Explaining Artificial Intelligence with Python
Algorithm explainability began with the first complex machines in the 1940s, the first being the Turing machine. Alan Turing himself struggled to explain how the intelligence of his machine solved encryption problems. Ever since machines have made calculations and decisions, explainability has been part of any implementation process through user interfaces, charts, business intelligence, and other tools.
However, the exponential progress of artificial intelligence (AI), including rule-based expert systems, machine learning algorithms, and deep learning, has led to the most complex algorithms in history. The difficulty of explaining AI has grown proportionally to the progress made.
As AI spreads out to all fields, it has become critical to provide explanations when the results prove inaccurate. Accurate results also require an explanation for a user to trust a machine learning algorithm. In some cases, AI faces life and death situations...
