Other derivative artificial intelligence (AI) programs, such as ChatGPT and DALL-E, are often seen as one of the ways to replace workers, but their real strengths are separate. It is to improve the work done by humans.
Fast and good are often contradictory. In generative AI, this conflict is overcome by increasing the worker’s ability or speeding up the work. Clearly, these tools can prevent headwinds when they are there as support rather than substitutes.
In general, when measuring workload, it is based on the productivity that can be completed in a given time. However, this method does not consider the quality of work. Because the faster you work, the lower the quality. It is possible to get the job done quickly if you fully master certain skills. However, to make up for small flaws or mistakes, even completed work should be reviewed one more time.
Quality is an interesting topic in itself. I read a book called ‘Zen and the Art of Motorcycle Maintenance’. According to the book, quality can be flexible and change depending on how the viewer perceives it. For example, what is considered high quality in a harsh factory may be of unacceptable quality in a Bentley factory.
But what if we built up Bentley-level quality while matching much higher volumes?
According to the article Top 10 Best ChatGPT Articles, the author used an AI-generated conversation as an example of his original work. The results weren’t bad, but they weren’t very appealing either. In my opinion, the article written by a novice writer was better than that article. Still, when I asked the AI how it handled the story concept or how it fixed the code and identified errors, it was pretty good.
Who among writers hasn’t had a hard time dealing with finicky editors who ask for permission every time an idea is fleshed out or edited? Considering this, it can be seen that the real strength of generative AI, at least so far, lies in improving the quality of work or refining finished writing.
Another area where AI technologies can shine is in finishing what creators started or merging eclectic and haphazard planning materials to produce completely different results. The other day, The New York Times disclosed the production process of the movie ‘Tron’, which Alejandreo Jodorowski would have directed with generative AI. The AI used here was able to learn how to write Jodorowsky’s films and how to create storyboards for films.
Growing up, I was fascinated by the works of dead authors, such as John Carter’s Mars, Conan the Barbarian, and Doc Savage. A generative AI can learn from these initial tasks and create an infinite number of sequels that remain consistent with the original.
At the same time, from a more practical perspective, AI can be used to deal with old software code that no one wants to maintain or update. Generative AI will be able to bridge the gap between past and present by learning code and methodologies.
To summarize the current situation, generative AI is not without its potential to replace human resources, but it is better suited for tasks that augment creative work. Although AI can imitate writers, it cannot create original and outstanding works or come up with unique ideas. AI can run with initial creations, but it struggles with initial creations. Because AI is inherently derivative.
But as a way to augment work already being done by humans, helping to refine an idea or project, generative AI will have a much higher success rate.
Source: ITWorld Korea by www.itworld.co.kr.
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