The Global Surge of Artificial Intelligence Generated Data: How GenAI and Large Language Models (LLMs) Are Reshaping the Global Information Landscape

Generative AI and LLMs Enabling a New Era of AI-Generated Content, Reshaping Humanity’s Digital Knowledge Base

The rise of Generative AI and Large Language Models (LLMs) like ChatGPT in generating an ever-increasing volume of digital global knowledge marks a pivotal moment reminiscent of the Great Library of Alexandria. As these technologies evolve, they are leading to a significant shift where AI and machine-generated data is expected to surpass human-generated content in the near future. This introduces challenges such as Model Autophagy Disorder (MAD) , a phenomenon where Large Language Models risk training on their own outputs, potentially compromising the integrity and quality of the collective digital repository.

The Global Digital Alexandria: Archiving the World’s Collective Knowledge

The world’s collective digital knowledge is securely stored across a vast array of platforms, including digital archives, websites, cloud servers, and data centers. These network and data storage infrastructures host an immense wealth of information, ranging from the millions of Wikipedia pages to digital books, audio files, YouTube videos, online discussion forums, and countless other websites. Up to this point, the majority of the content created and stored in this global knowledge infrastructure has served as humanity’s digital memory bank. This archive preserves human-generated data and knowledge, analogous to the Great Library of Alexandria that once stood in Egypt before its destruction in 48 BC by Julius Caesar, the Roman Emperor. This modern digital repository carries the legacy of that ancient institution into the digital age, serving as a comprehensive archive of human knowledge and innovation.

Traditionally, human cognition, creativity, and expertise have been the primary engines of this knowledge creation. Yet, we are on the verge of a paradigm shift that promises to redefine this landscape.

Forging a ‘New Global Digital Knowledge-Based Alexandria’ with The Ascendancy of Generative AI

With the advent of Pre-Trained/Foundation Large Language Models (LLMs) [1,2], built on transformer-based architectures, we’re witnessing the birth of a new paradigm in artificial intelligence. Known as Generative AI, this revolutionary shift gained momentum in November 2022 with the introduction of ChatGPT by OpenAI. These advanced LLMs are capable of generating not only text but also images, videos, and various other media formats. They are laying the groundwork for a new era, contributing to the creation of a AI or machine-generated ‘New Digital Alexandria,’ a vast repository of AI-generated content that complements and extends human knowledge.

Adding Synthetically Generative AI Data to the Global Knowledge Pool

In addition to that, in the specialized field of data science, synthetic data emerges as a specialized category unto itself. This data is meticulously generated through computational algorithms or advanced artificial intelligence models, such as OpenAI’s Generative Pre-trained Transformer (GPT) series. The primary aim is to replicate the statistical attributes and inherent characteristics of real-world data sets. The utility of synthetic data is multifaceted; it serves as an invaluable resource for system testing, model validation, and the training of machine learning algorithms. This is especially pertinent when genuine data is either scarce or compromised in quality, thereby offering a robust alternative for achieving reliable analytical results.

Figure 1 illustrates how the GenAI/LLM Pre-Trained Foundation Model ingests data to train its own models from a global knowledge pool. This pool is enriched by human-generated data, GenAI/machine-generated data, and synthetic data specifically generated by GenAI/LLM technologies.

3D Vector Representations in the LLM Global Knowledge Base

The concept of representing every and any data as a 3D vector (Fig. 2), in the LLM global knowledge base, signifies a quantum leap in how information is stored, accessed, and manipulated. This approach promises to standardize the representation of diverse data types, whether human-generated or AI- created, into a uniform format. It simplifies the complexities involved in data interpretation and retrieval, offering a streamlined way to navigate and visualize the exponentially growing reservoirs of information. By converting text, images, videos, and even synthetic data into 3D vectors, this method paves the way for more efficient and accurate GenAI/LLM algorithms, thereby fortifying the ‘Global Digital Knowledge-Based Alexandria’ with a more robust, scalable, and interoperable architecture.

