How AI vs Human Thinking Reveals Key Insights into Cognition

Understanding the Fundamental Differences Between AI and Human Thinking

The evolution of artificial intelligence (AI) has sparked a fascinating debate among researchers and tech enthusiasts: How does AI thinking compare to human cognition? The recent video, AI vs Human Thinking: How Large Language Models Really Work, delves into this comparison, looks at six distinct areas, and raises intriguing questions regarding how these two forms of 'thinking' align and diverge.

In AI vs Human Thinking: How Large Language Models Really Work, the discussion dives into cognitive comparisons, exploring key insights that sparked deeper analysis on our end.

The Mechanisms of Learning: Humans vs. Large Language Models (LLMs)

Humans learn through neuroplasticity—an incredible biological process that enables our brains to adapt based on experiences, making us remarkably efficient in learning from just a few instances. Conversely, LLMs rely on backpropagation, processing enormous amounts of data and adjusting their internal parameters in an entirely mechanical manner. Unlike humans, who can grasp a new concept with minimal exposure, LLMs require thousands of examples to build their understanding, resulting in a fundamentally different approach to acquiring knowledge.

Processing Information: A Parallel Comparison

Processing is another critical area where AI and human cognition diverge. While the human brain operates through billions of actively communicating neurons, processing concepts holistically, LLMs assess input in a linear fashion, operating on discrete tokens. Unlike humans, who grasp sentences by linking meaning to prior knowledge and context, LLMs create predictions based on patterns in their training, showcasing a significant difference in comprehension.

Memory Architecture: An Analysis

Memory systems also illustrate pivotal contrasts between humans and machines. Humans possess various levels of memory—sensory, working, and long-term—allowing us to make connections between experiences. AI, however, has a much simpler memory structure. Their knowledge comes from data absorbed during training and is limited by a context window, highlighting their inability to form truly associative memory as seen in humans. This encapsulates a significant limitation of AI in storing and recalling intricate, contextual relationships.

Reasoning and Error: The Nuances of Cognition

Regarding reasoning, humans engage in complex cognitive modeling influenced by our emotions and judgments. While LLMs attempt to simulate reasoning by producing believable token sequences, they lack true cognitive awareness. Their frequent errors—often termed 'hallucinations'—expose the disparity in understanding between human cognition and AI outputs. Unlike confabulation in humans, where false memories are constructed unknowingly, LLMs produce inaccuracies without awareness of truth, showcasing their limitations in logical reasoning.

The Role of Embodiment: An Inherent Difference

The final contrast lies in the concept of embodiment. Humans exist within a physical world that continuously shapes our understanding through firsthand experiences. In stark contrast, LLMs remain fundamentally disembodied, learning purely from written text devoid of any sensory interaction. This lack of a physical presence limits their common-sense knowledge and human-like understanding of spatial and physical reality, making it difficult for them to bridge certain cognitive gaps that humans easily navigate.