In “Build Deeper - Deep Learning Beginner's Guide by Thimira Amaratunga
This book confirms other predictive system results that I have seen, where it has often been found that we human as a species who fancy ourselves as psychics or using other la-di-da methodologies can at best achieve around an 80% accuracy rate, even with good regular practice and tuning. The more accustomed you are toward reaching ever higher accuracy & precision percentile targets the more the distance to the next little increase in goal horizon. Still it does bring into question the abilities of Science and machine systems designing new machine systems, often through excluding what are regarded as unrepeatable subjective methods in favour of repeatable objectiveness. Outliers and other non-obvious patterns & so on are pushing back the boundaries at the edge of our cultural belief systems.
I don't think that any computer scientist would dispute the point that modern AI or machine learning is nowhere near the threshold of 'consciousness' or even 'general intelligence'. But it's not uncommon for words to have a different meaning within a technical field compared to how they are used in everyday communication. In regular English 'chaos' means unpredictable, whereas in mathematics it refers to the tendency of sensitive nonlinear systems to exhibit emergent attraction basins that can potentially be extremely predictable. Those are arguably even antonyms. Another example would be terms 'deterministic/nondeterministic' in Computer Science, which also differ strongly from their meanings in regular English. The point is that if you feel the need to grandstand on these trivialities, you clearly don't understand the fundamentals of the subject matter under discussion.
If you're into Computer Science and Machine Learning in particular, read on.