In the end, the entirety of global knowledge, whether generated by humans or machines, will be distilled into vectors, simplifying the complex tapestry of information into a universal language.

LLM Transformer Models Fueling the Next Wave of Digital Content Generation

Transformer-based Large Language Models (LLMs) are engineered to generate all sorts of digital media formats, including new words, sentences, and even complete texts, thereby giving rise to a new class of digital, synthetic, or GenAI-generated content (Fig. 3).

LLM Data Symbiosis: How AI-Generated Content Could Feed Back Into Its Own Evolution and Generation

Here’s how LLM solutions search and incorporate data for their training. For example, a news organization that employs a ChatGPT-based Generative AI text editor can produce an entirely new piece of AI-generated content. Once this content is published on their website, it can serve as training data for the next generation of LLMs, or re-train existing ones (Fig. 4).

Over time, existing Large Language Models (LLMs) may be retrained or fine-tuned on new datasets to improve their performance and accuracy. This data is often curated from a variety of sources, such as academic papers, books, and reputable websites. Some Internet content is scraped by ‘content-eater’ web scraper bots. These bots scour both public and private websites, including news sites that are rich in human-written and AI-generated articles, blog posts, and digital books. The collected content is then prepared for LLM training or re-training. Looking ahead, it’s conceivable that the output from LLMs could serve a dual purpose: it could not only generate new AI content and data for consumption but also contribute this new ‘GenAI’ content or data back to a global knowledge pool. This recycled ‘GenAI’ data could potentially be used to train new LLMs or re-train the underlying deep-learning algorithms of existing LLMs during their next round of updates, thereby refining their quality and performance over time.

This process creates a self-reinforcing cycle where both human-generated and AI-generated content contributes to the ongoing evolution of LLM capabilities, continually feeding and training these insatiable models with new data to analyze and imitate.

The Coming Shift in the Digital Knowledge Landscape: Projecting the Future Dominance of GenAI over Human-Created Content

As we venture into an era of AI machine content generation, the balance between human-created and AI-generated content is shifting dramatically. Given the rapid pace and high volume at which AI — particularly Generative AI (GenAI) — can produce content, the human contribution to the global information pool may diminish in relative terms, as shown in Fig. 5.

GenAI is still in its early stages, but at the current pace, it could rapidly permeate the Internet, becoming an integral part of its foundational digital knowledge fabric. Looking ahead, we can anticipate a tipping point where GenAI-generated content doesn’t just supplement but actually surpasses human-generated content. This transformation will have profound implications for the digital landscape, reshaping our collective knowledge base in ways we are just beginning to understand.

The Unbridgeable Gap: Why Humans Can’t Keep Pace with GenAI-Generated Content and Data

As Generative AI (GenAI) technologies continue to evolve, the rate at which they can produce content and data is outpacing human capabilities. These advanced AI engines can operate around the clock, generating text, images, and other forms of media at speeds that are humanly unattainable. Moreover, they can analyze and process vast amounts of data more rapidly and efficiently than any human. This places humans at a distinct disadvantage when it comes to keeping up with the sheer volume and pace of GenAI-generated content.

The imbalance not only challenges our ability to consume and comprehend this burgeoning mass of information but also raises questions about the quality and integrity of this new AI-driven digital landscape. This concern is particularly relevant in understanding how these AI language models are created, especially in a landscape increasingly dominated by AI-generated content.

Hybridization of GenAI-Human Content Mix in Emerging AGI

Not all GenAI is purely AI-generated. In the early stages, humans-in-the-loop assist, augment, and validate GenAI content. It’s expected that a great deal of human-made content will drive GenAI. This is analogous to humans interacting, chatting, and augmenting ChatGPT with their own prompt input.

