History of Computer Science: Artificial Intelligence

“Artificial Intelligence research is an attempt to discover and describe aspects of human intelligence that can be simulated by machines.”

Philip C. Jackson, Introduction to Artificial Intelligence

Artificial Intelligence is a popular industry within technology today, but its history has been a rocky one filled with many transformations and long lulls between rapid booms. The definition itself has even evolved over the years in response to various innovations and setbacks. While the goal of AI has remained fairly constant, our understanding of that goal and innovations towards it have set milestones in its history. Two main types of AI have emerged over the years based on both limitations and advances: strong and weak AI.

Originally, artificial intelligence was the purely theoretical concept of machines that can mimic human learning and thoughts. This is now considered strong AI and while it ultimately remains the goal of the discipline, there is still a long way to go before it can be achieved. In the 1940s, early research involving modeling neural networks structured like mammalian brains helped introduce the discipline and shape further research pursuits. It was the Turing Test in the 1950s, however, that truly marked the popularity and rise in research into strong AI. Alan Turing devised a test that has become the standard for what would be considered (strong) artificial intelligence. A person at a computer terminal, called the interrogator, would be able to chat to two different agents, one human and one the proposed AI system. If the interrogator could not distinguish which agent is human with any significant probability the proposed system would be considered to exhibit artificial intelligence. This is more or less still considered an applicable test for strong AI and other tests have even been derived for it as our understanding of AI and the definition of it has evolved.

Over the next couple of decades some advancements were made in the field, particularly with problem solving applications and testing environments. Computer vision, robotics, and natural language processing in particular had advances in testing environments. By the 1970s, however, the rate of advancement in artificial intelligence simply wasn't keeping up with the demanding expectations held for it at the time. Despite the advances that were made, the practical applications were very rare and interest started to decline.

This lull lasted about a decade, but research continued and by the 1980s the sales in latest hardware and software for AI started to rise as the much needed practical applications for it started to rise. These applications led to the concept of weak AI where systems that were not intelligent in the strong AI sense but involving techniques considered vital to such a system and exhibiting intelligent qualities were used by themselves to solve a variety of interesting problems. Speech recognition and learning systems were two of the first areas that started to see an increase in such practical applications, followed by automatic programming systems that could write themselves based on desired behavior and many of the statistical and graph-based approaches that are widely popular today.

Through the 1990s and into the millennium many new applications and areas emerged, including machine learning, planning systems, and search systems. The use of weak AI techniques and systems has continued to increase to this day and now is very widespread with some major companies such as Google, IBM, and Microsoft leading the way in new research and applications. With this continuing trend, the possibility of strong AI once again emerges as a goal within the field. The hopes and expectations have even risen, such as the concept of The Singularity—a concept attributed to Vernor Vinge in which the intelligence of computers not only meets but exceeds that of humans. It is difficult to say if or when a truly intelligent system will be created, especially as the understanding of what properties such a system would be expected to contain evolves along with the techniques that might lead to such a system, but the field of artificial intelligence is certainly in interesting one that is not likely due for another decline in the foreseeable future.

“AI has the potential to change our world like no other technology.”

M. Tim Jones, AI Application Programming