Intelligence Unbound: Human Evolution, Artificial Limits, and the Ethics of Cognitive Power

  Intelligence Unbound: Human Evolution, Artificial Limits, and the Ethics of Cognitive Power

Rahul Ramya

09.02.2025

Patna, India

Human intelligence is an achievement of millions of years of human existence. Further human intelligence is unique in the sense that it has enriched itself even from past which is not there and from future which has still not arrived, the first is from inheritance and the second is from anticipation. Both are highly analytical. 

Human intelligence is enriched from both tangible and intangible and even from those which are not seen and even from other family kingdoms like those of plants and of other animals and of microorganisms. All these are done by humans without any external prompts most of the time and their impetus comes from their observational capacity and sense of self both of which are not present in any form of AI. The most astonishing part of human intelligence is that it can be enriched even from subconscious and most dramatically even from dreams. These things are beyond the capabilities of AI which is the creation of human intelligence only.

Hence AI has limitations of its intelligence to the extent humans are interested to share their intelligence with machines. This volition is again a unique characteristic of human intelligence. The only fields where AI defeats humans include the speed with which it's capable of processing vast amount of data in virtually no time but at the expense of vast amount of cost and energy which if spent on humanity can produce more wonderful results.

Human limitations in speed and processing may be argued to be limited due to evolutionary reasons as we neither need such speed. Humans also don't need from evolutionary purposes such vast amount of data at a time as it's risky to put all knowledge and information at one's disposal. Most tasks in human life is socially and contextually limited which don't need such vast amount of data at one place. We see now this concentration of knowledge and information at one place and their untested integration at unprecedented speed is causing havoc to humanity and the agents of havocs are corporations and corporations like governments.

This layered understanding of human intelligence reveals additional dimensions worth exploring. The inheritance aspect manifests not just through genetic transmission but through cultural memory, oral traditions, and collective wisdom passed down through generations. Each society's unique ways of knowing, indigenous knowledge systems, and traditional practices represent different pathways of human intelligence that have evolved through intimate interactions with specific environments and challenges.

The anticipatory nature of human intelligence enables not just prediction but also the creation of elaborate hypothetical scenarios, moral philosophies, and complex social contracts. This capacity for abstract thinking and future planning has led to the development of civilization itself - from agriculture to architecture, from legal systems to scientific theories.

The human ability to learn from other life forms goes beyond mere observation. It encompasses biomimicry in engineering, understanding of ecological relationships, and even the development of medicine through studying plant properties and animal behaviors. This cross-kingdom learning has been fundamental to human survival and technological advancement.

The role of subconscious processing and dreams in human intelligence points to the deep integration between different levels of awareness. Indigenous cultures have long recognized dreams as sources of knowledge and guidance. Modern psychology continues to uncover the critical role of subconscious processing in creativity, problem-solving, and emotional intelligence.

The evolutionary constraints on human processing speed and data capacity might actually represent sophisticated optimization. Our selective attention and ability to filter information based on relevance have evolved alongside our social structures and environmental needs. This optimization allows for deep, contextual understanding rather than just broad data processing.

The current technological paradigm of massive data processing and centralized control represents a significant departure from these evolved patterns of human intelligence. The adverse effects of this departure are becoming increasingly evident in various spheres - from mental health issues related to information overload to the erosion of local knowledge systems and community wisdom.

The energy cost of artificial intelligence operations raises important questions about sustainability and resource allocation. The massive computing infrastructure required for AI operations has significant environmental impacts, contrasting with the relatively low-energy, high-efficiency operation of human intelligence within its evolved parameters.

The social and contextual nature of human tasks reflects our evolution as social beings. Our intelligence is inherently embedded in relationships, cultural contexts, and local environments. This distributed, contextualized nature of human intelligence provides resilience and adaptability that centralized AI systems cannot replicate.​​​​​​​​​​​​​​​​

Here is the corrected and polished version:

Humans possess unique capabilities for multitasking with the same intelligence, transferring intelligence across different fields of work simultaneously and in varying contexts, as well as transmitting intelligence both to the next generation and within the same generation. They can cooperate, compete, and engage in conflict using this intelligence.

Furthermore, every human demonstrates different levels of intelligence in different contexts and may consciously choose to restrict the use of their intelligence in certain situations. One of the most striking features is that different humans, even those sharing the same origins and contexts, display varying levels of intelligence - there is no "one size fits all" model within the same generation.

All these characteristics are not only absent in AI but are fundamentally unattainable by machines, as machines are essentially statistical tools, while humans function as multiple tools simultaneously.

