The Exhibition of the Society of Model Engineers at London’s Royal Horticultural Hall. On the stage sits a metal, human figure. The machine stands, bows, and opens the exhibition with a four-minute speech. The mechanical man is lifelike, making hand gestures as he speaks, looking left and right to address the entire audience. After his speech, he returns to his seat.
You’ve just read a true story from 1928.
One of the world’s first humanoid robots, Eric was such a great success that he was taken on a world tour, inspiring people with this new and exciting technology. robots were a symbol for what couldbe achieved with machinery. A mechanical man that could wave at an audience has limited practical uses, but the meaning of that development was imperative; people looked at the first robots and asked themselves – what next? If those same hands can be built to equip tools, perhaps they could assist in manual labor, perhaps they could facilitate mass production.
What the model engineers of the early 20th century probably couldn’t envision was the more commonplace bots of today; they don’t move in metal shells but instead serve from within computers. Now, as “machine learning” gains traction in marketing technologies, the same question arises – what next? What can be done by us, as people, with computers that can think? We thought about these very possibilities – hopefully, we didn’t run away with our imaginations too much.
Structure the unstructured
We’ve been banging on about this for a while, but bear with us. The amount of data that is produced daily is incredibly vast. The majority of that data never gets effectively utilized since 80% of it is unstructured. For example, a database sets up a structure, and the data it retains then adheres to that structure. However, for the fragments of data floating around the articles, reports and social media posts on the web, there is no structure to unify them. AI is capable of applying a structure to that scattered data, allowing us to sort and utilize it.
What this means
Current automated content creation tools like Wordsmith are dependent on structured data, being fed in the right format. AI will allow us to generate content based on unstructured data. Imagine that before launching a campaign, you could run an automated report on what your key demographic has been engaging with most in the past week, on what channels and from what locations. Not only would you be able to produce a current, insightful report in moments, but the AI technology behind this could even identify patterns and links that would otherwise go undetected.
Predictive analysis uses data on what has worked before, but cognitive marketing technology can utilize artificial neural networks and algorithms to make predictions based on patterns that we would have otherwise missed. This is invaluable for CMOs needing to process large amounts of data to get a concise picture of their customers’ interactions.
A quick predictive timeline of how the use of AI will continue to develop: