e? Probably not, but more general applications of inference algorithms will - this is mechanised automated, mobile metadata inference value. Revenues from Image and Audio and Memory and Knowledge Sharing - The Role of Mobile Metadata and Similarity Processing Algorithms Inference algorithms potentially create new application value opportunities and add value to existing applications. Downloadable Druid ring tones spring to mind from the above example - a rather specialist market. 'Similarity processing algorithms' are related to but different from inference algorithms. The job of similarity processing algorithms is to detect and exploit spatial, temporal and social commonalities and similarities or occasionally, useful and relevant dissimilarities (the attrac- tion of opposites). The relationship with inference algorithms is clear from the example above. If the network knows I am a Druid (I am not but would not mind if I was) then the presumption could be made that I have common and similar social interests to other Druids. I am part of the Druid community that in turn has affiliations with other specialist communities. We may live in similar places. Before long, an apparently insignificant addressable market has Making Telecoms Work become more significant. Not only does the network know that I am a Druid, it also knows that I attended the 2011 summer solstice and may want to share memories of that moment with other members of my social community. This is the cue moment for the suggested recipients list for any image or video and/or audio recording I may have made of the event or for the sharing particular cosmic thoughts that occurred to me at sunrise. This is sometimes described in the technical literature as 'context to community' sharing. The suggested recipients sharing list might include other potentially interested and useful parties. A parallel example is the solar eclipse that happened in April 2006 in Africa and Central Asia. This spawned a swathe of specialist web sites to service the people viewing a