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Inside the black box: Understanding AI decision …
Inside the black box of shared decision making ...
Technology
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. The first thing to establish is what we're not talking about, which is human-level AI -- often termed 'strong AI' or 'artificial general intelligence' (AGI). A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C. Müller and Nick Bostrom reported a 50 percent chance that AGI would be developed between 2040 and 2050, rising to 90 percent by 2075; so-called 'superintelligence' -- which Bostrom defines as "any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest" -- was expected some 30 years after the achievement of AGI (Fundamental Issues of Artificial Intelligence, Chapter 33).
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