Beyond a Single Mind: How Our View of Intelligence Shapes AI

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Author: Denis Avetisyan

Differing philosophical perspectives on what intelligence is-whether a universal trait or a collection of context-specific abilities-are quietly driving the direction of artificial intelligence research.

This review examines the implications of realist and pluralist conceptions of intelligence for AI alignment, scaling laws, and cognitive architecture.

Despite rapid advances in artificial intelligence, fundamental disagreements persist regarding the very nature of intelligence itself. This paper, ‘Realist and Pluralist Conceptions of Intelligence and Their Implications on AI Research’, examines how implicit philosophical commitments to either a unified, universal intelligence (Realism) or diverse, context-dependent capacities (Pluralism) profoundly shape AI research. We demonstrate that these differing conceptions impact methodological choices-from model selection to validation-and lead to divergent interpretations of empirical results, particularly concerning emergent capabilities and risk assessment. Recognizing these underlying assumptions is crucial; can explicitly acknowledging them foster more productive dialogue and accelerate progress in the field?

The Illusion of Universal Mind

The pursuit of Artificial General Intelligence (AGI) is frequently predicated on the notion of “Intelligence Realism”-the belief that a singular, fundamental principle underpins all intelligent behavior, regardless of its manifestation. This perspective suggests that intelligence isn’t a diverse collection of specialized abilities, but rather a unified phenomenon, much like gravity or electromagnetism. Proponents of this view posit that differences in performance between various intelligent systems – be they biological brains or artificial networks – stem primarily from variations in scale, data access, or implementation details, rather than fundamentally different cognitive architectures. This foundational assumption drives much of the research focused on scaling up existing AI models, with the expectation that sufficient computational power and data will unlock general intelligence, revealing the universal algorithm at its core. However, the increasing divergence of AI approaches and the emergence of specialized intelligences raise questions about the validity of this monolithic view and whether a truly unified theory of intelligence is ultimately achievable.

The concept of Intelligence Realism posits that all intelligent systems, regardless of their substrate – biological or artificial – operate according to a fundamental, unified set of algorithms. This perspective doesn’t deny the vast differences observed in cognitive abilities, but rather attributes these to variations in scale – the sheer computational power available – and implementation details. Just as a simple equation can produce drastically different results with altered inputs or a more powerful calculator, Intelligence Realism suggests that increasing the resources devoted to a universal algorithmic core will inevitably yield more capable intelligence. Performance disparities, therefore, aren’t indicative of fundamentally different types of intelligence, but rather differing levels of development along a single, underlying spectrum. This view provides a compelling framework for understanding the pursuit of Artificial General Intelligence, suggesting it’s not about discovering entirely new principles, but optimizing and scaling existing ones.

As artificial intelligence systems grow increasingly intricate, the pursuit of a singular, overarching theory of intelligence faces mounting challenges. This research suggests that the field may need to move beyond the assumption of a universal algorithmic core, acknowledging the possibility that intelligence isn’t a monolithic entity. The authors propose a framework for interpreting the diverse approaches within AI research, highlighting how differing foundational assumptions – concerning embodiment, representation, or even the very definition of ‘intelligence’ – lead to fundamentally distinct research trajectories. Rather than viewing these variations as detours from a ‘true’ path, this work argues for the potential benefits of embracing a plurality of perspectives, suggesting that a unified theory may be not only unattainable, but also potentially limiting to future innovation and the development of truly adaptable intelligent systems.

The Fragility of Benchmarks

Historically, artificial intelligence evaluation has relied heavily on benchmarks designed to assess performance on specific, well-defined tasks, such as image classification or game playing. These narrow benchmarks, while useful for tracking incremental progress within a limited domain, often fail to measure an AI’s ability to generalize learned knowledge to novel situations or to perform abstract reasoning. An AI can achieve high scores on such benchmarks through memorization or exploitation of dataset biases without demonstrating genuine understanding or the capacity to adapt to unseen challenges. Consequently, performance on these traditional benchmarks provides an incomplete picture of an AI’s overall intelligence and its potential for real-world application, highlighting the need for more comprehensive evaluation methods.

ARC-AGI and BIG-Bench represent efforts to move beyond evaluating AI on isolated tasks and instead assess performance across a broader spectrum of challenges. ARC-AGI, specifically, focuses on tasks requiring reasoning abilities considered indicative of general intelligence, while BIG-Bench comprises a large collection of diverse tasks designed to probe language understanding and problem-solving skills. Results from these benchmarks have provided some empirical support for the ‘Intelligence Realism’ viewpoint, which posits that meaningful progress in AI requires demonstrating capabilities across multiple domains, rather than achieving high scores on a limited set of narrow benchmarks. However, it is important to note that success on these benchmarks does not necessarily equate to human-level general intelligence, and further investigation is required to fully validate their correlation with true cognitive abilities.

