A.i. is far from flawless.

While studying a course, I presented ChatGPT with a scenario accompanied by four corresponding choices to assign a score to each approach.
The scores ChatGPT was allowed to assign were respectively: a 0, 1, 3, or 5 points.
What ChatGPT did well was explaining the scores for each approach clearly...
However, it provided the following distribution:

Which, of course, is fundamentally wrong at a high level.
I pointed this out and made it clear to ChatGPT, and it claimed to understand...
Next scenario...
ChatGPT then came back with this result:

You see, the entire structure of 0, 1, 3, or 5 points per question is completely gone. Not only that, but it even invented its own scoring system based on something I never asked for.
So, what’s the moral of the story?
AI is fun... it’s a tool, and sometimes it has already helped me solve problems in test automation. In fact, I once asked a question where the answer made it feel like I was talking to someone with an IQ of 160... fantastic!
But it’s nowhere near the point where we can rely on AI completely and blindly accept everything it says.
When you see some of the mistakes ChatGPT makes, you sometimes wonder if it will ever get to such a point...
AI is far from flawless.

great related article: the ChatGPT Voldemort syndrome

-=-=-=-=-=-=-=-=-=-=-=-=- (this is kinda funny if you get it)-=-=-=-=-=-=-
I asked ChatGPT another question, it answered:

The error you describe, where someone incorrectly assigns scores in a scenario-based exam in ICT, can be referred to using the English terms "misallocation error" or "scoring bias." These are specific examples of mistakes stemming from cognitive processes or misinterpretation of the task. Here are some possible causes:
Misinterpretation of Instructions  The candidate misunderstood the instructions, for instance, failing to realize that each score can only be used once.

• English term: Instructional Misunderstanding.

Cognitive Overload  While reading and processing the scenario and options, the candidate becomes overwhelmed, leading to errors in score allocation.

• English term: Cognitive Overload.

Confirmation Bias  The candidate already has a preference or belief about what the correct approach should be, causing them to score subjectively.

• English term: Confirmation Bias.

Inattention to Detail  A lack of focus or care during the scoring process results in incorrect allocation.

• English term: Inattentional Error.

Heuristic Shortcut  Instead of conducting a thorough analysis, the candidate uses a mental shortcut (e.g., "This seems most logical; I'll give it the highest score") without fully considering the context.

• English term: Heuristic-Based Error.

Anchoring Effect  The candidate is influenced by one seemingly strong answer and adjusts the other scores accordingly, leading to incorrect allocation.

• English term: Anchoring Effect.

These errors can arise from a combination of human limitations in information processing and the stress of an exam context.

Think that's nice?

It gets even BETTER:

​If a Large Language Model (LLM) were to make such a mistake, it might be attributed to what could be described as a "context misalignment syndrome" or "instruction misinterpretation syndrome." These terms highlight the underlying causes of the error in terms of how LLMs process information and execute tasks. Here are some possible nuances to this "syndrome":
1. Context Misalignment SyndromeThe LLM fails to properly align its interpretation of the input context (scenario, instructions, or scoring rules) with the expected output.

• Root Cause: Inadequate attention to nuanced details or ambiguities in the instructions.

2. Instruction Misinterpretation SyndromeThe LLM misunderstands or incompletely processes the instructions, leading to an error in fulfilling the task.

• Root Cause: Ambiguity in the input prompt or insufficiently clear task constraints.

3. Cognitive Overfitting SyndromeThe LLM over-relies on patterns from its training data and applies them incorrectly to a specific prompt, resulting in inappropriate scoring or reasoning.

• Root Cause: Overgeneralization based on familiar patterns, rather than reasoning from first principles.

4. Anchoring Bias in ComputationSimilar to the human "anchoring effect," the LLM might assign disproportionate weight to certain key phrases or patterns in the prompt, leading to skewed outputs.

• Root Cause: Overemphasis on initial interpretations of the input.

5. Deterministic Rigidity SyndromeThe LLM rigidly applies rules or algorithms in scenarios where flexibility or deeper reasoning is required, leading to errors in judgment.

• Root Cause: Lack of adaptive reasoning or inability to detect when a different logical pathway is needed.

These "syndromes" are, of course, conceptual, and the actual "mistakes" made by an LLM stem from the probabilistic nature of its training and response generation processes.