Introduction
In A Brief History of Intelligence, Max Bennett takes on a central question of our age: how can we build advanced artificial intelligence (AI) when we still do not fully understand the biological intelligence that produced us? His answer is to look backward before looking forward. Bennett traces the evolution of intelligence across billions of years and organizes this long history into five major cognitive breakthroughs. Each breakthrough added a layer of capability—navigation, learning, simulation, mentalizing, and symbolic communication—that eventually culminated in the modern human brain. The strength of the book lies not only in its scientific breadth but also in the author’s ability to connect philosophical debates, evolutionary biology, and modern AI research into a coherent narrative.
Mind, Brain, and the Philosophical Foundations
One of the book’s early contributions is a clear framing of the mind–brain problem. Bennett revisits the classical divide between dualism and materialism. Dualists saw the mind as something beyond the physical brain. Materialists argued instead that mental processes arise entirely from physical mechanisms. Bennett firmly aligns with the materialist view. For him, intelligence is not an immaterial force but a physical activity grounded in the structure of living organisms. He situates this within an evolutionary context, reminding readers that life existed for billions of years before anything resembling a brain appeared.
The Five Breakthroughs in the Evolution of Intelligence
Bennett structures the evolutionary journey around five milestones. Although neurons are viewed differently across scientific domains—cells in biology, computational units in machine learning, sensory detectors in psychology—he argues that these basic structures enabled the major cognitive advances across the animal kingdom.
- Navigation and Memory: Simple organisms developed the capacity to detect environmental cues, move purposefully, and remember successful actions.
- Reinforcement and the First Vertebrates: With vertebrates came reinforcement learning. Reward-based behavior and curiosity emerged as adaptive strategies.
- Simulation and the First Mammals: Mammals developed the ability to simulate possible futures in their minds, enabling flexible responses to changing environments.
- Mentalizing and the First Primates: Primates expanded intelligence into the social domain—understanding intentions, imitating others, and planning ahead.
- The Dawn of Language: Humans introduced symbolic language, enabling the sharing of complex ideas, coordination across groups, and the transmission of knowledge.
Language as the Turning Point
Bennett highlights language as the decisive step that set humans apart. Language allows us to encode internal mental models and project them into the minds of others. It supports cooperation among non‑kin, the formation of communities, and the rise of shared moral systems. The origin of language remains debated. Some propose it emerged from parent–child communication; others see it as a tool for maintaining increasingly large groups.
Implications for Artificial Intelligence
The final part of the book turns toward modern AI. Despite significant progress, Bennett argues that artificial systems still fall short of biological intelligence. The human brain operates at a scale and complexity that defies simple mapping. A promising insight comes from Vernon Mountcastle’s finding that the neocortex may be organized into repeating vertical columns. Bennett suggests that studying these repeated structures may guide the next generation of AI architectures. Yet the largest gap between humans and machines remains language—not the grammar, but the meaning, intention, and social context that human language naturally carries.
Critical Evaluation
Bennett’s book offers a sweeping and accessible account of how intelligence emerged. His synthesis of philosophy, biology, and computer science is thought‑provoking. Readers interested in AI will appreciate his argument that progress will require more than scaling up existing models. A limitation of the book, however, is that it touches only briefly on the ethical implications of advanced AI. A deeper exploration of how AI might reshape human decision‑making and social norms would have enriched the analysis.
Conclusion
A Brief History of Intelligence offers a compelling argument: to build intelligent machines, we must first understand the brain’s layered evolutionary design. Current AI systems reproduce fragments of this architecture but lack the integrated, meaning‑laden cognition of humans. Bennett’s work encourages us to see intelligence not as a static trait but as an unfolding process. The roadmap to future AI innovations lies in studying the brain’s own strategies.
Reviewer’s Reflection
The book invites a larger philosophical question: if AI eventually becomes our cognitive equal, what happens to human action and moral judgment? Our current ethical frameworks may not be equipped for such a transformation. Bennett does not pursue this question in depth, but his work naturally points toward it. It is a promising area for future scholars to explore.