A Guide to the AI in Content Creation

Content can be tailored to fit within metrics set up by predetermined parameters (or rather, rules) that govern the creative process. By ensuring automated funnelling of relevant content, one can rely on achieving the desired level of engagement that will lead to business growth. Content producers operating within mediums such as music, film or TV have more freedom in the creative process of the work they create. They are able to take initiative in their output and build a cache of content that may not be as time sensitive as that of a weekly publication or digital advertising company. Time sensitive content, particularly when it comes to trending topics or content seeding, makes competitive advantage through AI, tech and content intelligence imperative. Time sensitive data, and the content created on its tailwind, are integral in the execution of plenty marketing campaigns in today’s world.

Content performance and analytics tools are the main driving forces behind crafted content strategies and schedules. Digital tools such as Content Management Systems (CMS) help guide smarter decision making on the type of content that target audiences find most interesting: exemplified by the level of user traffic or engagement generated.

Stacking multiple tech tools is a great way of ensuring multidimensional insight into the performance analytics of a campaign; such as the engagement and revenue generated by it. The options of such tools range in their complexity and scale but ultimately all aim to ensure that content ROI sees steady growth by enhancing user experience. Data deprivation can cause knowledge gaps around content, as well as existing and potential customers, and ultimately lead to a drop in engagement and traffic.

AI has become ultra sophisticated and hyper intelligent in its ability to take an enormous load of complex information based on a series of rules and communicate it in a hierarchical, understandable manner that makes it both user-friendly and adds value in the creative process.

For instance, Google Demand Signals, a predictive tool, analyzes aggregate data from a variety of online platforms to come up with predicted upcoming trending topics weeks in advance. This helps content creators, producers and marketers get ahead of the curve and gain competitive advantage in their strategies. Operating independently and/or alongside other tools such as Google Trends, creators are able to make informed decisions based on real-time data rather than on calculated assumptions.

The different types of AI are based on functionalities and complexities. Artificial Narrow Intelligence (ANI) is machine learning specializing in one area solving one problem. Artificial General Intelligence (AGI), essentially machine intelligence, is the use of computers to operate along the same level of intelligence as humans, tackling complex information along a preset algorithm.

This leads to Artificial Super Intelligence (ASI), which is an amplified version of AGI whereby machine consciousness substitutes human intellect in an arguably above average human brain given the complexity of its capacity and capabilities.

The three forms of AI include Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) – these components can function independently of each other but are better when stacked together. When stacked, advantages include: reduction in human error, availability 24/x7, efficiency in digital assistance and repetitive work, effective rational decision making and communication, as well as improved information security.

Machine learning involves computers thinking and acting to deliver tasks with less human intervention, whereas deep learning is about computers learning to think using structures modeled on the human brain and natural language processing is the analysis of data by computers based on rules set by humans.

Machine Learning (ML) teaches a machine how to make inferences and decisions based on past experiences. By identifying patterns, analyzing past data to infer the meaning of these data points to reach a possible conclusion without the need to involve human input in the process. This automation to reach conclusions by evaluating data, saves time and helps in better decision-making.

Deep learning is a sub-branch of machine learning and artificial intelligence (AI) that imitates the way humans learn and become more knowledgeable by processing a variety of data sets, and through efficient decision-making based on a set of rules. Whilst traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction with the aim of ensuring more precise results based on the rules set by the user.

Deep learning is part of a broader family of machine learning methods based on artificial neural networks (similar to the neural pathways of the human brain) with representation learning. Learning can be supervised, semi-supervised or unsupervised. Real world examples of deep learning include virtual assistants, vision for driverless cars, and face recognition, among many others.

Natural language processing (NLP) is the technology used to understand human language including lexical (structure) analyses, parsing, semantic analysis, discourse integration, and pragmatic analysis. Some better known application uses of NLP are Optical Character Recognition (OCR), Speech Recognition, Machine Translation, and Chatbots. The AI in NLP essentially comes down to an algorithm applying a combination of linguistics and natural language rules converted into a form of unstructured language data that computers can understand. With the help of AI, this data is then measured and then utilized for a variety of purposes like tailoring campaigns, enhancing customer experiences and such.

An example of this is Concured’s content suite, which personalizes user profiles in a more succinct manner. By utilizing NLP and ML to create a thorough understanding of individual customers for all clients in order to create more relevant content. Through the Personalization tool, it can provide smart recommendations based on consumer-centric metrics to outline a more detailed audience truth and thus give better direction for content seeding, and creation that boosts SEO ranking and enhances the user’s experience to drive engagement. The suite can work alongside tools such as Google Demand Signals, Google Trends to offer clients even more insight for more calculated decisions for the best performing content.

Ultimately, the digital revolution has taken over almost every aspect of business development, communication, content creation, and marketing. With increasing sophistication, AI has really facilitated the process of creating relevant content, products and services that enhance the daily lives of consumers everywhere.