Creative machines: How close are we to AI-generated content marketing?

We typically think of artificial intelligence (AI) within our industry in terms of processes and calculations. Media buying, for example, is ripe for intervention by sophisticated algorithms and machine learning systems.

The commonly-held assumption is that developments such as these will free up humans to spend more time on creative tasks, like campaign strategy and content production.

But as we move from rule-based automation to true AI, should we believe that creativity will remain a singularly human pursuit? How close is artificial intelligence to being able to carry out the role of a content marketer?

Gartner predicts that by 2018, “20% of all business content will be authored by machines”. Elon Musk thinks computers will be able to do anything a human can “by 2030 to 2040”; and just last week Google invested over $800,000 in the Press Association’s initiative to generate news stories solely through the use of AI.

Looking at statistics like these, the march towards AI-led creative content seems certain, but the reality is a little more complex. In this article we’ll look at what it would take for an artificial intelligence to generate truly creative content marketing, and how close we are to living in a world with AI content marketers.

What do we mean by ‘content marketing’?

‘Content generation’ can be a rather nebulous term, but we can broadly break it down into three areas: coming up with content ideas, producing the content, and promoting the content.

Content itself is an ever-shifting field, meaning anything from a tweet to a Hollywood blockbuster and billion other things in-between.

For the purposes of this article, let’s keep this restricted to a typical content marketing campaign that involves idea generation and content production in the forms of a video, some text-based articles with accompanying images, and social media promotion.

Within that scope, we require a combination of problem-solving, critical analysis, and original thinking to come up with an idea that will resonate with the target audience.

There are levels of independence for an AI program, which is what we should really be trying to ascertain here. Our questions are therefore: To what extent can artificial intelligence undertake and combine these specialisms, and will the result be the equal or superior of a human effort? And can a machine be creative, when we aren’t completely sure of the formula behind our own creativity?

AI and content marketing today

AI is normally deployed for logistical tasks in marketing, as its computational abilities far exceed our own. These rule-based systems can exponentially increase the efficiency of media buying, for example, leaving us to get on with the more strategic or creative tasks.

Content ideation, creation, and dissemination are tasks that tend to fall within the category of the arts and, as a result, seem less prone to mechanical disruption. The machines crunch the numbers, we’ll come up with the ideas, then the machines can personalize the message and target the right customers.

This attitude comes through clearly in a recent survey of CMOs by eMarketer:

There is a clear trend here; marketers expect AI to work with a pre-existing message, tailoring and targeting it for each medium.

The areas expected to see an impact from developments in AI can broadly be categorized as data analysis and online experience customization. These require stimulus materials, such as landing pages on websites or videos. And these, of course, arise from the deep and mysterious wells of human creativity.

Some efforts to introduce elements of AI into content generation have been quite effective, albeit limited in their ambitions. Platforms like Quill and Wordsmith, for example, offer automated content generation which is very useful for creating declarative content or product descriptions at scale.

Undoubtedly, a machine can scan news headlines, assess page-level traffic data, and decide which headlines will be most likely to generate clicks in future.

Whisper recently re-launched, too, with an AI-driven content customization engine, and the added security of some human editors behind the scenes.

As noted earlier, Gartner predicts that 20% of all business content will be AI-generated by 2018. That sounds like a big increase within the next 12 months, but we need to understand the concept of ‘business content’ as opposed to ‘creative content’ to gain some perspective.

Finance content, such as quarterly earnings reports, is often written by a machine. There is simply no need to inject personality into this content, so a computer is up to the job.

This is why Google’s recent investment in the AP’s artificial intelligence program will be intriguing to marketers. There is an opportunity to automate some news stories without people noticing the difference, but it will be fascinating to see if this develops beyond informational content.

A study (‘Enter the Robot Journalist’), featured in the New York Times in 2015, adds a bit more color to this observation.

The ‘Turing Test’ is often cited in relation to these questions, and a variation of this assessment was used in the study. In the Turing Test (named after English mathematician and AI-pioneer Alan Turing), a computer passes if a human interrogator cannot distinguish its answers from those given by a human.

Within the feature in the New York Times, the Turing Test was re-labeled: If an Algorithm Wrote This, How Would You Even Know?

This was significant in that it went further than the binary pass/fail of the Turing Test and asked participants which of two articles on the same event best fit a list of descriptive adjectives. Unbeknownst to the participants, one version was written by AI software, the other by a professional journalist.

The results are striking, if not altogether shocking. The software scores well on descriptors like ‘informative’, ‘trustworthy’ and ‘objective’. It lags behind on ‘pleasant to read’, but is the runaway victor on ‘boring.’ This seems to confirm our preconceptions about what a computer can and cannot do.

Although just one study, it does hint at an underlying truth: the best creative content is emotive, and computers are not good at being emotive. Without actually having emotions, it is nigh-on impossible for machines to tap into this deep, but impervious, reservoir of inspiration.