The original text captures significant insights about the dynamic, adaptable, and contextual nature of human intelligence that sets it apart from artificial intelligence. Would you like me to expand upon these ideas or explore any particular aspect in more detail?​​​​​​​​​​​​​​​​

👍👍 Given these limitations of AI and AGI, should we dedicate so much time, money, and energy to developing them for benevolent purposes?  If we don't act now, these technologies will concentrate money, knowledge, and information in a few hands, creating power dynamics that serve the interests of those who control them.  We already see corporations demanding deregulation, the right to own nuclear power stations, and similar things; in the future, they may determine election results and control the military, as the powerful rarely share power.  AI's use in benevolent areas like health and education won't empower common people if they depend on costly, complex technology.  Our engagement with AI may diminish our natural intelligence, further demoralized by claims of AI's false superiority.  Technological determinism will then mark the end of human freedom.

The Unique Nature of Human Intelligence: A Multifaceted Perspective

Human intelligence, shaped through millions of years of evolution, stands as a remarkable achievement that draws from both the past through inheritance and the future through anticipation. This intelligence demonstrates extraordinary versatility in its ability to learn from both tangible and intangible sources, including observations of other life forms like plants, animals, and microorganisms. What makes it particularly fascinating is its capacity to derive insights from the subconscious mind and dreams - domains that remain inaccessible to artificial intelligence.

The multitasking capability of human intelligence is particularly noteworthy. Humans can simultaneously engage in different activities, transfer knowledge across various fields, and adapt their intelligence to different contexts. This adaptability extends both horizontally within the same generation through cooperation, competition, and even conflict, and vertically across generations through various forms of knowledge transmission. The human brain, unlike AI, can seamlessly switch between different modes of thinking and application, making it a versatile tool for diverse situations.

A distinctive feature of human intelligence is its variability among individuals. Even people sharing similar backgrounds and contexts display different levels of intelligence, defying any standardized model. This diversity in intelligence levels and its contextual application is a natural phenomenon that cannot be replicated by artificial intelligence. Furthermore, humans possess the unique ability to consciously regulate their use of intelligence depending on the situation - a form of emotional and social intelligence that machines cannot emulate.

While artificial intelligence excels at processing vast amounts of data at high speeds, it remains fundamentally a statistical tool, limited by its programming and the intelligence humans choose to share with it. The high energy and financial costs associated with AI processing contrast sharply with the efficiency of human intelligence, which has evolved to handle contextually relevant information rather than process enormous amounts of data simultaneously.

The current trend of concentrating knowledge and information in centralized systems, primarily through corporations and government-like entities, poses significant challenges. This approach contradicts the naturally distributed and contextual nature of human intelligence, which has evolved to handle information in socially and environmentally appropriate ways. The untested integration of vast amounts of data at unprecedented speeds is causing disruptions to human society, highlighting the importance of understanding and preserving the unique characteristics of human intelligence.

In essence, human intelligence represents a complex, adaptable, and multifaceted system that functions as many tools simultaneously, rather than just a statistical processing unit. This fundamental difference underscores why artificial intelligence, despite its impressive capabilities in specific areas, cannot fully replicate or replace the rich tapestry of human cognitive abilities.​​​​​​​​​​​​​​​​

👍👍 The utilitarian approach to AI technology obscures the real damages it causes by exacerbating inequalities in skill development, the common people's oversight of power distribution mechanisms and wealth accessibility, the harm done to the least advantaged (per John Rawls's difference principle), and the ability of all to develop capabilities and ensure justice with dignity. If these philosophical considerations are neglected in the blind pursuit of preference utilitarianism, no amount of philanthropic displays or messianic political gimmicks can save humanity as in utilitarian philosophy, injustice is perceived as the overall loss of utility—such as happiness or well-being—compared to what could have been achieved. An unjust society is one where the collective happiness of its members is significantly lower than it could be.

The Perils of Utilitarian AI: A Philosophical Analysis of Technology's Impact on Human Society

The utilitarian approach to artificial intelligence technology represents a narrow and potentially dangerous perspective that fails to account for the profound implications of AI on human society and development. This approach, focused primarily on aggregate benefits and efficiency gains, masks several critical concerns that deserve deeper examination.

