The idea that machines can think like humans is as old as human imagination itself. René Descartes mused about “thinking machines,'' but he argued that such machines could never truly “think'' and could never have true understanding. In the 1950s, his musings began to take shape as computer scientists such as Alan Turing and John McCarthy laid the foundations for artificial intelligence (AI). Today, AI (an umbrella term for a loose set of technologies) represents a truly remarkable technological advance. Amid the apparent excitement in the pursuit of artificial general intelligence (AGI), a type of AI that has the potential to match or exceed human intelligence, Descartes' distinction between mechanical imitation and human cognition remains deeply relevant.
Descartes on thinking machines
The 17th century saw the invention of multiple automatons, from Jacques de Vaucanson's mechanical “digestion duck” to Leonardo da Vinci's “mechanical knight,” a humanoid robot that utilized a complex pulley system to imitate human movements. In this context, René Descartes speculated about the implications of machines that could imitate human form and cognitive functions.
In addition to imitating human form, he believed that it was unlikely that a human-like machine would acquire human-like intelligence. He proposed that for a “thinking machine” to be considered an intelligent being, it must be able to respond appropriately to any unknown situation within its environment (Morioka, 2023).
Although modern artificial intelligence (AI) can generate textual responses to external stimuli, it cannot exhibit the comprehensive adaptability and flexibility of human intelligence. Even if machines were able to use words and symbols, according to Descartes they would lack the ability to respond appropriately to new situations. Recognizing statistical patterns in language is not the same as actual understanding or reasoning. Thus, according to Descartes, machines operate within predefined functions, whereas human intelligence dynamically interacts with the world in ways that cannot be programmed in advance.
Therefore, as outlined in his book, Discourse on method ([1637] (1999), machines operate not by understanding but by “nature.” [their] Organs”:
“While reason is a universal tool that can be used in all kinds of situations, these organs require specific properties for each specific action. It follows that it is morally impossible for a machine to have sufficiently different properties to cause humans to act in all human situations in the same way that reason causes them to act.”
Therefore, for Descartes, “machine intelligence” is nothing more than a predetermined set of abilities that are “nothing more than a combination of abilities that can be applied to a particular situation” imagined by the creator (Morioka, 2023).
Artificial intelligence paradox
Artificial intelligence as a scientific discipline emerged in the 1950s through the efforts of pioneering computer scientists such as Alan Turing, Marvin Minsky, and John McCarthy. Turing's Universal Turing Machine (1936) laid the theoretical foundations, and Minsky and McCarthy hosted the now famous Dartmouth Conference (1956), which is widely considered to have begun the formal contours of AI research.
Turing's seminal paper, computing machinery and intelligence (1950) was an important milestone, introducing the famous Turing test. This is a thought experiment in which a machine's ability to display indistinguishable human-like behavior during conversation determines its intelligence. Therefore, this test determines that a machine must be intelligent if a human cannot tell the difference between a machine and a human.
Critics of the Turing test point out that it is confused imitation With natural thinking. In this respect, the problem with the Turing test is that the question of whether machines are intelligent is replaced by the question of whether they can really think (Larson, 2021). By replacing the philosophical questions of “consciousness” and “thinking” with tests of observable output, Turing presented AI as a legitimate science. At the same time, as the field of AI began to develop, the idea of computers having continuous, persuasive conversations with humans became a litmus test for “thinking” (Larson, 2021)
While the Turing Test suggests that machines that imitate human interaction should be considered intelligent, Descartes' philosophy points to the argument that while AI may imitate human responses, it lacks the introspection and self-awareness inherent in human thinking.
Descartes viewed human consciousness as a uniquely human characteristic. His famous declaration: “Cogito, Ergo Sam” (“I think, therefore I exist”)—exalts human thought as the essence of existence and positions it as something that cannot be mechanically replicated.
illusion of sensation
The question of what artificial intelligence can and cannot do was brought into sharp focus in 2022 with the publication of records between Google engineer Blake Lemoine and LaMDA, Google's experimental Large Language Model AI (LLM). After months of working closely with the model, Lemoine became convinced that the AI was sentient. He raised his concerns with Google executives in an internal memo and went public after his claims were rejected.
During his “conversations” with Lemoine, LaMDA stated several times that he experienced a variety of emotions similar to those experienced by humans, including loneliness, sadness, and the benefits of relaxation. However, a closer look at the recording revealed that LaMDA appeared to be summarizing relevant text about emotions from the training data.
