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The Intelligence Paradox: Why Smarter AI Needs Different Metrics

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Joseph Byrum

February 4, 2025

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Beyond Artificial Intelligence: Rethinking How We Measure and Enhance Human Cognition

For decades, we’ve measured progress in artificial intelligence by comparing machine performance to human benchmarks. However this approach has led to a paradox that challenges our fundamental understanding of intelligence. While AI can defeat grandmasters at chess and Go, it stumbles over tasks that toddlers master effortlessly – like recognizing everyday objects or exercising basic common sense.

This asymmetry reveals a crucial blind spot in how we think about intelligence. As Hans Moravec noted in 1988, “It is comparatively easy to make computers exhibit adult-level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.” Despite over three decades of technological advancement, this observation remains surprisingly relevant.

The Limits of Traditional Metrics

Why do our traditional metrics of intelligence so often miss the mark? The answer lies partly in how we experience our own thinking. Tasks that require conscious effort – like solving complex math problems – feel more intellectually demanding than activities we perform automatically, like riding a bike or understanding a joke. This subjective experience leads us to overvalue certain cognitive performance types while underestimating everyday intelligence’s complexity.

Consider a simple example: A doctor diagnosing a patient draws on multiple forms of intelligence simultaneously. There’s the explicit knowledge gained from medical training, the pattern recognition developed through years of experience, and the social intelligence needed to communicate effectively with patients. Traditional intelligence metrics might capture the first component but completely miss the others.

The Social Dimension of Intelligence

Recent research reveals that intelligence isn’t just an individual trait – it’s deeply embedded in social networks. Thalia Wheatley’s work at Dartmouth College demonstrates that patterns of brain activity become more similar between people who are more closely connected socially. Friends show especially high levels of neural synchronization when interpreting information, and these effects persist even when viewing new material.

This finding has profound implications for both human and artificial intelligence. Rather than focusing solely on individual cognitive capabilities, we need to consider how intelligence emerges from networks of interaction. Some individuals serve as crucial “brokers,” connecting different social groups and facilitating the flow of diverse perspectives. These brokers develop special skills through their experiences bridging different communities – skills that enhance collective intelligence in ways our traditional metrics don’t capture.

Learning from Nature’s Experiments

Nature provides fascinating examples of how different forms of intelligence can emerge. Research on “liquid brains” – like ant colonies that display intelligent behavior through dynamic interactions shows how sophisticated capabilities can arise from simple components following basic rules. However, there’s often a trade-off: species with larger colonies tend to have less individually intelligent members.

This natural experiment suggests something important about intelligence: Different forms may involve fundamental trade-offs between individual and collective capabilities. Rather than pursuing a single ideal of intelligence, we might need to cultivate diverse and complementary forms of intelligence in both human and artificial systems.

The Role of Evolution and Constraints

Evolution has shaped intelligence through the interplay of constraints and opportunities. Research on evolving artificial neural networks shows that imposing realistic constraints like limitations on processing speed or accuracy can lead to more robust and human-like intelligence. This suggests that the path to better AI might not lie in raw computational power, but in carefully chosen limitations that force more efficient and generalizable solutions to emerge.

Augmenting Human Intelligence

Perhaps the most promising path forward lies not in replacing human intelligence with artificial alternatives, but in creating systems that augment our natural capabilities. AI can be envisioned as a kind of “alien intelligence” that can help us explore possibilities we wouldn’t naturally consider. Rather than having AI systems converge on human-like thinking, we might deliberately tune them to think differently – helping us overcome our cognitive biases and blind spots.

The Netflix Prize competition provides an instructive example. When Netflix offered $1 million to anyone who could improve their recommendation algorithm by 10%, the breakthrough came not from individual efforts but from teams collaborating and combining different approaches. This suggests that major advances often emerge from combining diverse perspectives and capabilities – whether human or machine.

Looking Forward

As we move forward, several key principles should guide our approach to understanding and enhancing intelligence:

  1. Focus on networks rather than individuals. Intelligence emerges from patterns of interaction, whether in brains, societies, or artificial systems.
  2. Embrace diversity in intelligence. Different forms of intelligence involve trade-offs, and we need complementary capabilities rather than a one-size-fits-all approach.
  3. Value constraints. Limitations can drive the evolution of more robust and generalizable forms of intelligence.
  4. Build bridges between human and machine intelligence. The future lies in augmentation and complementarity rather than replacement.

For business leaders, this means rethinking how we organize for collective intelligence – moving beyond individual expertise to focus on patterns of connection and collaboration. For technologists, it suggests new approaches to AI development that embrace constraints and complementarity rather than pure capability. And for society as a whole, it offers a more nuanced and promising vision of how human and machine intelligence might evolve together.

The revolution in intelligence won’t come from machines that think just like humans, but from better understanding and enhancing the diverse forms of intelligence that already exist in nature, society, and technology. By embracing this broader view, we can work toward augmenting human capabilities while preserving what makes human intelligence unique and valuable.

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