-end camera phones. Autoexposure gives you 3 http://www.studyblue.com/#note/preview/632416 Information-Centric Software clue though not definite proof that it is day or night, autocolour balancing and autoexposure together give you a clue though not definite proof that the image is being taken in daylight outdoors on a sunny or cloudy day or indoors under natural, tungsten or fluorescent lighting. Autofocus gives you a clue though not definite proof of distance and spatial relationships within the image. Two cameras mounted a couple of inches apart and two microphones six inches apart would of course greatly increase the spatial relationship capture capability, a function beginning to be included now in 3G smart phones. People have social signatures, these are specific (voice and visual) and physical (my lap top has fingerprint recognition which I am impressed by but have never used) and general, for example behaviour patterns. The Role of Audio, Visual and Social Signatures in Developing 'Inference Value' Now these are interesting capabilities and necessary for higher-end camera phone functionality but do they have a wider application value when combined with network connectivity? Well yes in that they can add value to 'inference algorithms'. 'Inference algorithms' are algorithms that combine spatial, temporal and social information. Their job is to 'disambiguate' the context in which, for example, a picture has been taken or an observed event is taking place. So the fact that it is 4.00 a.m. (temporal) on the 21 June (temporal) and that you (personal signature) are somewhere on Salisbury Plain (cell ID spatial) looking at the sun (autoexposure and autowhite balance) rise over some strangely shaped rocks (image statistics and object recognition) at a certain distance away (autofocus) listening to some chanting (audio signature) tells the network with a very high level of confidence that you are a Druid Priest attending the mid summer solstice at Stonehenge. Does this particular example illustrate realisable valu