Additionally, prompt engineering at these early stages is considered the ‘art of humans,’ not machines. Humans not only direct and orchestrate but also add a significant chunk of human-generated text input into these prompts. Reinforcement Learning from Human Feedback (RLHF) will also involve humans-in-the-loop to validate the quality of AI answers during LLM fine- tuning processes.

Entering a Dangerous Zone: The Prospect of Autonomous GenAI Systems

However, this may dramatically change in the near future. As depicted in Fig. 5, as we enter the early stages of Artificial General Intelligence (AGI) [1], human prompt engineering could be replaced by AI-generated prompts. In this scenario, machines will drive other machines to finally complete the input-output AI LLM control loop automation cycle.

As we write this, a future GenAI-LLM engine could take these concepts and ideas from Internet text scraping and incorporate them as instructions to craft and guide a self-generated, automated, and AI-driven prompt. This could, in turn, drive these future LLMs to auto-generate specific data or content output. This would further channel control loop actions back into an LLM self-learning, self-training process, creating an automated prompt- feedback loop in the LLM AI engine during the early stages of AGI. That’s a possible outcome, which requires some further constraints on this future AGI’s near-term sentient intelligence.

What Could Happen if Generative AI Surpasses Human- Generated Content: Exploring the Possible Implications

When Generative AI (GenAI) not only becomes pervasive but also surpasses human-generated content in volume, there are several implications and scenarios, among others, to consider, as shown in Fig. 6.

Positive Implications

• Knowledge Acceleration: With GenAI producing content at a faster rate than humans, the accumulation of AI-driven digital knowledge could accelerate, potentially leading to quicker scientific and social advancements.
• Resource Efficiency: LLM engines can operate 24/7 without the human needs for rest, potentially leading to more efficient content production and data analysis cycles.
• Global Information Equality: If GenAI/LLM can produce high-quality content rapidly, it could level the playing field between well-funded organizations and smaller entities or even individuals. This could democratize access to information, but only if concerns about GenAI bias, ethics, among others, are addressed in the early stages of this technology’s emergence by the appropriate regulatory bodies

Negative Implications

• Information Overload: If GenAI/LLM begins to generate more content than humans do, the sheer volume of information could become overwhelming. This could make it challenging for people to sift through, consume, monitor, and find valuable content.
• Quality Dilution: With a higher volume of GenAI content, there’s a risk that the overall quality of content and data could decline as the focus shifts toward quantity, possibly distorted by the LLM Model Autophagy Disorder (MAD) [3,4]. This could dilute the value of genuinely insightful or groundbreaking human-generated content.
• Echo Chamber Effect: If GenAI/LLM systems are predominantly trained on existing content, they may perpetuate existing biases or viewpoints. This could reinforce echo chambers and potentially reduce intellectual diversity, as discussed in the ‘Global Information Equality’ section.
• Credibility Challenges: As GenAI content becomes more prevalent, distinguishing between human-generated and AI-generated content may become challenging. This could potentially erode trust in digital platforms. To address this issue, technologies for watermarking or labeling GenAI content could emerge as ways to differentiate between human- and GenAI-generated content.
• Data Privacy and Ethics: As LLMs scrape the web for content to learn from, they could inadvertently access, replicate and use private, sensitive or copyrighted information, leading to ethical and legal complexities.
• Propagation of Fake Profile/Identity, News, and Content: The increasing proficiency of GenAI in generating realistic text, video, audio and other media could facilitate the creation of convincing fake profiles, false news, and misleading content, raising significant ethical and societal concerns.

Possible Scenarios

• AI Algorithmic News Aggregators: In a future where GenAI content surpasses human-generated content, AI algorithmic systems could become the primary curators and distributors of news and information. This raises concerns about GenAI algorithmic bias and transparency, based on the data and content upon which they have been modeled and trained.
• Regulatory Interventions: Given the transformative impact of GenAI on the new digital society landscape, governments and international bodies might introduce GenAI regulations to ensure ethical use, data privacy, and content quality and diversity.
• Crisis of Authenticity:As GenAI becomes proficient ingenerating realistic content, the authenticity of digital assets (such as articles, reviews, and social media posts) may come into question. This will require new methods for verification, such as GenAI digital watermarking and labeling technologies, as discussed before.