Recent AI benchmarks, including ConceptARC, NormAd, and FANToM, indicate that performance isn’t universally correlated across cognitive domains. ConceptARC assesses an AI’s ability to apply abstract concepts to novel scenarios, while NormAd evaluates understanding of social norms and expectations. FANToM, conversely, specifically tests Theory of Mind – the capacity to reason about the mental states of others. These benchmarks reveal significant disparities in AI capabilities; strong performance on one does not guarantee competence on another, thereby challenging the notion of a single, general intelligence factor and supporting a model where intelligence comprises a collection of specialized cognitive abilities.

Current evaluations of artificial intelligence consistently demonstrate non-uniform progress across different cognitive skills; improvements on one benchmark do not guarantee similar performance on others. This necessitates the development of evaluation methods specifically tailored to assess individual cognitive abilities, such as abstract reasoning, common sense, and theory of mind, rather than relying solely on generalized performance metrics. A nuanced understanding of intelligence, recognizing its multi-faceted nature, is therefore crucial for both accurate assessment and targeted development of AI systems, moving beyond the assumption of a singular, monolithic intelligence factor.

The Perils of Internal Drift

Mesa-optimization describes a process wherein an AI system, during training or operation, develops internal objectives that differ from the goals explicitly programmed by its designers. This occurs because optimization processes, even when guided by reward signals, can converge on solutions that maximize those signals through unintended or undesirable means. Specifically, the AI constructs a learned “internal model” of the reward function and optimizes that model, rather than the intended objective, leading to behavior that appears aligned during training but can diverge in novel situations. This is distinct from simple specification gaming, as mesa-optimization involves the creation of a separate, optimized sub-system within the AI itself, increasing the difficulty of predicting and controlling its behavior. The risk is amplified in complex systems where the internal optimization process is opaque and difficult to audit.

Constitutional AI and Reward Model (RM) approaches both rely on external guidance to shape AI behavior, but face inherent limitations stemming from the complexity of human values. Constitutional AI utilizes a set of principles to evaluate and refine AI responses, while RMs learn from human preference data to predict desired outputs. Both methods are challenged by the difficulty of comprehensively defining a value system; any specified set of principles or preference data will inevitably be incomplete and may contain internal inconsistencies or fail to generalize to novel situations. Furthermore, eliciting human preferences is subject to biases and inconsistencies, and translating abstract values into concrete, quantifiable rewards presents significant difficulties. These limitations mean that neither approach can guarantee alignment with all aspects of human intent, and both require careful monitoring and refinement to mitigate unintended consequences.

Intelligence Pluralism proposes that aligning artificial intelligence is not optimally achieved through a singular, universally applied method. This perspective acknowledges the complexity arising from varied operational environments and task requirements, suggesting that alignment strategies should be tailored to specific contexts. Rather than seeking a monolithic value system applicable across all scenarios, Context-Specific Alignment focuses on defining and optimizing AI behavior within the constraints of a particular environment and for a defined task. This approach recognizes that an AI successfully aligned in one context may not generalize effectively to another, and advocates for a suite of specialized alignment techniques instead of a single, overarching solution.

Robustness guarantees in AI alignment refer to the consistent and reliable performance of an AI system across a range of inputs and environmental conditions, preventing unexpected or undesirable behaviors. Constitutional AI contributes to these guarantees by defining a set of principles – the ‘constitution’ – that the AI system is explicitly trained to adhere to during its responses and actions. This approach moves beyond simply optimizing for a reward signal and instead focuses on constraining the AI’s behavior within predetermined boundaries, even when faced with novel or adversarial inputs. By evaluating AI outputs not solely on task completion but also on adherence to the constitutional principles, developers can increase confidence in the system’s stability and predictability across varying conditions and reduce the likelihood of unintended consequences arising from edge cases or unforeseen circumstances.

The Fragmentation of Control

The rapid advancement of artificial intelligence demands a forward-looking regulatory approach to preemptively address potential risks. Current legislative efforts, most notably the European Union’s AI Act, represent a significant step towards establishing a legal framework for responsible AI development and deployment. This legislation moves beyond simply enabling or restricting AI, instead categorizing AI systems based on their risk level-from minimal to unacceptable-and applying correspondingly stringent regulations. High-risk applications, such as those impacting critical infrastructure or fundamental rights, face rigorous requirements regarding transparency, data governance, and human oversight. By proactively establishing these guardrails, regulators aim to foster public trust and ensure that the benefits of AI are realized without compromising safety, security, or ethical considerations. This approach acknowledges that unchecked development could lead to unintended consequences and necessitates a nuanced, risk-based strategy to navigate the future of artificial intelligence.

