Many electronic sensors will detect physical phenomenon with greater speed, dynamic range and areas of the electromagnetic and audio spectra, which cannot be detected by human senses. The data can also be calibrated against known references for accurate scientifically measurable values.

The main problem is that these are just data sets not information or intelligence data sets on which to base a decision making process. Artificial intelligence algorithms are still quite laughably immature. An example would be depth perception and edge detection. Humans process millions of visual data sets with ease everyday and although there have been improvements in such devices such as IR guidance systems for anti-aircraft missiles and X-box games controllers, they cannot compete with the wide aspect visual and speed capabilities of the human visual cortex.

Again music and speech audio are dealt with easily providing meaning to the human listener, but to an electronic system is just meaningless white noise.

Having worked and developed algorithms for Dynamic thermographic, laser Doppler flowmetry and photoplethysmography imaging for medical devices, the difficulty was always the interpretation of the data sets to pull out the information relevant for the decision maker. Inevitably the use of statistical methods such as non linear regression analysis was required. i.e. a best guess.