How to simulate feelings and instinct in computers

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The discussion centers on the portrayal of AI systems as capable of simulating human feelings and instincts, raising concerns about the implications of using such language. Participants argue that while AI can mimic human-like responses through statistical learning, it does not genuinely experience emotions or intuition. The use of terms like "gut instinct" in AI descriptions is seen as misleading, potentially causing misunderstandings about the technology's capabilities. There's a call for responsibility in how AI is described, especially given its impact on individuals with mental health issues. Ultimately, the distinction between human emotional experience and AI behavior remains a critical point of contention.
  • #31
FactChecker said:
I think that AI has gone well beyond that. ChatGPT can now take the requirements for a computer program controlling simple devices and generate a fairly good program along with instructions for wiring the device. The code does benefit from some tweaking by a human.
See this.
You are missing the point.
Generating code for programs is a prime example of something that can be made by training the software in relative word frequence, sentence construction and so on. As long as there is a huge amount of programming code in the training data, chances are youll get something useful, maybe in need of minor corrections. My opening post was about an example of AI for optimizing computer code. To test conceptual understanding, I argue that the software has to reach outside of the training data, to exclud mimicry. For example, train the software on data where prime searching algorthims (and similar programs) has been excluded. If the software has a conceptual understanding of the code it writes it should be able to make decent attempts on such programs without being trained on it. Without the use of stochastical processes.
Just as we expect from out high school students.
 
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  • #32
Kontilera said:
You are missing the point.
Generating code for programs is a prime example of something that can be made by training the software in relative word frequence, sentence construction and so on. As long as there is a huge amount of programming code in the training data, chances are youll get something useful, maybe in need of minor corrections. My opening post was about an example of AI for optimizing computer code.To test conceptual understanding, I argue that the software has to reach outside of the training data, to exclud mimicry. For example, train the software on data where prime searching algorthims (and similar programs) has been excluded. If the software has a conceptual understanding of the code it writes it should be able to make decent attempts on such programs without being trained on it. Without the use of stochastical processes.
Just as we expect from out high school students.
You seem to think that combining the concepts of "steam" and "engine" would take a large leap of imagination beyond their individual properties. Maybe so, but I am not so sure.
 
  • #33
FactChecker said:
You seem to think that combining the concepts of "steam" and "engine" would take a large leap of imagination beyond their individual properties. Maybe so, but I am not so sure.
I argue that in order to combine the concepts of steam and engine and thus sketch the idea of a steam engine youll need a conceptual understanding of the two words. That is, if you've never heard of the concept steam engine before.

I order to prevent the illusion of understanding by mimicing, we subtract the data describing the concept (or anything too similar) in the training set.
 

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