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Art vs Science: can creativity be automated?

17 Oct 15:00 by Source: https://www.thinkbiganalytics.com/2017/08/11/art-vs-science-can-creativity-automated/

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Originally featured on Information Age.

Can true creativity be achieved by a machine? Or is this capability reserved for humans? At current rates, probably not.

In an increasingly data-driven world, we see the automation of jobs across nearly every industry. For some, this is a daunting prospect, but evidence shows that rather than stealing our jobs, technology is creating new opportunities: smart humans are working alongside smart machines in a collaborative way.

But what about creativity? Can machines really replace human imagination, and where do the likes of data science, machine learning and artificial intelligence fit in with the world of art and creativity?

These are important questions that face us as we adapt company cultures in a digitalized world. In order to do so, let’s take a look at some examples of how creative industries are leveraging data and analytics to drive business outcomes:

Industry #1: Music

‘Flow Machines’ software, a subsidiary of Sony CSL, produced the first pop song to be created using artificial intelligence (AI). Aiming to release its first entirely AI album this year, the Sony technology analyses a database of lead sheets from different genres and writes its only melodies as a result.

It only takes one listen of the AI-produced track “Daddy’s Car”, to hear hints of machine learning in the background, and arguably there is still a significant way to go in terms of producing ‘human’ music.

However, machines learn quickly, and development is ongoing. In this case, the augmentative element is clear: a human composer is required to officially turn the melodies into a full song.

Industry #2: Television

Imagine the potential of being able to predict the ‘perfect’ television series. Netflix did just that. Their recent series are key success stories for data and analytics. For the popular series ‘House of Cards’ and ‘Orange is the New Black’, Netflix used data and analytics to find the right combination of elements in order for both to be a success.

For example, the right choice of actors, the ideal director and the right combination of genre elements provided a win for a $100m data and analytics based investment from Netflix.

This highlights how machine learning can take the extra step that humans cannot: by using data science elements, Netflix could identify over 76,000 genre types to describe user tastes: a process that would have been extremely lengthy if completed only by humans.

Industry #3: Marketing

Traditionally a creative function, marketing is about telling stories. increasingly, marketers are leveraging data to optimize outcomes and understand, as well as engage with customers more effectively.

With the digital footprints that customers leave behind when browsing and interacting online, organizations can leverage this data and analyse it to learn more deeply about the behaviours and intentions of that customer.

HSBC demonstrated the use of behavioural science to help customers reach their financial goals by developing a ‘nudge’ app using automated messaging to save customers £800,000 between Christmas and New Year alone.

Ultimately, data makes us better at decision making, and helps us to make sense of a more complex world of interactions and transactions. It’s essential that marketers do this in order to maintain a competitive edge: but human creativity is not obsolete – it sits alongside automation, rather than being replaced by it.

The changing face of customer data

Data is increasingly complex, capturing not just transactions, but also interactions and observations from customers. As analysts begin to delve into the data, they no longer extract only 10s of variables from data, but potentially 10s of 1000s of variables to try and understand customer behaviour at any given time. This data ultimately enables marketers to instigate meaningful interactions.

To create meaningful moments, it is essential that marketers follow the Google strategy of ‘be there, be useful, be quick’. To demonstrate how businesses are adopting analytics in conjunction with automated real-time decisioning we can look to ad agencies who are implementing billboards that can achieve insight from image analytics of video footage to be able to deliver customized advertisements depending on the make and model of car driving past.

Having the right content in real-time is not enough, location is also vital in understanding consumer decision-making journeys for the marketing teams of tomorrow.

Studies show that geo-targeted mobile offers, based on customer environment and proximity to retailers, can provide 2x better conversation rates. If delivered to a commuter on a busy route, the conversion rates of marketing ads are even better.

Organizations require a deep level of insight, and need analysis of data to be delivered in real-time to create meaningful moments that enrich a customer journey. To achieve this, businesses are realising they must adopt automation. If not, marketers will fail to make the huge number of decisions necessary in today’s marketing industry.

Humans or machines?

By adopting the right blend of art and science, creative industries can get to know their customers better and maintain a competitive edge: this is done through optimization of human creative skill sets, in combination with use of data and analytics to drive business outcomes.

Ultimately, the face of marketing in a connected and digital world is transforming and will continue to do so as machines advance. As demonstrated by the examples explored above, the creative industry is using machine learning and analytics to develop new products and services. Humans will still create the narrative and continue to drive innovation, but data science will support, providing intelligent automation at scale.