The intricacies of the human mind can be classified into two broad cognitive behaviors: passive and active thinking. By default, our brains tend to gravitate towards the former - passive thinking. It's a residual effect of our education system, which primarily reinforces this form of cognitive activity.
So, what is passive thinking? It's a thought process that's automatic, requires little conscious control, and largely depends on existing knowledge and assumptions. On the other hand, active thinking is a deliberate and conscious thought process that involves questioning, analyzing, and cross-verifying information.
While our default cognitive approach is often satisfactory for many tasks, it falls short when utilizing Artificial Intelligence (AI) tools, especially given their current capabilities and limitations. Consider the example of AI language models like ChatGPT. They are powerful assistants, capable of generating meaningful outputs based on the input prompts. However, their outputs are entirely dependent on the training they've received and do not verify the correctness or existence of external factors mentioned in prompts.
For instance, if I asked ChatGPT to use a specific framework (let's call it 'Framework X') to rewrite a piece of code, it would happily oblige, even if 'Framework X' was a proprietary entity, non-public, or even non-existent. The problem arises when we accept the generated code as accurate without employing active thinking. Debugging the resultant code or rectifying the misunderstanding with ChatGPT could drain significant time and effort - all of which could have been saved with some proactive verification.
So, how do we shift from passive to active thinking while using AI tools?
Here are a few actionable tips to foster "active thinking" in AI use-cases:
1) Cross-Verification: If you're dealing with unfamiliar tools, concepts, or frameworks, consider cross-verifying their existence or validity through a Google search or similar methods before integrating them into your AI interactions.
2) Questioning Outputs: When unsure about the output generated by AI, don't hesitate to cross-check the results using external resources or by running them past peers or experts.
3) Direct Inquiries: Querying AI tools like ChatGPT about the correctness or existence of an entity can be helpful. However, bear in mind that the response's accuracy will be contingent upon the AI's training data and limitations.
4) Prior Learning: Invest time in acquiring basic knowledge about the subject at hand before employing AI tools for tasks related to that domain. A sound understanding will allow you to effectively guide the AI and assess its outputs.
In conclusion, for maximizing the effectiveness of AI tools, it's crucial that we move beyond passive acceptance of their outputs and adopt an active thinking mindset. This approach not only helps avoid pitfalls but also ensures that we truly harness the potential of these advanced technologies.
