AI is about the question, not the Answer

There are some in the education industry lobbying for the restriction and outright ban of AI tools in schools. Predictably, the establishment is gaslighting parents everywhere.

It won't matter.  At first, chalk tablets were lamented when paper and pencils entered the one-room schools.  They squawked when calculators hit the market and barely hung on when the world moved from longhand to QWERTY.

ChatGTP is making them lose their minds.  For now.

"AI is about the question, not the Answer..."

The statement "AI is about the question, not the answer" refers to the idea that AI is not just about providing a specific answer to a problem, but rather about understanding the underlying question or task and then providing an appropriate solution.

For example, when an AI system is asked to identify objects in an image, the question is not simply "what objects are in this image?" but also includes a range of other considerations such as:

• What are the possible objects that might be present in the image?
• How can the system be trained to recognize these objects?
• How can it account for variations in lighting, angles, and other factors that might make it difficult to identify the objects?
• How can it incorporate prior knowledge or context to improve its performance?
• What are the potential biases in the data it was trained on?

Therefore, instead of thinking of AI as a tool for providing a single answer, it's more accurate to think of it as a tool for understanding and solving complex problems. To get the best results, one needs to be able to ask the right question and understand the underlying problem and constraints.

That's why critical thinking becomes important, as it helps one to understand and frame the question, evaluate the assumptions and constraints, analyze and interpret the results, and make informed decisions.

How important is critical thinking when working with AI?

Critical thinking is extremely important when working with AI. Developing and deploying AI systems involves making many decisions, including the selection of data sets to train models on, the design of the models themselves, and the interpretation of the results that the models produce. Without critical thinking, it can be easy to make mistakes or introduce bias into the system, which can have serious consequences.

Additionally, the outputs of AI models are not always clear-cut and may require additional analysis to understand the reasoning behind it. This is where human judgment and critical thinking come into play to evaluate the model's decision and apply it in real-world scenarios.

Overall, the ability to think critically is crucial for understanding the limitations and potential biases of AI models, and for effectively using them to make decisions.

Inevitable.  

But we have the edge - digital is never as good/real/flowing/natural as analog.