The quest to create mechanical creatures goes back to the ancient Greeks, but the concept experienced a revival at the end of the Renaissance. Around 1640 Descartes put forth the idea that the human body works like a machine and could be understood as such. The idea that nature can be viewed as a mechanical process was solidified in 1687 when Newton published his Principia. In it, he describes in detail how nature follows mathematical rules. Indeed, Newton viewed the universe as a massive clock built by god and set into motion. These ideas were a precursor to the Industrial Revolution and also made clockwork automata a fad in Europe in the early 18th century.
Jacques Vaucanson [1709-1782] was an unsung hero of the Industrial Revolution. The invention of the mechanical loom is usually credited to Joseph Jacquard, but it was Vaucanson who first came up with the idea of using punchcards to store textile patterns, a technology that would be used in the first computers 200 years later. Vaucanson also build the first functioning automaton, a mechanical flute player that emulated a human being. The lips and fingers of the player moved naturally on the flute, and he painstakingly copied the musculature and breathing of a human. Its breath could be felt emanating from the mouth as it played.
After the success of the flute player, Vaucanson built an automated tambourine player, and finally his most famous work, a mechanical duck in 1738. The duck was made of gilded copper and contained over 400 moving parts hidden from view. The duck could drink, eat, quack, splash about and even defecate. Vaucanson used a new high-tech material, rubber, to design the ducks digestive system, and thus developed the world’s first flexible rubber tube. It was later discovered that the duck did not actually defecate as the “feces” were stored in a separate compartment, but this did not diminish the magnitude of his masterpiece.
Vaucanson was a showman and toured througout Europe with his duck, charging admission and wowing audiences with his creation. No-one had ever before seen a mechanism which appeared so alive. He eventually caught the attention of the French government who hired him as inspector of the manufacture of silk. It was during this time he invented the first fully automated loom which used punch cards, the machine later improved upon by Jacquard. The silk workers of Lyon rebelled against Vaucanson’s automatic loom by pelting him with stones in the street, insisting that no machine could replace them. This foreshadowed the later anti-industrial sentiment of the Luddite movement in Britain. Sadly, Vaucanson’s original automata were lost to history, but a replica of the duck is now kept in the Musee des Automates in Grenoble, France.
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an·thro·po·mor·phize to ascribe human characteristics to things not human. 
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.
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 
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 
