Human Superiority in Reasoning, Knowledge, and Understanding over LLMs - Consequences, Evolution, and Current Difference
Introduction
The below is the result of thought experiments with a recent "Thinking Model", based on the updated list of Information Axioms described at this link. The result is a set of assertions about the differences between humans and LLMs, and how LLMs (perhaps AI more generally) may need to evolve, to be human like.
To be clear, I am a huge fan of LLMs and am in awe at the rate at which really smart people are evolving these things. I derive great pleasure and productivity by using LLMs. I even scored brownie points with one of my children today by showing her how to do something she needed (not one of the main chat LLMs) - is there any greater reward for a parent!!! Also to be clear, I would not even be considering the below if it were not for the current capabilities of LLMs. However, like many in the tech industry, I believe in concepts such as "our best current understanding", "the perfect is the enemy of the good", and "Second star to the right and straight on till morning."
The below is not asserted to be infallible, just some thoughts for your consideration. For those that assert Agentic paragims, models validating other models, and other emerging practices overcome some of the observations below, I have no comment at this time. The question has arisen in my mind as well, and I need to give it more thought.
Regards,
Mark
Assertion Summary
Assertion: Humans are Embodied, LLMs are Not.
Consequence for LLMs: Lacking embodiment, LLMs struggle with true real-world understanding, common sense reasoning grounded in physical experience, and tasks requiring direct sensorimotor interaction or physical intuition. They can process language about the world, but lack the direct experiential basis for understanding.
Required LLM Evolution: LLMs must evolve towards embodied architectures, integrating sensory inputs (visual, auditory, tactile, etc.) and motor outputs to interact with and learn from the physical world directly. This might involve robotic embodiment or simulated physical environments.
Human Difference Today: Humans, through their embodied existence, possess a deeply grounded understanding of the physical world built from continuous sensorimotor experience, enabling intuitive physics, spatial reasoning, and a "feel" for reality that current LLMs cannot replicate.
Assertion: Humans Engage in Active Exploration, LLMs are Passive Data Consumers.
Consequence for LLMs: LLMs are limited to learning from pre-collected datasets, hindering their ability to discover novel information, formulate their own questions beyond their training data, or drive their own learning process based on curiosity or active inquiry. They are reactive learners, not proactive explorers of knowledge.
Required LLM Evolution: LLMs need to become active learners capable of initiating their own exploration, formulating hypotheses, designing experiments (even if simulated), and autonomously seeking out information to fill knowledge gaps and test understanding. This requires integrating mechanisms for curiosity, goal-generation, and active information seeking.
Human Difference Today: Humans actively explore their environments and knowledge domains, driven by intrinsic curiosity and a desire to understand. This active learning approach allows humans to continuously expand their knowledge and understanding in self-directed ways far beyond what is programmed or provided externally, a capability lacking in current LLMs.
Assertion: Humans Build Structured Semantic and Causal Models, LLMs Primarily Learn Statistical Patterns.
Consequence for LLMs: LLMs, relying heavily on statistical patterns, can exhibit limitations in understanding true causality, abstract reasoning beyond correlation, and representing structured knowledge that is resistant to superficial linguistic changes. Their "understanding" can be shallow and prone to errors in situations requiring deep causal or semantic inference.
Required LLM Evolution: LLMs must evolve to incorporate mechanisms for building and reasoning with explicit, structured representations of semantics, causality, and abstract concepts. This may involve integrating symbolic reasoning components, knowledge graphs, or more neurologically plausible models of structured knowledge representation.
Human Difference Today: Humans naturally construct rich, structured mental models of the world, explicitly representing causal relationships, semantic networks, and abstract concepts. This allows for robust causal reasoning, deeper understanding of meaning, and flexible problem-solving beyond pattern matching, a level of structural understanding that current LLMs do not possess.
Assertion: Human Reasoning is Flexible and Context-Adaptive, LLM Reasoning Can be Brittle.
Consequence for LLMs: LLM reasoning can be brittle and less robust when faced with truly novel situations, out-of-distribution inputs, or contexts requiring subtle shifts in reasoning strategies. Their generalization can be limited to scenarios similar to their training data, and they can struggle with unexpected or ambiguous inputs.
Required LLM Evolution: LLMs need to become more flexible and context-adaptive in their reasoning. This involves developing architectures that can dynamically adjust reasoning strategies based on context, learn to handle ambiguity and uncertainty more robustly, and generalize effectively to truly novel situations.
Human Difference Today: Humans possess remarkably flexible and adaptable reasoning, able to adjust their thinking strategies based on context, generalize knowledge to unforeseen situations, and handle ambiguity and uncertainty with nuance. This adaptability far surpasses the more rigid and data-dependent reasoning of current LLMs.
Assertion: Humans Integrate Multimodal Information Holistically, LLMs Process Modalities Separately.
Consequence for LLMs: Even multimodal LLMs often treat non-linguistic modalities as secondary, limiting their ability to derive holistic understanding from truly integrated sensory experiences. This can lead to deficits in tasks requiring seamless cross-modal reasoning, embodied understanding, or interpreting nuanced social cues that are inherently multimodal.
Required LLM Evolution: LLMs must evolve to deeply integrate information from multiple modalities in a way that mirrors human sensory integration, creating unified representations where different modalities are not just processed in parallel but fundamentally inform and enrich each other.
