When artificial intelligence was first conceived, it was not a newly introduced idea but rather the realization of an ancient concept.  As it is understood today, artificial intelligence emerged from humanity’s fascination with an invention that could translate human comprehension into tangible code.  Historically, humans have always searched for revolutionary innovations that will advance society by reducing the burden of manual input.  Among all modern inventions, artificial intelligence represents the pinnacle of passive efficiency since it poses the possibility of decision making without human participation.  Although this is tempting in theory, the introduction of AI into moral and ethical territories raises the concern that once this threshold is crossed, humans will not be able to backtrack this progress.
       	To understand the progression of artificial intelligence to its current position, it is crucial to trace the circumstances that provoked its creation.  According to preserved ancient texts, the idea that inanimate objects could be injected with a sense of consciousness has been contemplated by many societies.  Some of the earliest evidence of intelligent machinery is found within Greek mythology, specifically the stories featuring Daedalus and Hephaestus.  The most poignant of these myths is the creation story of Talos, a colossal bronze warrior that was engineered to police the island shores of Crete.  It is intriguing that even though this legend was written thousands of years ago, it foreshadows a contemporary ethical debate that concerns whether robots should be trusted with the responsibility of law enforcement.  This parallel suggests that the philosophy humans associate with intelligent machinery has endured centuries of diversifying generations.  Literary interest in manufacturing life continued thrive and arrived at another turning point in the publication of Mary Shelley’s novel Frankenstein.  Although Frankenstein wasn’t corporeally mechanical like Talos, the emphasis remains on the prognosis of the interactions between humans and recreated intelligence.  Another significant intersection of these two stories is the failure of either of the creations to fulfill the expectations of their creators.  Both attempts at artificial life succeed in gaining a form of intelligence, but they do not properly integrate into society.  Therefore, it is established that even if it is possible to instill a sense of self into inanimate objects, the resulting creation will likely follow an unexpected course of action.
       	The combination of manufactured sentience in literature, human instinct, and technological advancement eventually lead to the inception of basic artificial intelligence in the 20th century.  Alan Turing was a compelling voice during the rise of the artificial intelligence movement and proposed a scale that examined intelligent behavior in machines.  The ultimate goal of the Turing test was to evaluate whether it was possible for technology to “imitate a human to the point where a suspicious judge cannot tell the difference between human and machine” (McGuire, The History of Artificial Intelligence).  This was considered an incredibly progressive thought during the 1950’s and remains relevant in the 21st century.  The criteria for the Turing test was that if a computer could pass as being “indistinguishable from an intelligent human,” then it could be concluded that machines could think (McGuire).  Although the completion of this test was inconceivable when it was introduced, it was essential to the creation of the field of AI since it defined a fixed goal.  Alan Turing’s research also raised many enduring questions and objections about the capabilities of machines. Does impersonating a human actually prove sentience, or is it simply a the result of solving a complicated logical puzzle?
Upon the development of AI technology in the late 1950’s, it was evident that computers were decades away from the extravagant forms of artificial intelligence discussed in literature.  One study that began to push the boundaries of AI developments past algorithms was the exploration of computerized chess.  Hopeful engineers believed that if a computer program could master the strategies of chess and outthink humans in matches, then artificial intelligence would be a success.  Unsurprisingly, it wasn’t that simple.  A recurring issue in AI progress was that although the machines were able to store information in their hard drives, they would never truly be able to understand what it all meant.  Robotic chess was divided into two approaches, the brute force approach (Type-A) and the strategic approach (Type-B).  It was a disappointment to the scientific community that Type-A fared better than Type-B because that implied less of a human understanding within the programs.  Type-A programs are engineered to “analyze every possible move as quickly as possible whereas a human chess master might only consider a few moves at any given moment” (Murphy, “Checkmate”).  This algorithm relies more on computer speed and storage than a genuine understanding of the game of chess.  To truly demonstrate intelligence that is comparable to humans, it would require that AI chess programs actively learn from their opponents.  This landmark in engineering continues to demonstrate humanity’s desire to devise an invention that can independently keep up with the human mind.
As the conglomerate of technology rapidly expanded and improved in the successive decades, expectations for artificial intelligence also grew.  During these years, engineers began exploring the possibilities of imitating the physiology of humans in robotics.  The development of the Stanford Arm and the Silver Arm in the early 70’s were inventions that revolutionized the process of machine assembly.  Emulating human anatomy in machines suggested that in the future, it would be possible to program apparatuses that could both think and move like a human.  In 1970, the first locomotive robot that was governed by artificial intelligence was created.  SRI’s Shakey robot was “equipped with sensing devices and driven by a problem-solving program” to respond to its environment and maneuver itself with ease.  Later on in the early 2000s, Honda will be the first developers that have succeeded in creating an interface that combines humanistic physique with reasoning skill.  The achievement of Honda’s AISMO catapulted AI research into where it currently stands today.  Although this breakthrough permits humans to rely more on intelligent machinery, it is accompanied by moral dilemmas that question the need for the mechanical counterpart of humanity.
Developments in machine learning marks a new era for the applications of artificial intelligence as technology enters an age of handheld computing systems, massive search engines, driverless cars and drones.  Advancements in mechanical cognition that were unthinkable decades ago are now able to grow by interacting with the multimillion person user base of the internet.  Commercial websites such as “Amazon and Netflix use machine learning to suggest products that you might like to buy” which reflects that computers now have the ability to listen and understand through algorithms (Jeremy Howard, “The wonderful and terrifying implications of computers that can learn”).