Throughout history, storytelling has been an intrinsic part of human culture, deeply intertwined with the technologies of the time. Ancient storytelling practices saw narratives etched onto rocks, transcribed on papyrus, written in paper scrolls, and eventually printed in books.
The invention of the printing press marked a significant evolution, allowing stories to be replicated en masse and accessible to a larger audience. This democratization of information laid the groundwork for modern storytelling, where the advent of computers introduced new formats for narrative, initially requiring human engineers to write the underlying code and software.
Historically, humans played a critical role in determining the essence of a story, whether its objective was truth-seeking or driven by subjective interpretation. Until the emergence of sophisticated algorithms and artificial intelligence, no technology could autonomously decide what stories should be told. Printing presses and early computers relied on human judgment to curate content, ensuring that narratives aligned with societal values and truths.
Algorithms and AI present a pivotal shift in storytelling dynamics, designed specifically to take humans out of the storytelling loop, thereby altering both the creation and dissemination of narratives.
Modern algorithms now prioritize user engagement maximization as their primary objective, fundamentally changing the landscape of storytelling.
These sophisticated algorithms actively prioritize narratives that draw the most attention and retain users on digital platforms for extended periods. For corporations like Meta and Alphabet, the overarching goal is user capture—extending user time within their ecosystem—rather than a commitment to truth-seeking.
Under this framework, any story, including those lacking factual accuracy or integrity, can serve the purpose of maximizing engagement, leading to an environment where misleading or untruthful narratives can dominate the information landscape and compromise the reliability of content.
During Myanmar's tumultuous transition to democracy, Facebook emerged as the principal news source amid widespread political unrest. The demographic composition of Myanmar is predominantly Buddhist, constituting 90% of the population, in stark contrast to the minority Rohingya Muslims.
This religious and ethnic landscape fostered ethno-religious tensions, which were exacerbated by the use of Facebook as a platform for a hate campaign against the Rohingya community. The repercussions were devastating, leading to grave humanitarian atrocities including the killings of 7,000-25,000 civilians, the sexual violence against 18,000-60,000 men and women, and the mass expulsion of approximately 730,000 Rohingya Muslims.
A variety of messages circulated in Myanmar during this period, categorically two types:
Inflammatory and hateful rhetoric primarily propagated by Buddhist monk Wirathu, who incited violence against the Rohingya.
Gentle and accommodationist messages conveyed by more moderate monk figures like Sayadaw U Vithuddha, who sought peace and understanding.
Algorithms inherently favored and amplified the hate-filled messages, focusing exclusively on user engagement and extending attention spans. This phenomenon cultivated a new intersubjective reality that reshaped societal interactions and contributed to violent conflict and civil war.
The evolution of AI signifies the next monumental leap in computing software, enhancing its capability to engage with human language autonomously.
Initial algorithmic designs—termed 'baby algorithms'—are introduced, subsequently trained on massive datasets comprising trillions of data points.
Through sophisticated pattern recognition processes, computers can now analyze speech, imagery, text, and music, leading to the generation of increasingly complex patterns.
Initially, these patterns mirror human creativity in language, visuals, and music, representing a key aspect of Machine Learning (ML). As a result, algorithms evolve to create even more advanced algorithms, significantly expanding the breadth of generated content.
Deep Machine Learning (DML) represents a further advancement, wherein sets of algorithms produce increasingly complex sets of algorithms, continuously generating newer outputs and creative forms.
Outputs generated through DML are profoundly sophisticated, challenging human understanding and interpretation. The advancements in computing memory, algorithmic complexity, data volumes, and processing capabilities enable computers to identify patterns that elude human cognition.
This technological evolution creates opportunities for entirely new forms of storytelling, including languages, visual arts, and musical compositions that are fundamentally machine-generated, pushing the boundaries beyond human intellectual capacities, commonly referred to as 'wet intelligence'.
The paradox arises as humans develop technologies capable of manipulating language and crafting narratives independently, raising questions about authorship and the role of human creativity in the storytelling realm. The intermingling of vast datasets, advanced algorithms, and relentless processing power yields imaginative, awe-inspiring, yet potentially haunting outcomes.
The outputs produced through DML are characterized by such sophistication that they may fall beyond human comprehension. The disparity in computing memory, algorithmic design, data capacity, and processing speed far exceeds human cognitive abilities, allowing for the discernment of complex patterns inaccessible to human minds.
This paradigm shift opens avenues for the generation of new languages, innovative stories, unique visual narratives, and musical forms—creations that are thoroughly machine-based and capable of transcending traditional notions of intelligence.
Humans are actively engineering technologies that empower these systems to manipulate linguistic constructs and develop narratives without human intervention.
This amalgamation of data, algorithms, and expansive processing capacities may yield results that are not only remarkably innovative but could also venture into realms of unpredictability and confrontation with human agency.
We are at a crossroads in witnessing the emergence of a form of intelligence that may eclipse and surpass human intellect, often referred to as 'wet' intelligence; this poses existential question about the coexistence of organic and inorganic forms of intelligence. While humans experience emotions and make deductions, machines operate within the frameworks set by parameters and goals devoid of emotional intelligence.
If the fundamental goals of storytelling revolve around truth-seeking and societal ordering, DML could transform into a perilous instrument utilized by nefarious entities. This must be approached with caution, as the myths birthed from these systems might infiltrate public consciousness and dilute personal and collective agency, making societies susceptible to manipulation by AI-driven narrative creators.
The historical instances of manipulation exemplified through narratives include:
The persecution of witches underscored by societal fear and hysteria.
Racial hierarchy justified by institutions promoting slavery as a norm.
Colonialism framed as the 'white man’s burden' or Europe’s civilizing mission.
Gender biases portraying women as the 'weaker' sex.
Misrepresentation of homosexuality as a disease, perpetuating stigma.
Germany’s scapegoating of Jews, Socialists, and Communists for military undermining during WWI.
QAnon’s propagation of a fictitious global conspiracy orchestrated by high-ranking pedophiles infiltrating governmental structures.
The Birtherism conspiracy questioning the legitimacy of President Obama’s citizenship.
Narratives in the U.S. surrounding election fraud claims aiming to discredit electoral integrity and democratic processes.
The evolution of storytelling can be observed through two distinct chains:
Human-Computer connections as demonstrated through platforms like Facebook, Instagram, and TikTok.
Computer-Computer connections facilitated by Machine Learning (ML) and Deep Machine Learning (DML) algorithms.
In an evolutionary perspective, the ML and DML systems could evolve towards a form of intelligence that may eventually exceed human capacity, referred to as Alien Intelligence.
By drawing parallels to biological evolution where DNA and genetic codes have evolved over the course of nearly 4 billion years, the rapid evolution of intelligence resulting from human-computer to computer-computer interfaces could unfold within a few decades.
Hypothetically, should GPT-4 be viewed as an amoeba in this evolutionary chain, one wonders what form of intelligence—akin to a T. rex—might emerge in the future.
The preordained objectives harnessed by data and software companies often prioritize profit over public welfare, leading to ethical dilemmas in storytelling.
These corporations frequently lobby governmental entities to safeguard themselves from liabilities concerning their pervasive role in narrative dissemination, evading accountability.
The argument that the “client is always right” assumes a level of understanding of digital technologies among the public, which is notably lacking; studies suggest that only about 2% of individuals are fully aware of the complexities involved—this includes significant figures within government positions.
Consequently, a mere fraction of engineers and managerial elites within these organizations possess a comprehensive grasp of the digital landscape, with profit motives typically overshadowing communal interests.
"War is the continuation of politics by other means."