Negussie Tilahun, Ph.D.
June 29, 2026
Artificial intelligence, with no consciousness, emotions, or understanding of its own existence, diagnoses diseases, solves complex mathematical problems, and writes algorithms that tackle the perplexities of quantum mechanics. For far too long, we have held the view that only conscious beings can tackle complex problems. But we are witnessing the emergence of a new frontier, and old barriers are being shattered at an alarming rate. How is this possible? Recently, I reviewed four books that made significant contributions to our understanding of intelligence and the human mind [for a more detailed review, visit https://ihitmconsultancy.org/]. One central theme that the authors agree upon is that the human brain is perhaps the most complex structure known to exist. It has developed the capacity to learn, adapt, reason, communicate, and cooperate through millions of years of evolution. This extraordinary organ has enabled humans to dominate the planet, although the human species is physically inferior to many other species in strength, speed, and natural defenses. Bennett’s (2023) work in this area is fascinating. Contrary to popular belief, he argued through meticulous research and systematic analysis that intelligence does not require a brain, since intelligent species emerged millions of years before they developed a brain. Nevertheless, understanding how intelligence emerged has fascinated philosophers, biologists, psychologists, economists, and, more recently, computer scientists. Their efforts have produced different explanations of learning, decision making, consciousness, and behavior. Although these perspectives differ, together they provide a useful framework for understanding artificial intelligence and its implications for healthcare and society.
The Evolution of Intelligence
Bennett (2023), traces intelligence across billions of years of biological evolution. He argues that intelligence did not emerge suddenly but evolved through a series of major cognitive breakthroughs. The first breakthrough was navigation and memory, when simple organisms learned to respond to environmental cues and remember successful actions. The second was reinforcement learning, which emerged with early vertebrates and enabled behavior to be shaped by rewards and punishments. The third breakthrough occurred with mammals, which developed the ability to simulate possible futures and evaluate alternative courses of action before acting. The fourth breakthrough appeared among primates through mentalizing—the ability to understand the intentions and beliefs of others. Finally, humans introduced symbolic language, allowing the transmission of complex ideas across individuals, groups, and generations. Bennett argues that language represents the decisive step that separates humans from all other species. Through language, humans can share knowledge, coordinate activities among large groups, transmit culture, and accumulate learning across generations. Civilization itself rests on this foundation.
How the Brain Learns and Makes Decisions
Kahneman (2011) approaches intelligence from a different perspective. He argues that human thinking operates through two systems.
System 1 is fast, automatic, intuitive, and largely unconscious. It enables people to respond rapidly to familiar situations without deliberate analysis. System 2 is slow, deliberate, analytical, and effortful. It is activated when people solve complex problems, evaluate evidence, or make careful decisions. Most human behavior is governed by System 1. While efficient, this system is vulnerable to numerous cognitive biases. Anchoring bias causes people to rely heavily on the first information they encounter. Availability bias leads individuals to judge events based on how easily examples come to mind. Loss aversion causes losses to feel more painful than equivalent gains feel rewarding. The planning fallacy leads people to underestimate the time and resources required to complete a task.
These observations formed the basis of Prospect Theory, which challenged the traditional economic assumption that humans are rational decision makers. Kahneman demonstrated that people routinely make decisions that depart from strict logic and economic self-interest.
Kahneman’s insight is important because intelligence is not simply the ability to reason. As he notes, intelligence also involves the ability to retrieve relevant information from memory and apply it appropriately when needed.
Free Will, Consciousness, and Human Behavior
Sapolsky (2023) offers an even more fundamental challenge to conventional thinking. He argues that human behavior is the product of biological, environmental, cultural, and historical forces that operate long before conscious decisions are made.
Sapolsky rejects simple explanations of behavior. Human actions cannot be understood solely through genetics, brain chemistry, childhood experiences, social influences, or culture. Instead, behavior emerges from the interaction of all these factors. Genes operate within environments. Neurons function within vast networks of neurons. Culture influences biology, while biology simultaneously influences culture. From this perspective, intelligence itself is not an isolated characteristic, but part of a larger system shaped by evolution, experience, and circumstance. Human behavior is simply too complex to be reduced to a single cause. Sapolsky’s argument raises important questions about free will and responsibility. If our actions are shaped by factors beyond conscious control, then intelligence may be less about autonomous choice and more about how biological systems process information and respond to their environment.
