Many people still cling to the idea that artificial intelligence is merely a tool in the hands of the developer (this definition might have been acceptable a year or two ago), but the situation is different now. To demonstrate that it's more than just a tool, let's define the concept of a tool.
A tool is an extension of humanity itself; every action it takes stems from human action, and the quality of its results is directly linked to the quality of its use. It will typically malfunction or become less useful if it deviates from its intended purpose.
However, when a tool becomes capable of reframing the problem it solves, changing the direction of the solution itself, or perhaps even redefining the framework, the gap between the user's objective and the nature of the outcome begins to widen. I'm not claiming that AI will be able to act autonomously, but the need for precise guidance is diminishing daily.
Remember GPT-3? How bad it was at writing even the simplest code! It required at least three attempts to get it working. Aside from the training mechanisms and technologies that have evolved, the most crucial thing it lacked was data itself. The internet today is full of tutorials on how to use AI and get the best results, but what we often overlook is that AI itself learns from this knowledge and follows these methods and rules on its own.
For example, in the past, when you asked one model to write a prompt for another, it would often be confused because it didn't understand what those prompts meant or what it meant to instruct a language model. These concepts simply weren't part of its training. The same applies to things AI doesn't know today; it simply hasn't had enough exposure to them.
Therefore, in any course that teaches you how to do things AI can't do, remember that you and AI are, at this moment, classmates.
That's why I see AI today as more than just a tool; it's more like a partner, at least for now.
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