I asked ChatGPT to give me an IQ test. It got one of the answers wrong

Understanding the Limitations of AI in Reasoning: Lessons from a Misstep in IQ-Style Testing

In the realm of artificial intelligence and natural language understanding, even sophisticated models like ChatGPT can encounter challenges when tasked with reasoning-intensive questions. A recent informal experiment highlights this point vividly, demonstrating both the strengths and limitations of AI systems in handling diverse cognitive tasks.

The Test: A Multi-Domain Reasoning Exercise

A human posed a series of six questions to ChatGPT, designed to assess various reasoning skills:

1. Pattern Recognition: Determining the next number in a sequence.
2. Logical Deduction: Drawing conclusions from premises about flowers and roses.
3. Verbal Analogy: Understanding relationships between objects, such as glove to hand and foot to ?
4. Mental Math: Computing the cost of multiple pencils given unit prices.
5. Short-Term Memory: Remembering and reversing a sequence of characters.
6. Lateral Thinking: Solving a classic water jug measurement problem.

The AI’s Responses and Performance

The AI successfully answered five out of six questions correctly: it identified the pattern sequence number as 42, determined that you cannot definitively conclude roses fade quickly, identified the correct analogy (shoe), calculated the price ($5.00), and described the jug method effectively. The only misstep was in reversing the sequence of characters, where it initially provided an incorrect answer but corrected it upon further reflection.

A Deeper Dive into Question #2: Logical Reasoning and Test Conventions

The second question, a logical deduction about flowers and roses, revealed interesting nuances. It asked whether, from the premises “All roses are flowers” and “Some flowers fade quickly,” we could conclude “Some roses fade quickly.” The answer hinges on understanding logical entailment versus factual certainty.

The correct inference—taking a standard logic perspective—is that the conclusion does not necessarily follow from the premises. The premises do not specify that roses are among the flowers that fade quickly; they only state that some flowers fade quickly. Therefore, the proper answer, adhering to formal logic principles, is “No”—the conclusion cannot be validly derived.

However, the initial evaluation conflated truth-value considerations (“Can we tell if roses fade quickly?”) with logical validity, leading to some confusion. Clarifying these distinctions emphasizes a critical point: AI systems rely on interpreting question semantics accurately. When questions involve assessing logical validity versus factual truth, precise wording and understanding are essential