The shift towards GenAI-generated content surpassing human-generated content is indeed a transformative development with nuanced implications that would require careful consideration and management.

The Emerging Risk of LLM MAD: How Self-Perpetuating GenAI’s Generated Content Feedback Loop Could Compromise LLMs

When this AI-generated content loops back into the data corpus used to train and retrain Large Language Models (LLMs) , as shown in Fig.4, it initiates a self-perpetuating cycle. Essentially, LLMs would be training on their own machine-generated outputs, leading to a form of “digital content cannibalism.” This Generative AI LLM Model Autophagy Disorder (MAD) phenomenon could distort the quality and integrity of these models, as they would increasingly reflect machine-generated patterns rather than a balanced blend of human and machine insights [3,4]. Over time, this could compromise the quality and reliability of information in the new GenAI digital landscape, making it difficult to distinguish between human wisdom and machine-generated content.

Digital Watermarks for GenAI Content: Enhancing Transparency and Preventing Feedback Loops in Foundation Models

The idea of a digital watermark or special label for GenAI-generated content aims to enhance transparency by clearly identifying the content’s origin. This could help users assess LLM model outrome credibility, although achieving this would require industry standardization. Adding a digital watermark to GenAI-generated content could serve multiple purposes: enhancing LLM model design comprehension, increasing user output transparency, and mitigating the risk of the content being reused in training large language models (LLMs). This could potentially prevent a self- destructing content feedback loop that might exacerbate misleading or harmful MAD phenomena, by stopping the continual propagation of GenAI- generated content within these large language models (LLMs).

Takeaways

The advent of Generative AI (GenAI) and Large Language Models (LLMs) is significantly impacting the new digital landscape of our society and has the potential to create a new Global Digital Knowledge-Based Alexandria. This contributes to an expansive AI-driven online repository of information, creating a seismic shift in the balance between human-generated and machine-generated content. While these technologies offer unparalleled opportunities for rapid knowledge dissemination and broader access to information, they also bring forth challenges such as information overload, variable content quality, and credibility concerns.

An emerging issue is a self-reinforcing cycle where Large Language Models increasingly train on their own machine-generated outputs, termed “Model Autophagy Disorder” (MAD), which could compromise the integrity and quality of these LLM models. To mitigate these challenges, mechanisms like digital watermarks for GenAI-generated content may be developed to enhance LLM model transparency and prevent recursive training loops. Effective management of this evolving landscape is crucial for maximizing its benefits while minimizing associated risks.

References

[1] C. Lima, “Unleashing the True Potential of Artificial Intelligence: The Key Building Blocks to Achieving AGI ”, Medium, April 10th 2023.

[2] C. Lima, “Building Foundation and Enterprise-Based Large Language Models (LLM) ”, Medium, September 25th 2023.

[3] C. Lima, “Large Language Models (LLMs) Facing the Prospect of MAD and the Threat of Prowling Bots ”, Medium, September 24th 2023.

[4] S. Alemohammad et all, “Mad paper Self-Consuming Generative Models Go MAD”, arXiv, July 4th 2023.

About the author

Claudio Lima, Ph.D., is a pioneer in the digital transformation of numerous industries, leveraging technologies such as Generative AI/LLM, Quantum Computing, IoT, and Blockchain/DLT. He specializes in nurturing emerging companies in the realm of Artificial Intelligence and Quantum Technologies. His expertise in cutting-edge technologies has helped to drive innovation and progress in various industry sectors, and he is widely regarded as a thought leader in the field of energy/renewables, smart city, telecom and others . He is passionate about exploring new frontiers and pushing the boundaries of what is possible with GenAI technology.