Can Machines Think?
"I believe that at the end of the century the use of words and general educated opinion will have
altered so much that one will be able to speak of machines thinking without expecting to be
contradicted."
— Alan Turing
In the vast theatre of human inquiry, few questions have reverberated with such persistent
intellectual fascination and existential gravity as the question posed by Alan Turing in 1950:
Can machines think? In an age of neural networks, large language models, and quasi-
autonomous systems, the urgency of grappling with this enigma has never been more
pronounced. But before one can answer whether machines can think, one must first ask: What
does it mean to think?
To posit whether a machine can think is to immediately confront the nebulous and mercurial
nature of the term “thinking.” Is thinking synonymous with reasoning? Is it the ability to feel,
reflect, imagine, or possess intentionality? The English language, in all its poetic ambiguity, does
not offer a crystalline definition. When we say a person is “thinking,” we might mean anything
from solving a quadratic equation to daydreaming about a lost love. Therefore, to inquire if a
machine can think is to ask not one question, but many.
René Descartes, the progenitor of modern Western philosophy, grounded human existence in
the act of thinking: Cogito, ergo sum—I think, therefore I am. But Descartes was also quick to
dismiss the possibility that non-human entities—particularly animals and automata—could
possess minds. He drew a rigid boundary between the res cogitans (thinking thing) and the res
extensa (extended, material thing). By that standard, machines are mere artifacts, devoid of soul,
self-awareness, or sapience.
Yet such distinctions are increasingly problematic in the face of machines that can beat
grandmasters at chess, compose symphonies in the style of Mozart, or convincingly simulate
human dialogue. Are these merely clever imitations, or are we witnessing the incipient stages of
artificial cognition?
, Alan Turing, in his seminal paper “Computing Machinery and Intelligence,” sidestepped the
metaphysical intricacies of defining thought and proposed instead a pragmatic test: if a machine
could engage in a textual conversation indistinguishable from that of a human, it might as well
be said to “think.” This became known as the Turing Test, and it remains one of the most elegant
and provocative heuristics in the field of artificial intelligence.
However, critics have pointed out the limitations of Turing’s operationalism. The philosopher
John Searle, for instance, introduced the famous “Chinese Room” argument, wherein a non-
Chinese speaker follows syntactic rules to produce Chinese responses without understanding a
word of the language. The room passes the Turing Test, yet there is no true understanding—no
semantics, only syntax. Therefore, Searle concludes, passing the Turing Test is insufficient to
claim genuine thinking. While I admire Turing’s argument personally find the “Chinese Room”
example more convincing because it captures how machines can simulate understanding without
really having it.
This distinction between simulation and cognition is crucial. A parrot may mimic human speech,
but it does not understand the semantics of what it says. Similarly, This made me wonder that is
a machine that generates human-like responses truly thinking, or merely reflecting the statistical
patterns embedded in its training data? In the same way “a machine might process information
like a vending machine – it responds to input but it does not know what a snack is or why
am I hungry”.
Contemporary AI systems—especially those employing deep learning—are based on neural
networks inspired, albeit loosely, by the architecture of the human brain. These systems can
recognize images, generate human-like text, drive cars, and even engage in elementary forms of
creativity. But do these functions constitute thinking?
Some argue that intelligence is substrate-independent: if cognition is nothing more than
computation, then it does not matter whether it arises from biological neurons or silicon circuits.
When I asked AI to write a poem for my friend, it sounded emotional-but I knew it didn’t feel
anything. This contrast fascinated me .
Others, however, maintain that consciousness and genuine understanding are inextricably tied to
biological embodiment. The philosopher Hubert Dreyfus, drawing from phenomenology,
argued that human intelligence arises not from rule-following, but from an embodied experience
"I believe that at the end of the century the use of words and general educated opinion will have
altered so much that one will be able to speak of machines thinking without expecting to be
contradicted."
— Alan Turing
In the vast theatre of human inquiry, few questions have reverberated with such persistent
intellectual fascination and existential gravity as the question posed by Alan Turing in 1950:
Can machines think? In an age of neural networks, large language models, and quasi-
autonomous systems, the urgency of grappling with this enigma has never been more
pronounced. But before one can answer whether machines can think, one must first ask: What
does it mean to think?
To posit whether a machine can think is to immediately confront the nebulous and mercurial
nature of the term “thinking.” Is thinking synonymous with reasoning? Is it the ability to feel,
reflect, imagine, or possess intentionality? The English language, in all its poetic ambiguity, does
not offer a crystalline definition. When we say a person is “thinking,” we might mean anything
from solving a quadratic equation to daydreaming about a lost love. Therefore, to inquire if a
machine can think is to ask not one question, but many.
René Descartes, the progenitor of modern Western philosophy, grounded human existence in
the act of thinking: Cogito, ergo sum—I think, therefore I am. But Descartes was also quick to
dismiss the possibility that non-human entities—particularly animals and automata—could
possess minds. He drew a rigid boundary between the res cogitans (thinking thing) and the res
extensa (extended, material thing). By that standard, machines are mere artifacts, devoid of soul,
self-awareness, or sapience.
Yet such distinctions are increasingly problematic in the face of machines that can beat
grandmasters at chess, compose symphonies in the style of Mozart, or convincingly simulate
human dialogue. Are these merely clever imitations, or are we witnessing the incipient stages of
artificial cognition?
, Alan Turing, in his seminal paper “Computing Machinery and Intelligence,” sidestepped the
metaphysical intricacies of defining thought and proposed instead a pragmatic test: if a machine
could engage in a textual conversation indistinguishable from that of a human, it might as well
be said to “think.” This became known as the Turing Test, and it remains one of the most elegant
and provocative heuristics in the field of artificial intelligence.
However, critics have pointed out the limitations of Turing’s operationalism. The philosopher
John Searle, for instance, introduced the famous “Chinese Room” argument, wherein a non-
Chinese speaker follows syntactic rules to produce Chinese responses without understanding a
word of the language. The room passes the Turing Test, yet there is no true understanding—no
semantics, only syntax. Therefore, Searle concludes, passing the Turing Test is insufficient to
claim genuine thinking. While I admire Turing’s argument personally find the “Chinese Room”
example more convincing because it captures how machines can simulate understanding without
really having it.
This distinction between simulation and cognition is crucial. A parrot may mimic human speech,
but it does not understand the semantics of what it says. Similarly, This made me wonder that is
a machine that generates human-like responses truly thinking, or merely reflecting the statistical
patterns embedded in its training data? In the same way “a machine might process information
like a vending machine – it responds to input but it does not know what a snack is or why
am I hungry”.
Contemporary AI systems—especially those employing deep learning—are based on neural
networks inspired, albeit loosely, by the architecture of the human brain. These systems can
recognize images, generate human-like text, drive cars, and even engage in elementary forms of
creativity. But do these functions constitute thinking?
Some argue that intelligence is substrate-independent: if cognition is nothing more than
computation, then it does not matter whether it arises from biological neurons or silicon circuits.
When I asked AI to write a poem for my friend, it sounded emotional-but I knew it didn’t feel
anything. This contrast fascinated me .
Others, however, maintain that consciousness and genuine understanding are inextricably tied to
biological embodiment. The philosopher Hubert Dreyfus, drawing from phenomenology,
argued that human intelligence arises not from rule-following, but from an embodied experience