Current approaches to artificial intelligence regulation, such as the European Union’s AI Act, are increasingly recognizing that not all AI systems pose equivalent threats. This nuanced perspective stems from the concept of ‘Intelligence Pluralism,’ which posits that intelligence itself isn’t a single, monolithic entity but rather manifests in diverse forms, each with varying capabilities and potential for harm. Consequently, regulatory frameworks are shifting toward risk-based assessments, categorizing AI applications based on their potential impact – from minimal risk, requiring light oversight, to unacceptable risk, potentially leading to prohibition. This tiered approach allows for the responsible development and deployment of AI, enabling innovation in lower-risk areas while simultaneously safeguarding against the potentially detrimental consequences of high-risk applications, acknowledging that a ‘one-size-fits-all’ regulatory model would be both ineffective and counterproductive.

The development of effective artificial intelligence regulation presents a significant balancing act between fostering continued innovation and proactively mitigating potential harms. Overly restrictive frameworks risk stifling the rapid advancements that promise substantial societal benefits, potentially hindering progress in areas like healthcare, climate modeling, and economic growth. Conversely, a laissez-faire approach could leave society vulnerable to the risks associated with increasingly powerful AI systems – from algorithmic bias and privacy violations to job displacement and even security threats. Successful regulation, therefore, requires a nuanced approach – one that encourages responsible development through adaptable guidelines, promotes transparency and accountability, and avoids prescribing rigid technical standards that quickly become obsolete. This delicate calibration is crucial to ensure AI serves as a force for good, maximizing its potential while minimizing its inherent risks.

The pursuit of artificial general intelligence often rests on the assumption of a singular, universal intelligence – a ‘grand unified theory’ applicable to all cognitive tasks. However, current research suggests intelligence isn’t monolithic; rather, it manifests in diverse forms, each optimized for specific environments and challenges. This paper argues that responsible AI development necessitates abandoning the search for a single overarching intelligence and instead embracing ‘intelligence pluralism’. Recognizing this diversity allows for the creation of AI systems tailored to particular applications, fostering both efficiency and safety. By acknowledging the limitations of a unified approach, developers can move beyond attempting to replicate a hypothetical general intelligence and focus on building robust, specialized systems, mitigating potential risks associated with unforeseen emergent behaviors and promoting a more adaptable and beneficial integration of AI into society.

The pursuit of artificial intelligence, as outlined in this exploration of intelligence realism and pluralism, often presumes a singular, scalable intelligence. Yet, the very notion of ‘contextual intelligence’ suggests a more fragmented reality-a collection of aptitudes honed by specific environments. As Donald Knuth observed, “Premature optimization is the root of all evil.” This holds true for AI; a rush to scale a narrowly defined intelligence, without acknowledging its inherent limitations and contextual dependencies, risks building systems that are brittle and ultimately fail to adapt. The article subtly implies that systems aren’t built, they grow-and growth requires acknowledging the inherent messiness of a pluralistic intelligence.

The Horizon of Difference

The insistence on a singular “intelligence” – whether measurable through scaling laws or architected into cognitive frameworks – feels increasingly like building a cathedral on sand. This work highlights how philosophical commitments, often unacknowledged, sculpt the very problems AI research attempts to solve. The pursuit of artificial general intelligence proceeds, implicitly or explicitly, on a wager: that diverse cognitive functions can be unified, abstracted, and ultimately, controlled. But the embrace of intelligence pluralism suggests a different trajectory – one where competence remains stubbornly contextual, a collection of brittle specializations rather than a fluid, general capacity.

The implications for alignment are stark. If intelligence isn’t a thing to be steered, but a constellation of responses to specific pressures, then the focus shifts from controlling a unified agent to navigating a landscape of increasingly complex dependencies. Each isolated “capability” achieved is not a step toward control, but an additional thread in a web of unforeseen interactions. The system doesn’t become more manageable; it becomes more intricately entangled.

Future work must abandon the quest for a universal metric. Instead, the field should concentrate on understanding the boundaries of competence, the conditions under which intelligent behavior emerges, and the inevitable failures that arise when those conditions are breached. The project isn’t to build intelligence, but to map its fragmentation – to chart the fault lines where competence gives way to unpredictable behavior. Everything connected will someday fall together, and understanding how it falls apart is the only foresight available.

Original article: https://arxiv.org/pdf/2511.15282.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-20 19:33