees are responsible for specific functions. Reproduction is even specialized within the hive, as the Queen is the only bee allowed to reproduce. Individual worker bees are therefore selfless, working only to ensure the survival of the Queen and her DNA. Like a single cell in your body, a worker bee’s own survival is trivial as compared to the reproductive process of the organism as a whole.
But, does a beehive have a collective mind? For an outside observer, it would appear so. For example, when new sources of nectar are discovered nearby, the entire hive can be rallied into activity, as more foragers are sent to the source, and more storer bees are recruited inside the hive to handle the influx. Also, when it’s time to move the nest, the hive considers optimal locations by sending out scouts. Then, once a suitable location is chosen, the entire hive is quickly relocated in an organized fashion. No single bee has the entire plan. In fact, each bee only has a tiny bit of information about the activities of the hive as a whole, including the Queen. A beehive is therefore a good example of Emergence, where complex behavior can result from the interactions of a set of relatively simpler behaviors.
In the case of beehives, the key to generating complex behavior is based on 1) the concentric organization of the hive, 2) the presence of environmental cues, and 3) bees’ ability to communicate with each other. Hives are organized from the Queen outwards, and this physical organization will dictate an individual bee’s career path. When a bee is born, it stays close to the Queen, grooming it and cleaning nearby cells. Then it can be recruited to storage tasks, taking incoming nectar, pollen, and water and storing it in the honeycomb. Finally, the bee can be recruited to a scouting or foraging role outside the hive. Within the hive, each bee has access to certain “global variables,” such as temperature and nectar throughput. This information, combined with the bees’ ability to communicate with each other through the various “recruitment” dances, results in the complex behavior we see.
Artificial intelligence researchers are interested in emergent behavior as this might be a viable means of creating complex systems that exhibit intelligent behavior. Like the hive, the human brain is comprised of smaller, simpler units, whose individual behavior is simpler to describe. Examples of emergence abound in nature, but also in human societies, economics in particular. No single trader can fully understand the nature of the stock market as a whole, but the collective actions of traders together result in a complex system capable of maintaining efficient prices, and sometimes acting quickly and collectively in response to new economic environments.
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- Emergence
- Bee dances: Waggle, Tremble, Grooming
- Eusociality
Daisyworld
Is the Earth alive? The Gaia hypothesis proposes that living and non-living parts of the earth form a complex, interacting system that can be thought of as a single organism. When the British scientist James Lovelock first developed the idea in the 1960s, he was criticized for suggesting that the Earth somehow had a “collective will.” But in 1983, he created a mathematical model called Daisyworld, which demonstrated that it is possible for a planet to regulate its own temperature. This property of self-regulation, or homeostasis, is a complex process usually only associated with living things.
Imagine a planet with only two life forms: black daisies that thrive in low temperatures, and white daisies that thrive in high temperatures. This planet, called Daisyworld, orbits a sun that varies in energy output. Despite these changes, however, Daisyworld is able to maintain a relatively constant temperature, just as a warm-blooded animal exposed to different external temperatures does. How is this possible?
If the planet begins to cool, the population of black daisies increases. The surface of the planet becomes more black, absorbing more light, and therefore warming up. When the planet begins to warm, the population of white daisies increases. The surface of the planet becomes more white, reflecting more light, and likewise cooling down. These two opposing forces create a homeostatic balance, enabling the planet to maintain a relatively constant temperature.
Although the science of climate change is still in its infancy, it offers several real-world theories to explain how a planet maintains homeostasis. One hypothesis claims the Earth’s cloud system acts like the iris of an eye, allowing more light in when then the oceans are cool, and less light in when they are warm. Other homeostatic systems have been proposed to explain the earth’s ability to maintain a specific percentage of oxygen in the atmosphere. There are potentially hundreds of such Daisyworld-like mechanisms, which act collectively to ensure an environment suitable for life on Earth.
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