A conversation (with my computer) about a computer "thinking"

I asked Chat GPT the following:

I maintain that statements such as "the computer thinks ..." or "the computer sees ..." are useful metaphors. No, the computer doesn't "think," because computers don't think. No, the computer doesn't "see," because computers can't see. I think that although the computer doesn't think or see, the outcome is just the same is if the computer thought or saw. Put this argument concisely to an AI skeptic, who thinks computers are rigidly constrained by what programmers instruct them to do.

The response was not as detailed as I wanted, so I further instructed ChatGPT:

 

Okay, that's a bit more concise than I wanted. Explain more thoroughly to someone who doesn't believe AI is different from classical programming where you instruct the computer what to do at every stage.

 

The response: 

 

Here’s a more thorough version you can use.

 

I understand the view that a computer can only do what it has been programmed to do. That description fits classical programming very well. In traditional software, a human writes explicit step-by-step instructions: “If the email contains this word, mark it spam. If the number is larger than that threshold, do X.” Every decision path is laid out in advance by the programmer. The machine simply follows those instructions. In that world, saying the computer “thinks” or “sees” is just loose talk. 

 

Modern AI systems work differently. We do not hand-write rules for every possible situation. Instead we give the system a general learning method and a large collection of examples. During training the system adjusts millions of internal numbers so that its overall behavior matches the patterns in the data. No programmer sits down and writes “when you see a cat, output ‘cat’” or “when the email looks like this, call it spam.” The system discovers useful regularities on its own. 

 

Because of that learning process, the system can handle situations it was never explicitly told about. An image classifier can correctly label a photo of a breed of dog it has never been shown before, as long as the new photo shares features with ones it has seen. A spam filter can catch a new scam email that uses wording and tricks different from any example in its training set. A language model can answer a question that was never written into its code. The specific competent behavior was not programmed in advance; it emerged from the training. 

 

That is why the metaphors are useful even though they are not literal. When we say “the computer sees the object” or “the computer thinks the message is spam,” we are not claiming it has conscious experience or human-style understanding. We are pointing out that the result is the same as if it had recognized the object or evaluated the message. The practical outcome—correct classification, useful prediction, coherent response—matches what we would get from genuine seeing or thinking. 

 

So the skeptic’s core claim needs updating. Yes, everything a computer does is ultimately calculation following instructions. But in modern AI those instructions are not a rigid list of rules for every case. They are a learning procedure that produces flexible, data-driven behavior. The difference is large enough that treating the system as if it can “see” or “think” is often the most accurate and convenient way to describe what it actually achieves.