Tag Archives: theories
Darkly Wise and Rudely Great
Imagine you are a great historical scientist and discover a disturbing truth about nature. Perhaps you discover that the world is not flat like a map, but in fact round like a tomato. Or, maybe you discover that instead of the Earth having a special place at the center of the universe, it is a just an ordinary rock floating in space – distasteful and troubling revelations.
The naturalist, Charles Darwin, was in such a situation in 1859. Although a devout Christian, Darwin’s pursuits led him to discover the mechanism by which life on Earth developed from a common ancestor, leading to the obvious conclusion that humans evolved from their closest relatives, the great apes. It was the most disgusting idea of the century, and he was personally troubled by it for the rest of his life.
The path Darwin’s life took made him a likely candidate to discover the mechanism of evolution. He had an obsessive-compulsive personality, avidly collecting and cataloging beetles and other insects around his home. And, his own grandfather, Erasmus Darwin, had already put forth the idea of common ancestry in a poem 60 years earlier:
“The Temple of Nature”
Organic life beneath the shoreless waves
was born and nors’d in ocean’s pearly caves;
First forms minuk, unsceen by spheric glass,
move on the mud, or pierce the watery mass.
These, as successive generations bloom
New powers arquire and larger limbs assume
whence countless groups of vegatation spring.
And breathing realms of fin and feet and wing.-Erasmus Darwin 1802
Most auspiciously, his family was affluent. His father, frustrated with Charles’ unending curiosity and lack of direction, eventually agreed to let him travel abroad with the HMS Beagle even though he considered it to be a complete waste of time. Darwin’s writings of the journey solidified his position as a well respected and well connected member of the scientific community.
He became close friends with Charles Lyell and John Dalton Hooker, the most influential geologist and botanist of his time respectively. Through Lyell, he came to see the natural world as a gradually changing process. Hooker was a trusted confidante with whom he could share some of his troubling revelations. After writing his first essay on natural selection, Darwin told Hooker it was like “confessing a murder”. Hooker gave him the calm, critical feedback Darwin needed to proceed.
The final push came from the naturalist Alfred Russel Wallace who wrote his own version of natural selection in 1858, ahead of Darwin. Both Darwin and Wallace had been inspired by the ideas of the Reverend Thomas Malthus who promoted the idea that population outstrips food supply, and both came to similar conclusions about how this creates selection pressure in nature. Hooker and Lyell arranged to have some of Darwin and Wallace’s ideas presented at a conference in 1858. This inspired Darwin to kick into high gear and finish “The Origin of Species” within a year, unleashing the most consistent and comprehensive explanation of natural diversity ever conceived, then and since.
Darwin did not doubt the literal translation of the bible as a young man, but bravely came to question the nature of the Christian God after observing nature in detail. His attitude was well summarized by his observations of the Ichneumonidae wasp. He wrote:
“I cannot persuade myself that a beneficent and omnipotent God would have designedly created the Ichneumonidae with the express intention of their feeding within the living bodies of Caterpillars, or that a cat should play with mice.”
-Charles Darwin 1860
Like Copernicus’ realization 350 years earlier that the Earth was not at the center of the universe, Darwin’s theory of natural selection pressed heavily against the religious views of his time. Though the religious conflict was never fully resolved in his own mind, Darwin lived to witness his theory gain acceptance in the scientific world and among much of the general public before his death in 1882.
Darwin might have found some solace in the poet Alexander Pope who foresaw the impingement of science upon religion. In “An Essay on Man”, Pope describes Man as passionate and ignorant, existing between God and beast, and that science is a valuable tool for understanding our nature, but not useful in answering religious questions:
Know then thyself, presume not God to scan,
The proper study of mankind is Man.
