Abhinav Anand
One of the numerous main ideas in the excerpt from the article “To Find Out How Smart
AI Is, First Test Its Common Sense” is that current AI large-language models (LLMs) lack true
common sense. The article defines common sense as boiling down to basic logic and reasoning,
and the author explains why AI models like ChatGPT and Gemini don’t have common sense: an
over-emphasis on “structured knowledge” inferencing and not enough “context-dependent”,
“logic-based” training. The rules that LLMs follow don’t always work for ambiguous situations,
where the answer is obvious to humans but not to AI models. The “cheat day” example in the
text perfectly illustrates why common sense stretches beyond fixed rules that an LLM was
trained on. Guests being on a diet usually indicates that cake should not be served, however
when humans learn that the visitors have cheat days, they can easily adjust their reasoning unlike
AI models which rely solely on probabilities and have no subjectivity or flexibility.
I completely agree with the author’s argument that memorization should not be
intermingled with intelligence. Many people may be impressed that an AI model can answer
difficult trivia questions, but that does not always demonstrate true understanding. It’s almost
analogous to a student memorizing words on a paper without pausing to read between the lines
and truly understand the content. The article’s idea that LLMs can occasionally make simple
reasoning mistakes supports the notion that success on challenging tests can conceal fundamental
flaws.
I think this article highlights an important challenge that we must consider in the future,
which is that an overdependence on AI for everyday tasks may harm society due to its lack of
common sense. If AI overlooks simple, obvious solutions, then placing our full trust in it could
be problematic in fields like healthcare and law. For example, an LLM might recommend a
treatment just because it’s effective, without fully considering its potentially harmful side effects
and questioning its necessity like a real physician would.
My own experience working with AI supports the author’s point. When using an LLM, I
have noticed that it responds quickly and confidently, but at times, overlooks crucial context
clues. For instance, I remember one viral prompt circulating online: “The car wash is 100m away
from my house. Should I walk or drive?” While most AI models correctly answered “drive” as
you need a car to clean it, some AI models, particularly ChatGPT, incorrectly replied “walk” as
the car wash is just 100m away. Although there is some ambiguity since one could be going to
the car wash for reasons other than washing a car, humans can naturally infer the most likely
situation. The AI’s failure to make that conclusion shows its difficulty jumping to everyday
assumptions and its limitations in common sense.
The article argues that AI development should focus not only on instilling knowledge but
also on improving common-sense reasoning. I agree with this perspective because true
intelligence comes from applying knowledge, with flexibility, to real-life situations. By creating
better tests and drawing from fields like psychology, researchers may gain a more accurate
understanding of how close AI is to humans and what challenges still remain.