“Computers are useless, they only give you answers.” — Pablo Picasso.
In marketing communications, it is well known that creativity can multiply the effectiveness and efficiency of campaigns. In fact, in advertising, award-winning creativity used to be a 12x efficiency multiplier until recently, according to the IPA (it has dropped, Peter Field says, because of brands’ focus on short-termism).
In any case, it’s fair to say that creativity is the one thing that you can use when you have the chance to be in front of your audience, so that they will pay attention to what your brand has to say.
It’s easier said than done, though, because good creativity, the kind that stops people from scrolling for a bit to actually see and hear your message, is hard work. And, unless your advertising contains a big idea, it will pass like a ship in the night, as David Ogilvy used to say. This is truer than ever, of course, with the avalanche of things competing for our attention on all of our screens.
Add to that the need to be more agile, to try to keep up with the speed of meme culture, and the result is that having a great idea (and actually bringing it to life) is basically a black swan event. What can be done to increment the probability of getting big ideas?
Deep Learning and the warping of time
By now you’ve surely seen in your social media feeds dozens, if not hundreds, of images generated with software like DALLE-2, MidJourney, or Stable Diffusion, like this one:
These tools are the most recent and visible examples of the advances in Deep Learning, a technique in the field of artificial intelligence that has been delivering surprise after surprise during the past ten years.
But Deep Learning is behind many things that we use on a daily basis, such as when we issue voice commands to our devices, or when Netflix or Amazon recommend what to see or buy next. It’s used extensively in financial applications, scientific research, self-driving cars, and within popular applications such as Photoshop, to automagically remove the background with one click, for example.
It’s also what powered AlphaGo, which six years ago took the world of Go completely by surprise, defeating Korean Go Master Lee Sedol, one of the greatest players of all time — and all time, by the way, is a very long time in Go, because it’s been played for around four thousand years.
When thinking about the power of Deep Learning, time is precisely the key thing to think about. In the case of AlphaGo, it was trained by playing millions of games against itself for a few days; in contrast, a professional Go player plays maybe 100.000 games during their whole life. It’s like these algorithms can live hundreds of lives while we are deciding what to have for breakfast.
The best way to have a good idea
The best way to have a good idea, as Linus Pauling said, is to have lots of ideas. Yes, sometimes you may have a good idea almost immediately, but that’s typically not the case. Not for you, and not for people whose job is to come up with ideas on a daily basis.
The reason why Pauling’s dictum resonates with scientists and creatives alike is that they know they have to let their minds wander, probe, make unusual connections — but not in search of the idea or the solution, but of as many possibilities as they can think of and dump on paper or screen, so that later, from the many, a few that are actually good can emerge.
And this leads us back to our question: What to do to have better chances of having a great idea? The answer is, of course, to have as many ideas as we can. But we often don’t have the luxury of time to keep churning out ideas. We have deadlines to meet and lives to live. Fortunately, as we have seen, there’s something that can help us with that, because it lives in a different timescale than we do: Deep Learning!
While we are jotting down a few words or staring at the ceiling, tools like Seenapse can present us with a plethora of ideas for us to consider and elaborate on. But, you may wonder, are these things truly creative, or are they just scraping things off the web and serving them to me as if they just occurred to them? Well, they are creative. These tools are not copying and pasting, they are generating the ideas on the spot, specifically for the brief you give them.
And there are communications professionals around the world, like we at Good Rebels, and other agencies that are using them, in their own words, to help them “try new things, look for new signals in the noise and develop media strategies from less obvious starting points.”
The future is already here
It’s just not very evenly distributed, as William Gibson said. Machine Learning has been widely used to optimise media budgets for years now, for example. But Augmented Creativity is being used, professionally, by only a few agencies and a few brands around the world. This is about to change, and the consequences for marketing and advertising will be profound. Nobody needs to settle for the best they came up with on their own with a tight deadline anymore; you have a powerful ally in your screen now.
At this point you may wonder: If machines can have ideas, do we need creative people to make our big ideas anymore? The answer is yes, of course we do.
You see, the great advantage these tools bring is the ability to explore many divergent avenues of thought very quickly, to multiply the number of ideas we as humans can come up with in a short amount of time, and because of that, greatly improve our chances of having a great idea. The type of idea that will stop people from scrolling to pay attention to your brand.
But what these machines don’t have is the judgement, the gut feeling that we humans have, to be able to spot the idea, among all the others, that has the biggest chance to stand out in the real world, to solve the communication problem, to connect with its intended audience, and to be on brand. To find the proverbial needle in the haystack.
And this is, by the way, what it means to be a creative person in the age of creative machines: To exercise our judgement and ask the interesting questions. Because computers, as Picasso noted, can only give us answers.