The advent of #MachineLearning as related to social listening precipitates a lot of honest-to-God hard work during a transition from human-led to machine-led insights. This will happen because many humans will be needed to create custom attributes and custom classifiers for the machine to use in the new upcoming #AI driven insights industry. Humans teaching the machine to think like we do, to analyze like we do, to understand nuance, culture and the evolution of language (keep in mind, social tech experts, I am referring to a truly global machine analyzing the languages of the world…not just English).
Since there are many gaps in social data, we face an uphill in pairing other data sources to the snippets we get from public sources of conversations. The key to a complete “AI Social Data Intelligence Machine” will be sources such as Axciom, census data, background check sources, private networks like Doximity/A Small World and finally, if possible, the nexus of data from Facebook and Amazon. This particular nexus is a kind of holy grail that the leading machine intelligence experts will eventually open to brand leadership. But previous to such events, we have a grueling journey as analysts, insights researchers and programmers to “teach the machine how to be a human”.
One of the most exciting branches of this “teaching” will be a self-aware machine, an artificial mind that will begin to offer recommendations…and then may decide to act upon these recommendations.