Artificial Intelligence: A Journey Through Time
A Philosophical Undertaking
When I first heard the term artificial intelligence, I thought about machine learning and self-driving cars. I did not clearly understand all the fields it cuts across and encompasses. I knew that the field involved the pursuit of understanding and building intelligent entities. To learn more about this universal term that encompasses everything from the general (learning, perception) to the specific (driving a car, diagnosing a disease), I had to go back to the basics of what artificial intelligence is all about.
This endeavor took me from the alleys of ancient Greece to the war-rooms of the world wars. Here is a rundown of the most interesting concepts I came across.
The core principles along which any intelligent system can be developed are rooted not in the technological breakthroughs of the modern world but in the development of theories about thought processes and reasoning that go back millennia. So let's get philosophical.
Artificial intelligence systems are founded on philosophies that outline a formal process through which information is gained, extrapolated, and acted upon. Aristotle was the first person to lay out a precise set of laws governing the rational part of the mind by proposing a system of syllogisms. A syllogism is when deductive reasoning is used to arrive at a conclusion based on assumed prepositions to form a logical argument. Ramon Lull then suggested that reasoning could also be carried out by mechanical artifacts and Thomas Hobbes was the first to suggest the possibility of an artificial animal. As philosophers developed on the philosophy of rationalism, we fast forward a few centuries to the confirmation theory, the first theory of mind as a computational process. It suggests that there is an explicit computational procedure for extracting knowledge from experiences. These two functions make up the bedrock of any intelligent system.
The pursuit of thinking and acting humanly
The field of artificial intelligence intersects with the field of cognitive science, wherein computer models from AI and experimental techniques from psychology are used to construct precise and testable theories of the human mind. This cognitive modeling approach helps us gain an understanding of how humans think.
The pursuit of thinking and acting rationally
Artificial intelligence systems are designed to act rationally. Rationality is an ideal performance measure, and a system is rational if it does the “right thing” given what it knows. The right thing here means the conclusion that a given action will achieve the desired certain goal. This requires logic, which is drawing correct inferences and reasoning. For a system to act rationally, it must first think logically.
Apart from philosophy, the other disciplines which are essential to developments in the field of artificial intelligence are mathematics, engineering, economics, neuroscience, psychology, and linguistics.
A Historical Undertaking
In 1950, Alan Turing proposed an operational definition of intelligence: if a machine can exhibit behavior equivalent to, or indistinguishable from, that of a human - it is intelligent. This became known as the Turing test, which can only be passed when a system possesses the following capabilities:
- Natural language processing to enable communication
- Knowledge representation to store information
- Automated reasoning to use the stored information to draw conclusions and answer questions
- Machine learning to adapt to new circumstances and extrapolate patterns
In 1950, two students at Harvard University built the first neural network computer known as the SNARC. They used 3000 vacuum tubes and surplus automatic pilot mechanisms to simulate a network of 40 neurons. This was one of the earliest systems that can be classified as AI.
The initial decades following the inception of the field saw the rise of many theories and systems that grew and changed over time. The field of AI was conceptualized at a two-month workshop in Dartmouth College in 1956 which brought together people such as John McCarthy (Princeton), Newell and Simon (Carnegie Tech), Arthur Samuel (IBM), Oliver Selfried (MIT), and others who would dominate the field in the coming decades. AI became a separate field, rooted in the methodology of attempting to build machines that will function autonomously in complex environments. In the 1950s, the first AI programs were developed at IBM, which proved mathematical theorems and played checkers against humans. At MIT, McCarthy defined Lisp––which became the dominant AI programming language for the next 30 years.
Such early programs were limited because they succeeded by means of simple syntactic manipulations. The problem with these systems remained that just because a program can find a solution in principle does not mean it contains the mechanisms needed to find it in practice. They could not be scaled. The solution to this problem was the development of domain-specific, knowledge-intensive systems in the 1970s such as DENDRAL which separated knowledge from reasoning. The 1980s saw the commercialization of AI. In the span of a few years, the AI industry had grown by billions of dollars in value as corporations began to build expert systems. The field has had increasing interaction with other disciplines to deal with agents.
Today, the field is firmly rooted in the scientific method. AI is not only about proposing theories but about focusing on their real-world applications. And that is where we conclude our journey of AI over the years. The field today looks vastly different than when it was initially conceived––a testament to human development.
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