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This is the AI we didn’t see coming: artificial imagination

Natalie Nixon says there are four ways to think about generative AI as a co-creator.

This is the AI we didn’t see coming: artificial imagination
[Source photo: Brennan Burling/Unsplash; Andriy Onufriyenko/Getty Images]

I first heard the term “artificial imagination” in 2017. I was reading a Price Waterhouse Coopers magazine interview with Shelly Palmer, a musician, technologist, and consultant. In the interview, he described computers’ ability to generate jazz music improvisation and this was a form of “artificial imagination.” Huh? I was just getting acquainted with artificial intelligence. I was stunned and a bit aghast. My initial reaction was . . . fear.

I’ve since level set my trepidation, but I’ve increasingly pondered the following question since Chat GPT came on the scene:

What does it mean to be creative in the midst of ubiquitous technology?

Big data has been tracking us for a long time. While there’s been a lot of hullabaloo around Open AI’s—and now Microsoft Bing’s—Chat GPT, AI is not new. Before generative AI became part of popular parlance, the internet of things made the synchronization of your banking account, grocery shopping, and eating habits possible for years. If you haven’t been mindful about your microphone access settings, then your iPhone has been tracking not only your sleeping patterns but also other nightly activities (I know, a bit creepy, but it’s a built-in setting). When traveling from state to state, if you use some version of E-Z Pass, yes, traveling is more seamless, and you’re being tracked.

Fast-forward to the proliferation of generative AI. When your texting device suggests words to help you finish your sentence—this is AI. Or when you’ve played around with audio transcription devices like Voice Memo on iPhones or the Otter app—this is also AI. And that’s not even to mention other applications such as Midjourney (image generation from language) or MusicLM by Google (music generation from text).

Artificial intelligence is a field that combines “computer science and robust datasets to enable problem solving.” And the first wave of automation and AI mainly affected blue collar workers. There was widespread anxiety—and rightly so—about the elimination of factory jobs as robots replaced people on assembly lines. But this latest iteration of AI affects white collar knowledge workers, and those of us who exchange value based on the currency of our ideas.

There are three responses we could have in response to my question, “What does it mean to be creative in the midst of ubiquitous tech?” One is a dystopian view to “Run for the hills the robots are taking over!” The second is a utopian view: “All’s well in the world. Didn’t life just get so much easier with all of the automation and bots that abound?”

The third response is more of the one that I adopt. Galit Ariel, an augmented reality expert, refers to this perspective as a heterotopia: there’s good news and there’s bad news. The bad news first. Many jobs will become irrelevant and thus require reskilling and upskilling to prepare for the new jobs that are created adjacent to artificial intelligence. The great news is that as basic tasks get done by AI and bots, the opportunity is to redesign work and different metrics for productivity that will make room for what makes us uniquely human. Think of what a workplace looks like where technology is not only doing mundane tasks but it is also prompting us to engage in our emotions, our intuition, our creativity.

Ultimately, this is about mindset shifts.

In this moment, we can reimagine Carol Dweck’s growth mindset work. Or as David Bentley, the CEO of Porter Novelli, told me “It’s the prompt to help us jump across a chasm of fear.”

If we adopt a fear- and scarcity-based mindset, then AI is a doomsday bell ringing loudly in our ears. But if we try on a growth mindset, then AI offers tools and opportunities about which we can be positive, open-minded, and excited. AI offers opportunities for efficiency and new job creation.

There are of course challenges that we must keep top of mind as AI creeps into creative territory. For example:

  • How do we leverage existing people’s work while giving the people who originated the idea their due credit?
  • How do we ensure ethics and accountability?
  • Is our imagination under threat with this injection of generative artificial intelligence?

With regard to the last question, I think not. And here’s why.

While functions such as Chat GPT seem on the surface to take away the work of humans, not so fast. Generative AI is neither a dystopian quagmire nor a utopian panacea. Brilliant and rambunctious imagination is required now more than ever. The algorithms still need humans who know how to frame better questions. And the better you are at asking questions (a catalyst for building your creative capacity), the more effective is ChatGPT.

Here are four ways to think about generative AI as a cocreator:

  1. AI as a collaborator to spark our imagination. Whatever apps like ChatGPT spit out are only the starting place.
  2. AI as a catalyst to help us learn to ask better questions. With Chat GPT, questions are the inputs, so you still have to be excellent at question framing.
  3. Integrity remains supreme. You must be smart and diligent about doing your background research. For example, Chat GPT’s training data is only through 2021. So definitely do not try to write your next book only using Chat GPT.
  4. AI ups your game for critical thinking. See all three points above.

Big data and mechanisms such as Chat GPT can reveal patterns, but they don’t necessarily discern the meaning of the patterns. That is in part because of the way Chat GPT functions. It is scrubbing the internet that is only representative of the people who use it. Therefore, the World Wide Web is not full of inputs from every single person from every tiny village and corner of the world. Thus its inputs are limited. As Seth Godin wrote in his January 6, 2023 blog,  “GPT and other AI tools don’t actually know anything. They’re pattern matchers and pattern extenders. And those patterns are called culture.”

We need not only big data (the bird’s eye view), but also what I would call deep data (the worm’s eye view). The data that comes from deep observations, interpersonal interactions, and story.

So, take heart. The “I” in AI stands for intelligence, and for imagination, but you’re still in charge of driving both.

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Natalie Nixon, PhD, is a  creativity strategist, global keynote speaker, the author of the award-winning  The Creativity Leap: Unleash Curiosity, Improvisation, and Intuition at Work,  and the  president of  Figure 8 Thinking. More

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