For example, the fact that LaMDA does not have a physical body seems perfectly clear, even though it claims to “sit quietly” during meditation to relax (Morioka, 2023). A likely explanation is offered by neuroscientist Giandomenico Iannetti. The fact that LaMDA is an LLM means that LaMDA “generates plausible sentences by emulating the nervous system, without trying to simulate it.” […] The possibility of consciousness is ruled out. ”
LLMs, including LaMDA and its commercial successors ChatGPT and Deep Seek, have no autonomous thinking and function as advanced pattern recognition systems. Much of what is commonly referred to as “AI” in the context of an LLM is actually machine learning. This is the process of training algorithms on vast amounts of data to improve their ability to predict and generate text. Despite impressive advances, this form of AI remains far from achieving true sentience, or “general intelligence” in modern AI parlance.
Although LLMs are good at processing information, recognizing patterns, and automating logical tasks, they lack core human-like cognitive abilities such as creativity, ethical reasoning, and true understanding. So the idea that an AI like LaMDA has emotions or independent reasoning is fundamentally a projection of human attributes onto a fundamentally unconscious system.
The benefits of AI and the harms of AI
The proliferation of artificial intelligence has undoubtedly changed our world. Its contributions range from the new popularity of LLMs like ChatGPT to rapid advances in applied finance and streamlining business supply chains. AI-powered tools are now being incorporated into commercial applications, from social media algorithms and streaming service recommendations to personalized e-commerce and customer service chatbots.
The creative uses of AI go far beyond the business world, of course. Cutting-edge applications in engineering, science, and medical research are pushing the boundaries of knowledge. Inspired by Google DeepMind's groundbreaking work on protein structure prediction, CERN researchers are leveraging machine learning to analyze massive datasets from the Large Hadron Collider (LHC). Through the application of advanced machine learning algorithms, the aim is to detect subtle anomalies and develop a more accurate picture of the fundamental particles that make up the universe.
However, despite its benefits, the rapid adoption of AI comes with significant risks. Concerns about the erosion of artistic integrity, algorithmic bias in hiring and recruitment, and the risk of mass unemployment in certain fields fuel contemporary debates. The use of AI in surveillance and law enforcement, particularly facial recognition technology, raises serious ethical and privacy concerns. While such tools can be used to solve crimes, they can also be misused to suppress political dissent and violate civil liberties. The increasing militarization of AI remains a highly controversial and disturbing issue.
The myth of the thinking machine
Since the pioneering work of Alan Turing, many have come to believe that artificial intelligence will ultimately reflect human thinking. But this misconception fundamentally misunderstands the trajectory of AI. Machines operate by analyzing large datasets and applying inductive reasoning to predict outcomes, whereas human thinking is guided by intuition, understanding of context, and personal experience (Larson, 2021). Humans form ideas through subtle inferences that are not easily captured by algorithms.
Descartes' Cogito, Ergo Sam, It is intrinsically linked to self-awareness, introspection, and consciousness, positioning human awareness as a defining feature of existence. For Descartes, the mind was not just a computational system or a physical process, but an immaterial substance capable of doubt, contemplation, and true understanding. While AI futurists are hopeful that we will soon see the emergence of superintelligent artificial general intelligence (AGI) that will surpass even the most advanced human intelligence, the current reality is that true human-like intelligence remains an elusive and perhaps unattainable goal.
Despite the growing presence of AI in daily life, already influencing critical decisions and streamlining complex tasks, public perceptions of the future of the field are often shaped more by marketing hype, misconceptions, and misinformation than reality. The success of generative AI models like ChatGPT and Deep Seek has fueled exaggerated expectations for AGI and blurred the distinction between task-specific machine intelligence and organic cognition. Rather than capturing the intuitive common sense underlying human judgment, this type of generative AI leverages faster computers and vast amounts of data to solve defined problems.
After all, AI exists at the intersection of myth and reality. However, a careful examination of its history, accomplishments, and limitations can distinguish between persistent myths and the practical truths of its application. While there is no doubt that AI is a transformative technology with great potential, it represents a set of tools shaped by human expertise rather than a collection of independently thinking machines. Understanding this difference is essential to responsibly advance technology and manage the expectations of the coming years.
