With computers taking a toll on humans, there will be a time when AI will completely take over us and our jobs.
“While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail,” according to McKinsey Quarterly.
Professions that require empathy and understanding like therapists, psychologists and roles that rely heavily on social and negotiation skills, like managerial positions, are less vulnerable to automation.
But for those who are in the creative field and require logical thinking and original ideas, like content creation, are also deemed at a comparatively lesser risk of having to lose their jobs due to these hard working, smarter and cheaper to manage resources. To create a machine that can generate great content and ideas which are worth reading as well as consuming is a tough job. Isn’t it? To assume such a thing will put us in the dark as the reality is leaning somewhere else. We do have machines that are already writing content and are good at it.
In fact, Gartner predicts, “By 2018, 20% of all business content will be authored by machines.”
To some relief, it is safe to say that business content is not the same as creative content. Content marketing without humans would rob it of its most important aspect —-creativity. Artificial intelligence may be able to replace human effort to some extent, but creativity is where this debate stops.
Disturbingly, this might not be as clear-cut as most content marketers would likely hope it to be. Advanced computer-based products and tools already exist which are capable of producing amazing products.
For example, deep neural networks, computer systems which are designed to replicate the underlying synaptic structure of the human brain do exist. Advanced systems like these have already managed to create pieces of music, and ING’s Next Rembrandt project used A.I technology to loyally generate an eerily accurate, entirely new painting in the style of the master artist.
What exactly is content automation?
Major leaps forward may occur as natural language processing (NLP) develops. NLP refers to a machine interpreting what human language means with an acceptable degree of accuracy.
Natural language generation (NLG) is an AI technology that takes structured data and converts it into text. The data is translated according to the translation rules coded by the system’s creators. Thereby humans are themselves teaching the NLG system the relationships between data points. In some usages, NLP might also include elements of NLG, as a machine processes language, then generates more of it based on its understanding of a text. Siri and Alexa are two examples of consumer technologies that combine NLG and NLP.
For example, the Associated Press and Automated Insights work together to code and hence teach the Automated Insights system each rule for successfully translating financial data into a text-based earnings report that is very similar to one written by a human.
Here’s an example of a command they can code into the system: an increase of 30 percent or more when comparing two data points over a period should be written as “a notable increase” in the final report. Once that command is entered, the system would automatically refer to an increase of 30 percent or more as “a notable increase” when presented with properly organised data. Thousands of human-coded rules like this work together with the machine’s various other algorithms to create full written reports from the codes and data provided.
These NLG systems already exist. However, they require extensive work to tailor the system to particular content needs. An earnings report is relatively easier to break down into its constituent parts: report rises and falls in various metrics, which are linked together by commentary about each rise and fall. Every time a different type of narrative is needed, the AI tool or platform must be customised to produce that data. Once it is, then many more descriptions of that type, generated from a structured dataset, can be automatically created.
Is your company ready for content automation?
The usefulness of content automation for a brand depends heavily on its situation, scale and feasibility.
If you do not require content at scale, then it might not be worth the effort or time to customise NLG technologies for automation. Is your brand willing or can it invest beyond the initial setup of NLG? Additional editing, tweaking, and improvements may be required. An NLG even needs another limb of social content specialists to manage the machines. Can your brand procure or produce the talent to implement NLG? NLG is not a regular writing job. Its implementation requires a solid knowledge of rules-based and branching logic as well as a low grasping power of primary narrative mechanics. Tracking the growth of A.I. over the years does bring us to a vague answer to the question of whether A.I will be able to replace the job of a content marketer.
Recognising audience, defining content purposes, creating informative and quality content, and assessing results to inform future projects. These are a few tasks that A.I. will gracefully take from our hands in the future.
Content marketers need not panic just yet. There is a long way yet to let any computer being able to do these things in correlation with one another. The sheer complexity of a good marketing strategy does involve human emotions and smartness, which these machines are still to be prepped for.
Content marketing still gets to stay given the fact that Artificial Intelligence may be getting smarter, but at the same time, it has its limitations as well. They are built for humans and should be used by them in the way demanded. Computer systems are combating with 1’s and 0’s but still lack the perfect algorithm for a marketing strategy.
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