That is not to say that artificial intelligence has hit a brick wall when it comes to content creation. News outlets can depend on AI to write accurate stories on events, but AI has also developed the ability to produce creative work through the usage of neural networks.

Neural networks and creative content generation

The move to widespread usage of expensive but effective deep learning neural networks has started to erode the requirement for a priori human knowledge. A machine can learn on its own, like a human can. However, what it learns is not restricted by the limits of our perceptual apparatus.

A simplified diagram like the below serves to illustrate how many deep learning neural networks function, including Google’s RankBrain algorithms that shape the search results we see.

The number of neurons in the hidden layers can be increased significantly (as can the number of layers) to create a system that can produce any desired outcome.

This approach was used by Aiva, an AI music composer. The input layer for Aiva consisted of sheet music from classical music composers. Within the ‘hidden layers’, Aiva learned the underlying structures and patterns that occur within the music. After mastering the theory, the AI put it into practice – you can listen to Aiva’s work on Soundcloud here.

This is hugely impressive, but some notes of caution are needed. Aiva’s music is dependent on the stimulus material and derives its composition directly from it. Also, a human orchestra is required to play the music. Nonetheless, this is evidence of a significant stride towards creativity.

Similar experiments have been undertaken to see how well an artificial intelligence system can understand the structure of literary works.

Since so much content production is driven by text, this is an important area for us to understand. The study produced visualizations like the one below, which maps out Cinderella’s fortunes through the duration of the eponymous character’s story.

The AI system was fed almost 2,000 works as its input layer and the desired output was an analysis of any common structures it could find. It located the following six:

  1. Rags to Riches (rise)
  2. Riches to Rags (fall)
  3. Man in a Hole (fall then rise)
  4. Icarus (rise then fall)
  5. Cinderella (rise then fall then rise)
  6. Oedipus (fall then rise then fall)

This analysis relates to the emotional arc within each book, not just the narrative or plot-based arc.

Once more, it is impressive, but does not tell us whether the same AI could write a book of similar quality.

We don’t have definitive answers on this yet, but the signs point to a successful AI-authored book within the next decade. Just last year, a short novel written by an AI program made it past the first round of judging in the prestigious Hoshi Shinichi Literary Award in Japan. One of the judges, the science-fiction novelist Satoshi Hase, said: “it was a well-structured novel. But there are still some problems [to overcome] to win the prize, such as character descriptions.”

This chimes with what we have seen above. Well-structured, informative, accurate, but lacking in the artistic elan that marks out a human author’s work. Nonetheless, we should expect mechanical wordsmithery to continue improving as AI systems edge closer to sentience.

So we are nearing the point where AI-generated creative content is very possible. But is it accessible?

There are hints at an answer here from Google.

Google is training its algorithms to teach each other through a process known as Federated Learning. Federated Learning is Google’s latest attempt to speed up the data-sharing processes and increase the data quantities that are so central to the improvement of machine learning systems.

This is a significant development, as Google has a history of providing open access to similar programs.

Google has also just launched a new initiative with the aim of adding more humanity into its artificial intelligence systems. This is precisely the element that has been lacking so far, as so much of content marketing depends on the communication of ideas and emotions.

We are some distance from a marketing ecosystem where AI-generated content is attainable at a large scale, but recent progress should make us optimistic about the potential for this technology.

In summary: How close are we to ‘AI-generated content’?

Much of what occurs today in the arena of ‘AI-generated content’ could be defined more appropriately as content curation.

Without the initial content (created by a person), this process is stultified and becomes an inward spiral with diminishing returns.

It will be in the unification of intelligence across the sensory, the mental, the mechanical, and the interpersonal that AI will reach its full potential, as we wrote recently.

If AI systems attain the capacity to think creatively and independently, there is no reason why this skill would not be applied to content generation.

The most intriguing area of this discussion lies in the possibility for collaboration. The potential for ‘extended intelligence’ that maximizes the strengths of humans and machines does sound fanciful, but there are hints already that this could come to fruition.

For inspiration, we can look to the 1993 novel Just This Once, written by a Macintosh IIcx nicknamed ‘Hal’, in collaboration with its programmer, Steve French. Though much derided for its quality (or lack thereof) at the time, it now seems quite forward-thinking in its attempts to bring together the skills of a computer and a human author.

This is significant when we bear in mind that artificial intelligence is not confined by the limits of human intelligence. AI may mimic elements of how we process information or make decisions, but it is concerned primarily with solving problems. As a result, it can discover novel solutions to problems that we could never consider.

Although progress in AI-related fields seems assured, its pace and direction are undecided. So, too, is our place within this landscape. The lines between human and machine are constantly shifting, and that matters.

We need to know what this technology can do, what it can’t do, and where we should position ourselves to capitalize on upcoming possibilities for collaboration with AI systems. That applies to every aspect of our work, from the analytical to the creative.

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