Intelligent Artifacts may be found in literature since Greek mythology but only after World War II, when modern computers became available, it has become possible to create programs that perform difficult intellectual tasks.
Artificial Intelligence is over half a century old and for a very long time it was dominated by rule-based systems, a field whose proponents were called Symbolists. In contrast during the 1980’s a new kind of AI began to emerge called Machine Learning. However the big disruption has occurred this decade with the desployment of the “Deep Learning” approach, a broader family of machine learning methods based on learning data representations through the design of neural networks.
In his book “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World”(2015) Pedro Domingos outlines five tribes of research approaches in Machine Learning: inductive reasoning, connectionism, evolutionary computation, bayes theorem and analogical modelling. The author explains that each different perspective has the potential to contribute to a unifying “master algorithm” that will design the “ultimate learning machine that will remake our world”.
Software Architect, enterpreneur, and writer Carlos Perez says that Deep Learning in Artificial Intelligence is a extremely disruptive technology that involves the interplay of Computer Science, Physics, Biology, Linguistics and Psychology. The ramifications to society and even our own humanity will be profound, therefore the compelling need to think about its implications.
In the following infographic we can learn more about how AI research is not a homogenous field, but rather a field in constant tribal warfare… Which side is yours?
*(Pictures: Timothy Allen & Intuition Machine Inc.).