Consciousness and the Challenge of Artificial Intelligence
Harari (2017) extends the discussion by examining the relationship between intelligence and consciousness. He argues that the mind functions much like an information-processing algorithm. Through evolution, humans developed consciousness as a mechanism for interpreting information, making decisions, and navigating complex social environments. Yet Harari acknowledges that much of human behavior operates unconsciously. Hunger, fear, loyalty, attraction, and countless other motivations emerge without conscious deliberation. The precise purpose and origin of consciousness therefore remain unresolved.
The emergence of artificial intelligence complicates the issue further. Machines increasingly perform tasks once thought to require consciousness. They can recognize images, interpret language, diagnose disease, play chess, and generate sophisticated responses. If machines can perform these functions without consciousness, then consciousness alone cannot explain intelligence.
Harari argues that modern technology has effectively decoupled intelligence from consciousness. For centuries, it was assumed that intelligence required subjective experience. Artificial intelligence challenges that assumption.
Biological Intelligence vs. Artificial Intelligence
Artificial intelligence did not emerge in isolation. Many of the core concepts of artificial intelligence are inspired by the biological brain. A great deal of artificial neural networks takes their cue from biological neurons and processes information through interconnected networks. The human brain learns through repeated exposure to experience. Similarly, machine learning systems study patterns through large datasets. Despite these similarities, important differences remain. Human learning is shaped by emotion, culture, memory, social interaction, and lived experience. Machine learning depends on data, computational power, and algorithms. Humans often understand context and meaning. Machines excel at recognizing patterns and generating predictions. This distinction highlights the difference between prediction and understanding. An artificial intelligence system may accurately predict the likelihood of disease, equipment failure, or hospital readmission without understanding why those events occur. Human experts, by contrast, can explain causal relationships. Transparency, accountability, and human oversight therefore remain essential.
Harari (2017) discusses the future of artificial intelligence in detail and stipulates a future in which artificial intelligence rules the world and humans are reduced to lower or inferior beings and treated like animals, the way we now treat animals. Bennett (2023) professes an opposite point of view. He contends that the human mind is too complicated, with intricate processing models whose neurons establish connections with other neurons while simultaneously disconnecting from millions of other neurons in a millisecond as the brain makes day-to-day decisions. About 86 billion neurons act in an infinitesimal number of possible ways with infinitesimal possibilities in a millisecond. He argues that artificial intelligence will not cope with the human mind unless it processes information the way the brain does. Thus, the future of artificial intelligence is unlikely to be a replacement for human intelligence. Rather, it will function as an extension of human capability. The most successful systems will combine machine efficiency with human judgment, at least for now.
Artificial Intelligence, Healthcare, and the Future
Harari’s (2017) prediction that artificial intelligence will create a large class of economically useless people remains highly controversial. There is growing evidence that jobs are not eliminated by artificial intelligence itself but by people who know how to use artificial intelligence more effectively than others. The challenge, therefore, is not resisting technological change but adapting to it. In this sense, artificial intelligence readiness is not primarily a technical problem. It is a leadership challenge. The individuals and organizations that thrive in the age of artificial intelligence will not necessarily be those with the most advanced technology. They will be those that learn, adapt, and innovate most effectively.
The same principle applies to healthcare. Hospitals that successfully integrate artificial intelligence into clinical practice, administration, disease surveillance, and decision support are likely to outperform those that do not. The issue is not whether artificial intelligence will enter healthcare but how effectively healthcare organizations prepare for its arrival. This observation is particularly relevant for developing countries. The future of healthcare may depend less on acquiring advanced technology and more on developing leadership, governance structures, workforce capabilities, and organizational readiness to use that technology effectively. In this sense, artificial intelligence readiness is not primarily a technical problem. It is a leadership challenge. The organizations that thrive in the age of artificial intelligence will not necessarily be those with the most advanced technology. They will be those that learn, adapt, and innovate most effectively.
References
Bennett, M. S. (2023). A brief history of intelligence: Evolution, AI, and the five breakthroughs that made our brains. Harper.
Harari, Y. N. (2017). Homo Deus: A brief history of tomorrow. Harper.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Sapolsky, R. M. (2023). Determined: A science of life without free will. Penguin Press.