What is Artificial Intelligence?
Artificial intelligence (AI), a branch of Computer Science, is a constellation of technologies grouping generally two (2) main technical fields namely, symbolic learning (SL) and machine learning (ML). Advances in hardware enable AI implementation. Such advances will likely facilitate innovation and thereby expand the field of AI.
AI attempts to mimic human intelligence. For example, a human navigates the outside world moving from place to place and all the while making decisions by using sensory data like sounds recorded by the ears, images and symbols captured by the eyes, temperature sensed by the skin, and smell perceived by the nose. Using these data the brain computes a reality within the temporal and spatial dimensions.
Conventionally, symbolic learning, which falls under image processing comprises two (2) main branches namely, robotics and computer vision.
Machine learning (ML) is also divided into two (2) main branches to wit, statistical learning, and deep learning. Generally, machine learning is used for classification (e.g., identify patterns with large volumes of business data) or prediction (smart algorithm). In pattern identification, if the machine is left to figure out the pattern, some in the industry refer to it as unsupervised learning. On the other hand, if a trained algorithm with an answer is used, then it’s referred to as supervised learning.
Statistical learning is further divided into speech recognition and natural language processing (NLP). Deep learning involves neural networks. Neural network as a field of AI has evolved into convolution neural networks (CNN), recurrent neural networks.
By way of background, the earliest neural net, the perceptron was developed in the 1950s. It was considered the first step toward human-level machine intelligence. However, a 1969 book by MIT’s Marvin Minsky and Seymour Papert entitled “perceptrons” prove mathematically that such networks could only perform the most basic functions.