Human Difference Today: Humans naturally and seamlessly integrate information from all senses, emotions, and contextual cues to form a rich, unified, and embodied understanding of the world and social situations. This holistic, multimodal integration is far more sophisticated and deeply ingrained than current LLM approaches to multimodality.
Assertion: Humans Possess Meta-Cognition and Self-Reflection, LLMs Lack Genuine Self-Awareness.
Consequence for LLMs: Lacking meta-cognition, LLMs are unable to truly understand their own limitations, correct their own reasoning processes in a self-directed manner, or reflect on their own knowledge and biases internally. They operate "blindly" in terms of self-awareness, hindering self-improvement and robust error correction beyond external feedback.
Required LLM Evolution: LLMs need to develop genuine meta-cognitive capabilities, including the ability to monitor their own reasoning processes, detect and correct errors internally, reflect on their knowledge and limitations, and actively learn to improve their cognitive architectures in a self-directed way.
Human Difference Today: Humans possess meta-cognitive abilities, allowing for self-reflection, error detection, self-correction, and continuous improvement of their cognitive skills and knowledge. This capacity for self-awareness and self-improvement is a fundamental aspect of human intelligence that current LLMs do not possess.
Assertion: Humans are Intrinsically Motivated to Understand, LLMs are Extrinsically Task-Driven.
Consequence for LLMs: LLMs lack intrinsic curiosity and a drive for understanding for its own sake. Their learning and behavior are driven by external prompts or reward signals, limiting their ability to pursue independent lines of inquiry, explore knowledge domains beyond immediate tasks, or develop a genuine "thirst for knowledge."
Required LLM Evolution: LLMs need to incorporate intrinsic motivation mechanisms, simulating curiosity, a drive to understand, and the ability to generate self-directed goals and learning objectives. This may involve designing AI with internal reward systems based on knowledge acquisition, novelty seeking, or the resolution of internal uncertainty.
Human Difference Today: Humans are driven by intrinsic curiosity and a fundamental desire to understand the world around them, pursuing knowledge and exploration even without external rewards or task demands. This intrinsic motivation is a core engine of human learning and intellectual advancement that is absent in current LLMs.
Assertion: Human Intelligence is Evolutionarily and Culturally Shaped, LLMs are Engineered.
Consequence for LLMs: LLMs lack the deep optimization and contextual shaping provided by millions of years of evolution in complex real-world and social environments. They are engineered systems trained on digital data, missing the pressures and affordances of evolutionary and cultural history that have sculpted human cognition for survival and social cooperation. This can lead to limitations in their "common sense," social intelligence, and grounding in real-world constraints.
Required LLM Evolution: To truly match human-level intelligence, future AI development may need to draw inspiration from evolutionary and cultural processes. This could involve designing AI systems that can "evolve" over long timescales, adapting to changing environments and social contexts, and integrating mechanisms for cultural learning and knowledge transmission across generations.
Human Difference Today: Human intelligence is deeply shaped by a vast evolutionary history and rich cultural context, optimized for navigating the complexities of the real world and social interactions over millennia. This deep, embodied, and socially embedded evolutionary history is a fundamental differentiator that current LLMs, engineered in decades, cannot yet replicate.
Assertion: Humans Leverage Shared Cultural Schemas, LLMs Lack Deep Cultural Context.
Consequence for LLMs: LLMs, while trained on vast amounts of text reflecting cultural information, often lack a deep, nuanced, and embodied understanding of cultural context, norms, and implicit knowledge. This can lead to misinterpretations in culturally sensitive situations, a lack of genuine cultural competence, and an inability to fully grasp the implicit meanings embedded within cultural communication.
Required LLM Evolution: LLMs must develop more sophisticated mechanisms for acquiring, representing, and reasoning with cultural knowledge and schemas. This might involve training on data that explicitly encodes cultural norms, values, and histories, as well as architectures that can model and utilize implicit cultural context in communication and reasoning.
Human Difference Today: Humans possess a deep, often implicit, understanding of their own cultures and, through acculturation, can learn to navigate and understand other cultures in nuanced ways. This deep cultural competence, shaped by lived experience within cultural contexts, goes beyond the surface-level cultural information processing of current LLMs.
Assertion: Humans Exhibit Bounded Rationality as an Adaptive Strategy, LLMs are Fundamentally Limited.
Consequence for LLMs: While LLMs can process vast amounts of data, their current architectures and training paradigms inherently exhibit a form of "bounded rationality" arising from limitations in their design and training data. This manifests as biases, inconsistencies, and errors, even when computational resources are abundant. Their bounded rationality stems from architectural and data limitations, not necessarily adaptive cognitive economy.
Required LLM Evolution: Future LLM development should explore architectures and training methods that not only improve performance but also explicitly address and mitigate the sources of bounded rationality in current models. This might involve architectures that better reflect human cognitive constraints and strategies for efficient information processing under limitations, moving towards adaptive bounded rationality rather than just exhibiting limitations as a byproduct of design.
Human Difference Today: Humans operate with bounded rationality as an adaptive strategy, using heuristics and biases to efficiently navigate complex information environments under cognitive constraints. This bounded rationality, while leading to occasional errors, is also a source of cognitive efficiency and robustness, a nuanced form of limitation compared to the different kind of bounded rationality currently inherent in LLMs.