The potential impact of AI and its practical implications for many aspects of our daily lives have been widely communicated to us in the past. However, most of us didn’t have the opportunity to gain firsthand experience of how disruptive and powerful this technology could be. So, when credible people like Stephen Hawking and Elon Musk warned us about the potential dangers of AI, it often felt like a distant concern, and many of us didn’t take it seriously (Kharpal, 2017). This sentiment, however, changed rapidly when ChatGPT made its first introduction into our lives. For most, ChatGPT’s responsiveness and general capabilities posed an unprecedented experience with a machine, leaving many of us with an existential fear of being replaced in the future (Brower, 2023) . To rationally assess such concerns, it’s essential to delve into the intrinsic properties of the algorithms underpinning AI applications like ChatGPT.
A brief explanation of its algorithms
In its formative years, AI applications primarily relied on supervised and unsupervised learning algorithms. These algorithms were inherently reliant on structured data to process information and solve data-intensive problems (Kar, 2016). In contrast, ChatGPT harnesses the use of deep- and reinforcement learning to mirror human intelligence. Consequently, GPT-4 can process and, in turn, produce unstructured data such as text, images, audio, and video (Dwivedi et al., 2023). Due to its intricate nature, however, ChatGPT functions as a black box, i.e., it remains unclear how it arrives at its output based on the input given to its algorithms. What is evident, however, is that its reinforcement protocols continuously improve through a constant influx of new training data. So, in fact, ChatGPT mostly produces recycled original content presented in a coherent and logical manner (Chandra et al., 2022).
The issue at hand
So, what precisely is the downside of this? The issue at hand revolves around the surge of new and highly capable AI applications, which has fostered a sentiment that humans are replaceable. A great example of this trend can be found in the art world. Since the advent of GPT-4 and Dall-E, many artists have become discouraged by the depreciating effect of these technologies on the value of artwork (Ciftci, 2023). The irony in this lies in the fact that these applications inherently depend on human-generated original input. This begs the question of how these applications will evolve as humans generate less original content over time.
So, are we spearheaded towards a future burdened by a lack of original content, or will the situation stabilize itself as people come to realize that these sophisticated algorithms are essentially advanced recycling systems? Regardless of the outcome, I firmly believe in advocating for a future that stimulates an environment where individuals are incentivised to produce original content. Let’s not compromise the creative capabilities of our future generation by displacing it with unoriginal, mass-information-inducing algorithms.
Bibliography
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Ciftci, B. (2023, March 12). Why some artists see AI-generated art as a threat to their livelihood. Retrieved from Medium: https://bootcamp.uxdesign.cc/why-some-artists-see-ai-generated-art-as-a-threat-to-their-livelihood-f4634b24a5ce
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Kharpal, A. (2017, November 6). Stephen Hawking says A.I. could be ‘worst event in the history of our civilization’. Retrieved from CNBC: https://www.cnbc.com/2017/11/06/stephen-hawking-ai-could-be-worst-event-in-civilization.html#