First, the impact on skill development is particularly concerning. As AI systems become more sophisticated, there is a growing risk of creating two distinct classes: a small elite who understand and control AI systems, and a larger population whose traditional skills are devalued or rendered obsolete. This bifurcation of society threatens the fundamental human capacity for learning and adaptation that has evolved over millions of years. Unlike human intelligence, which develops through diverse experiences, social interactions, and generational knowledge transfer, AI-driven skill development often follows a predetermined, standardized path that may not accommodate the natural variety in human learning and capability development.

The concentration of power through AI technology presents another critical challenge. While human intelligence naturally distributes across societies in varying forms and contexts, AI systems tend to centralize power in the hands of corporations and governmental entities. This centralization obscures the traditional mechanisms of power distribution that humans have developed over generations. The common people's ability to oversee and influence these power structures diminishes as decision-making becomes increasingly automated and opaque.

John Rawls's difference principle, which argues that social and economic inequalities should be arranged to benefit the least advantaged members of society, is particularly relevant here. The current implementation of AI technology often violates this principle by prioritizing efficiency and aggregate utility over distributive justice. The capability of humans to operate at different levels of intelligence in different contexts, to choose when and how to apply their intelligence, and to transfer knowledge both horizontally and vertically within society, is being undermined by AI systems that enforce standardized approaches to problem-solving and decision-making.

The notion of justice with dignity becomes particularly pertinent when considering how AI affects human agency and self-determination. Human intelligence, with its unique ability to draw from both conscious and subconscious processes, to learn from dreams and intuitive insights, and to adapt to various social and environmental contexts, is being increasingly subordinated to machine-driven decision-making processes. This subordination threatens human dignity by reducing the space for individual judgment, creativity, and moral agency.

The philosophical shortcomings of preference utilitarianism in AI development become evident when we consider how it fails to account for the multifaceted nature of human intelligence. While humans can simultaneously serve as multiple tools - capable of cooperation, competition, and conflict resolution - AI remains essentially a statistical tool, albeit a powerful one. The utilitarian focus on maximizing aggregate utility overlooks the importance of preserving and nurturing these diverse aspects of human intelligence.

Furthermore, the massive energy and financial resources devoted to AI development raise questions about opportunity costs. These resources might be better spent on enhancing human capabilities and addressing social inequalities directly, rather than pursuing technological solutions that may exacerbate existing disparities.

The notion that philanthropic initiatives or political maneuvers can compensate for the fundamental injustices created by a utilitarian approach to AI is particularly problematic. Such attempts at remediation fail to address the core issue: the erosion of human agency and the capacity for authentic development of intelligence across diverse contexts and generations.

Ultimately, the injustice arising from a utilitarian approach to AI extends beyond mere losses in collective happiness or well-being. It represents a fundamental threat to the evolved patterns of human intelligence and social organization that have developed over millennia. A truly just approach to AI development must recognize and preserve the unique characteristics of human intelligence - its variability, its contextual nature, its capacity for multitasking and knowledge transfer, and its ability to operate at different levels in different situations. Without such consideration, the pursuit of technological advancement risks undermining the very foundations of human society and development.​​​​​​​​​​​​​​​​ This is manifested by the following example.

while technology holds the promise of transforming education, it is imperative to implement inclusive strategies that ensure all students, regardless of their background, can access and benefit from these advancements. Addressing the digital divide through targeted initiatives and policies is essential for achieving truly equitable education.

The data increasingly shows that AI tools alone are not sufficient for effective learning, particularly in contexts where human interaction, contextual understanding, and emotional intelligence play a crucial role. Instead, remote learning models that integrate AI with human teachers—leveraging both machine efficiency and human adaptability—yield better educational outcomes. Here’s why:

1. Human-AI Collaboration Enhances Learning Outcomes

AI-powered tools can automate assessments, personalize learning paths, and provide instant feedback. However, studies indicate that students learn better when these tools are used to augment, not replace, human teachers. Teachers bring critical thinking, empathy, and motivation—factors AI lacks.

Example:

   •   A McKinsey study (2020) on education technology found that AI-assisted teachers improved student performance by 30%, whereas AI-only systems showed diminishing returns after an initial boost.

   •   India’s eVidyaloka initiative (which connects remote students with human teachers via digital platforms) has demonstrated significant improvements in student engagement compared to purely AI-driven models.

2. AI Lacks the Emotional and Cognitive Nuances of Human Teaching

Learning is a deeply social and emotional process. While AI can personalize content, it cannot fully address motivational issues, mental health challenges, or the need for real-time contextual adjustments in teaching.

Example:

   •   Research from the OECD (2021) found that students who received AI-generated feedback alongside teacher interventions performed better than those relying solely on AI feedback.