Placed on this isthmus of a middle state,
A being darkly wise and rudely great:
With too much knowledge for the Sceptic side,
With too much weakness for the Stoic’s pride,
He hangs between, in doubt to act or rest;
In doubt to deem himself a God or Beast;
In doubt his mind or body to prefer;
Born but to die, and reas’ning but to err;
Alike in ignorance, his reason such,
Whether he thinks too little or too much;
Chaos of thought and passion, all confused;
Still by himself abused or disabused;
Created half to rise, and half to fall:
Great lord of all things, yet a prey to all;
Sole judge of truth, in endless error hurl’d;
The glory, jest, and riddle of the world.- Alexander Pope 1734
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Plastic Brain
How does the brain learn? Understanding that process would allow one to write software that could learn the same way humans do. But, prior to 1949, no one had a very good answer. It had already been well established that the functional unit of the brain was the neuron, and the structure of these neuronal cells had been studied extensively. However, psychologists did not have a good theory about how neurons produced human behavior.
Enter Donald Hebb, a Canadian Psychologist who was fascinated by how the brain worked. Hebb postulated that neurons form cell assemblies, collections of neurons that act in concert to produce behavior. This idea formed the beginnings of the field of connectionism, an approach to the mind that views complex behavior as emerging from an interconnected network of simpler units. But how do these networks form? To answer this, Hebb proposed a mechanism which has come to be known as “Hebbian Learning.” The idea stated simply is: “Neurons that fire together, wire together.”
The brain is fully connected at birth, but the strength of these connections changes through time as we learn, forming the cell assemblies that Hebb theorized were responsible for behavior. Hebbian learning postulates that when neuron A activates, and then causes neuron B to activate, then the connection strength between the two neurons is increased, and it will be easier for A to activate B in the future. The idea sounds simple, but it goes a long way in explaining how neural networks form in the brain. Not every learning process in the brain can be explained by Hebbian learning, but it does provide an explanation of how complex networks of neurons could form.
After Hebbian learning made its debut in the 1949 book, “The Organization of Behavior,” it then became possible to program computers with the Hebbian rule, giving them the ability to learn. Today, many different types of artificial neural networks (ANNs) are used extensively in the field of artificial intelligence, including applications in face identification, speech and handwriting recognition, financial applications, data mining, and even autonomous vehicles. Hebb’s discovery spawned a whole branch of artificial intelligence and methods for constructing learning mechanisms on computers. ANNs are not yet sufficient for creating human-level intelligence on a computer though. Real neurons are complex biochemical engines, whose behavior can only be approximated with ANNs. Also, human brains come pre-configured to some degree, and without understanding this innate structure well, building large artificial neural networks is not practical.
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Evolution: Creativity Engine
Evolution is an inherently creative process. Left to its own devices, it will automatically generate complex and beautiful forms.
Only three ingredients are required for a runaway evolutionary process to take hold: replication, variation, and selection. In nature, DNA is the replicator. It is able to make high-fidelity copies of itself under the right conditions. Variation comes from occasional errors during the copying process, and selection comes from the environment as some life is better suited to live and reproduce under the extant conditions of the planet than others.
Interestingly, these three ingredients are relatively easy to program into a computer. Replication is something computers do inherently. Copying data from one place to another is their most basic function. Variation is achieved by artificially adding some randomness to the copying process, again trivial for a computer. Selection requires a little more work. The programmer must decide what makes some entities more “fit” than others which usually requires a simulation of some kind. The fitter entities are then given more of a chance to copy themselves.
My masters thesis at the University of Sussex in 2005 was to design running, springy robots in simulation (called Metapets). The design task itself was too difficult to solve on my own due to the complex interaction of the springs and coordination of the limbs although I tried. Eventually, I appealed to evolution to work out the details. The fitness of each robot was determined by how far it moved forward, thus selecting robots for speed.
At the start of the evolutionary process, most robots would just fall down and go into convulsions. Others walked backwards, went in circles, or just stood still. But, after letting evolution run for several weeks, more functional designs gradually emerged, and the longer I ran the simulation, the better the designs became.
Many of the designs that evolved were quirky looking, while others dragged their legs, walked on their elbows, or moved in ways that one would not consider intuitive. Therein lies artificial evolution’s primary caveat: it may provide a means of automatically solving a specific problem in a creative way, but not necessarily the way you intended or expected. When this happens, the evolutionary pressure can be refined by adjusting the “fitness function” which determines each model’s fitness score in the selection step.
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