Much has been written about artificial intelligence and machine learning, yet there are still far too many who neither understand the difference nor comprehend the applications of these growing technologies. Some of this is to be expected, as the two fields are changing rapidly to meet the demands of application developers, system engineers, and business in general. Still, the initial academic inquiries into these two subjects established a body of knowledge that has formed the foundation for all the study that has taken place since.
Programming a computer to make decisions based on an arbitrary set of data is not artificial intelligence. Computers make “decisions” billions of times a second. The transistor is essentially a decision engine, it can be configured or controlled in a manner that simulates decision making.
Artificial Intelligence, or AI, on the other hand, is a system that poses questions. When a computer correctly recognizes the necessity of a question, that is the first step towards intelligence. The answer to the question is by definition academic at the point where the machine correctly recognizes the conditions that must give rise to it.
Ultimately, AI is far more an academic concept than it is a practical application of computer science. It exists when an arbitrary set of conditions are met, and those conditions can change based on the application at hand.
When a machine is said to be “learning,” more often than not, it is refining either the set of data being fed to a standardized algorithm or it is refining an algorithm to derive better efficiency or more accurate results from a set of standardized data.
Machine Learning is a process that produces greater efficiency, greater speed or more accurate data. It is AI’s counterpart in most any construct or system designed to investigate a source of information. Artificial Intelligence and Machine Learning can be designed to work together depending on the kinds of problems they are being asked to solve. AI asks the questions, and machine learning produces the best possible answers. Properly utilized, the two processes can form a positive feedback loop, which would be considered an emergent property of an artificially intelligent machine.
Computer science, by and large, is far more concerned with the theoretical applications of microprocessor-based electronics than it is with the practical limits of the same technology. What is clear from the research, however, is that AI and Machine Learning are most likely to produce progress if they are properly understood and implemented.