Inside the black box: Understanding AI decision-making

Neural networks, machine-learning systems, predictive analytics, speech recognition, natural-language understanding and other components of what’s broadly defined as ‘artificial intelligence’ (AI) are currently undergoing a boom: research is progressing apace, media attention is at an all-time high, and organisations are increasingly implementing AI solutions in pursuit of automation-driven efficiencies. [1] [1] By Charles McLellan for ZDNet

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