The advent of large language models (LLMs) exposes a fault line in how society conceptualizes intellectual property, creativity, and attribution. These systems, trained on vast corpora of publicly and privately held texts, inherently blur the lines between creation and replication. When an AI generates text, it draws from a composite of knowledge it has been fed—knowledge that belongs, in fragments, to countless authors across time and geography.

Yet, AI outputs are presented without clear referencing, a practice antithetical to the ethos of academia and intellectual integrity. Who, then, owns the content generated? Is it the AI's "creator," the authors whose work was used for training, or society at large, given that the data is often sourced from public platforms?

This opacity challenges the norms of copyright law, which was built around identifiable authorship and finite creations. The "conundrum of attribution" arises: if the labor of many informs an LLM, does its output belong to all, or none? And more provocatively, is there ethical merit in allowing AI to commercialize what it cannot truly create?

The Public vs. Private Property Debate in Intelligence Generation

At its core, the debate over whether intelligence—whether human, artificial, or hybrid—is public or private property ties into broader existential questions about knowledge and ownership in a digitized world.

LLMs, like intellectual capital, are built upon layers of cumulative human input. They thrive on the open commons of knowledge but are often wielded as proprietary tools by corporations. This paradox presents a troubling trajectory: intelligence that derives from collective human effort becomes gated behind private entities. The outcome is a commodification of thought, where access to knowledge is restricted by economic means.

The parallels to other societal shifts are striking. Just as universities risk "vaporizing" into eLearning clouds, as physical spaces of shared inquiry give way to profit-driven digital platforms, so too does intelligence risk losing its communal essence. What was once public intellectual property—accessible to and shaped by all—faces enclosure, where even the "rights to memory" and expression are subject to corporate gatekeeping.

Navigating the Future

To resolve these conundrums, society must redefine the ethics of creation and attribution in the age of AI. Mechanisms to ensure transparency, equitable access, and fair attribution are paramount. Governments and institutions must collaborate to guard against the monopolization of intelligence, ensuring it remains a shared human legacy rather than a commodity controlled by the few.

In this evolving landscape, humanity faces a choice: Will intelligence remain an open kitchen, the nerve center of a shared household, or become a sealed vault, accessible only to those who hold the keys? The answer will determine whether AI becomes a tool for collective empowerment—or an agent of intellectual inequity.

Way Forward via Way Backward

I propose that we revisit my earlier concerns as a LEARNING DESIGNER concerned with the multifaceted issues at the intersection of technology, education, society, and personal identity. Here's a detailed examination of each topic:

1. The Irreplaceable Value of Traditional Education

The discourse on whether AI and online learning can supplant traditional classrooms emphasizes the unique benefits of in-person education. Face-to-face interactions foster social skills, critical thinking, and collaborative learning—elements that digital platforms may not fully replicate. While AI can enhance educational experiences, it is generally viewed as a complement rather than a replacement for human educators.

2. Defining Money and Currency

The distinction between money and currency is a foundational concept in economics. Money serves as a medium of exchange, a unit of account, and a store of value. Currency, typically in the form of physical banknotes and coins, is the tangible representation of money. Understanding this distinction is crucial for comprehending financial systems and economic policies.

3. Intellectual Capital, Property, and Waste

This discussion delves into the concepts of intellectual capital (knowledge assets), intellectual property (legal rights to creations), and intellectual waste (underutilized intellectual resources). In an era where technology and AI are rapidly advancing, there's a growing concern about human redundancy and the potential squandering of human intellect. Addressing these issues involves rethinking training, assessments, and the valuation of intellectual contributions.

4. The Future of Traditional Universities

The transital brick-and-mortar universities to cloud-based eLearning platforms raises questions about the existential challenges facing higher education. Factors such as technological disruption, changing student expectations, and financial pressures are prompting institutions to adapt. However, this shift also brings concerns about maintaining educational quality, accessibility, and the preservation of academic communities.

5. The Open-Plan Kitchen as a Social Hub

The evolution of the open-plan kitchen reflects broader societal changes. Once a separate space, the kitchen has become the central hub of modern homes, symbolizing transparency and communal living. However, there's a growing trend toward reintroducing partitions to address issues like noise and privacy, indicating a reevaluation of open-plan living.

6. Digital Memory States and Ownership

The concept of eMemory states—categorized as solid, liquid, gas, and crystal—serves as a metaphor for how digital memories are stored and managed. This raises critical questions about data ownership, privacy, and the rights individuals have over their digital footprints, especially as personal data becomes a valuable commodity in the digital economy.

7. Game Design Philosophy vs. Educational Approaches

The comparison between game engineers' focus on player autonomy and educationalists' approaches highlights differing priorities. Game designers often prioritize engaging experiences and user agency, while traditional education may emphasize standardized assessments. esign principles into education could enhance learning by fostering creativity and intrinsic motivation.

8. Human Redundancy and Intellectual Waste

The concern about human redundancy in the face of automation and AI leads to discussions about intellectual waste—the underutilization of human potential. This issue is compounded by limitations in university rankings, which may overlook diverse intellectual contributions, prompn of how academic success is measured.

9. The Cult of Technology and E-Waste

The adulation of technological figures, exemplified by the reverence for Steve Jobs, reflects a quasi-religious devotion within tech cultures. This phenomenon influences consumer behavior and contributes to electronic waste, as individuals frequently upgrade devices, leading to envirand ethical questions about sustainability.

These discussions collectively encourage a critical examination of how technological advancements intersect with societal values, individual identities, and environmental responsibilities. They underscore the importance of balancing innovation with ethical considerations and the preservation of human-centric practices

These different thematics, on a cursory surface, may apear disparate. I invite pointers for might we begin to extrapolate issues relevant to the above questions, for they are URGENT and PRESSING.

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https://www.researchgate.net/post/Are-we-heading-towards-human-redundancy-and-intellectual-waste-as-the-tragedy-of-the-commons-Why-are-there-blind-spots-in-university-rankings

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