>In most robotics and AI applications, calculations are unlikely to suffer greatly from inaccuracy but benefit from speed. FMA instructions are more accurate a

>In most robotics and AI applications, calculations are unlikely to suffer greatly from inaccuracy but benefit from speed. FMA instructions are more accurate and faster compared to a sequence of separate multiply and add/sub instructions, not less. The article literally talks about it in the beginning. The issue is reproducibility, which with floats can be quite bad even without FMA. Also both robotics and neural networks (I dislike the tendency to call it "AI") can be quite susceptible to errors. Gradient-based optimization algorithms can suffer noticeably from calculation errors. This is why you usually want to train ANNs using 64-bit floats.