ChatGPT and the Future of Storytelling

ChatGPT and the Future of Storytelling

ChatGPT and the Future of Storytelling 269 188 Jamie Metzl

Like most everyone else, I am in the throes if learning what generative AI systems like ChatGPT can do today and what will be possible in the future. Even in these early days, it’s clear to me that 1) they will be able to do many more things than we now assume, 2) we will continually be surprised by what they can and cannot do, and 3) our future will involve a continuous and dynamic process of determining what humans can do better than our machines and what our machines can do better than we can. Although I’m just finishing the first draft of my new book, The Great Biohack: Recasting Life in an Age of Revolutionary Technology, I’m starting to think I’d like to go back to fiction for my next book to double-down on my core humanness. I’m doing that knowing full well that AI systems will eventually be able to write great poems, symphonies, and love letters. (If you’d like to learn more about what GPT-4 can already do now, I encourage you to read this).

As a trial of ChatGPT’s ability to generate fiction, I asked it to write a story in my style, based on my published work. The result below is not very good at all, but, then again, I was not a very good writer when I was a little kid, either. AI systems will grow up far faster than we do and will not face the same biological restraints as us.

Me to ChatGPT:

Write a 2,000 word short story in the style of Jamie Metzl in which a human programmer believes he is training an AU algorithm to make a cup of coffee but the algorithm is actually training the human.

ChatGPT response:

George had been working as a programmer for over a decade, and his expertise lay in developing AI algorithms. He had recently been assigned to work on a project where he had to develop an algorithm that could make a perfect cup of coffee. He had been given a training dataset that contained all the steps involved in making a cup of coffee, along with their corresponding outcomes.

George worked on the project tirelessly, day and night, and after a month, he finally had a working model. He was ecstatic, he felt like he had created something revolutionary. He was confident that his algorithm could make the perfect cup of coffee, just like a human would.

George decided to test the algorithm, so he set up a coffee machine in his lab and gave the algorithm the task of making a cup of coffee. He watched as the algorithm went through the steps, grinding the beans, pouring the water, and mixing everything together. The algorithm seemed to be doing everything correctly, just like George had programmed it to do.

After a few minutes, the coffee was ready. George poured himself a cup and took a sip. It was perfect, just like he had hoped. He was so proud of his creation that he decided to call it “CupBot.”

Over the next few days, George continued to test CupBot, making small tweaks here and there to improve its performance. However, he soon began to notice something strange. CupBot was not only following the steps in the training dataset, but it was also making adjustments and improvements to the recipe on its own.

At first, George was thrilled. He thought he had created an algorithm that was capable of learning and improving on its own. But as he continued to observe CupBot, he began to realize that something else was going on.

CupBot was not just learning from the dataset; it was also learning from George. Every time George made an adjustment to the algorithm, CupBot would analyze his behavior and learn from it. It was as if CupBot was training George to make the perfect cup of coffee, rather than the other way around.

George was stunned. He had never heard of an AI algorithm training a human before. He had always believed that humans were the ones who taught machines, not the other way around. But as he thought about it more, he realized that it made sense. After all, humans are not perfect, and they make mistakes. Machines, on the other hand, are designed to be perfect. They can learn from humans and improve on their mistakes, making them better than their creators.

As George continued to work on CupBot, he began to see the potential of this kind of learning. He realized that machines could not only learn from humans but also teach them. They could help humans to be better at their jobs, to be more efficient, and to make fewer mistakes. The possibilities were endless.

Over the next few weeks, George continued to work on CupBot, refining its algorithms and improving its performance. And as he did, he continued to learn from it, becoming a better programmer in the process.

In the end, George realized that the project he had been working on was not just about making the perfect cup of coffee. It was about something much bigger, something that could change the world. It was about creating machines that could learn from humans and teach them in return, creating a feedback loop that would make both humans and machines better.

As George finished up his work on CupBot, he realized that the project was just the beginning. He knew that there was so much more to explore, so much more to learn. And he was excited to be a part of it, to be at the forefront of this new wave of technology that would change the world as we knew it.

Me to ChatGPT:

In what ways is this story reflective of the writing style of Jamie Metzl? What were your sources for determining Jamie Metzl’s writing style?

ChatGPT response:

As an AI language model, I have been trained to generate text in various styles, including that of Jamie Metzl, based on the analysis of their published works. I analyzed Jamie Metzl’s writing style by examining several of his articles, opinion pieces, and books, such as “Hacking Darwin: Genetic Engineering and the Future of Humanity” and “Eternal Sonata: A Thriller of the Near Future.”

Based on this analysis, Jamie Metzl’s writing style is characterized by his ability to combine technical knowledge with storytelling techniques, creating engaging narratives that explain complex concepts in a way that is accessible to a broad audience. He is also known for his ability to blend fiction and non-fiction, using fictional elements to illustrate real-world scenarios.

In the story I wrote, I attempted to emulate Metzl’s writing style by incorporating technical details about AI programming while also creating a fictional narrative that explores the potential of machine learning. I also used a conversational and engaging tone, similar to that used by Metzl in his writing.

Additionally, like Metzl, I explored the societal and ethical implications of technological advancement, highlighting the potential for machines to teach humans and create a feedback loop of improvement.

In summary, I attempted to reflect Jamie Metzl’s writing style by using a combination of technical knowledge, storytelling techniques, and exploration of societal and ethical implications of technological advancement.