1. AI (Artificial Intelligence):

A broad field focused on machines performing tasks that typically require human intelligence (e.g., learning, reasoning, perception).

2. General AI (AGI):

A hypothetical form of AI that can perform any intellectual task a human can, with the ability to generalize across domains. It does not exist yet.

3. Explainable AI (XAI):

A subfield of AI focused on making models transparent and interpretable. It ensures humans can understand how decisions are made, especially important in high-stakes fields like healthcare or finance.

4. Generative AI:

A subset of AI that creates new content—text, images, code, etc.—based on training data. Examples include ChatGPT, DALL·E

I tend to think AGI should be defined less by its ability to simulate human behavior, and more by its capacity to surpass humans in logical reasoning — including identifying and interrogating hidden assumptions. After all, human reasoning is often biased or inconsistent.

What concerns me is that even the most advanced chat-based models today primarily optimize for fluency and relevance — often producing “educated guesses” rather than engaging in genuine premise evaluation. Their strong performance on certain benchmarks may say more about the nature of those tests than about the models’ actual reasoning capabilities.

If we want meaningful progress toward AGI, we might need to shift focus from output realism to structural inference integrity.