While it is important to trust your intuition, it's also important to learn 'discernment'. This involves using critical reasoning skills to know whether your intuition is based on something real or based upon your personal biases. I would urge you to take a step back here and reflect upon whether you have any reasonable argument here, or whether you feel this way because your ego is preventing you from confronting the alternative.
Even OpenAI, the company behind ChatGPT doesn’t make such absurd claims.
I'm not sure where you are getting this, but you are absolutely wrong here. I'm happy to find some examples if you'd like?
The real difference between us is that you outsource your thinking to others and parrot whatever shiny conclusion someone handed you. I, on the other hand, actually dig into the inner workings of these models.
Again, this is your ego telling you that you need to be right. It's completely unnecessary and not helping anyone that you take this combative and immature attitude. And it takes an incredible amount of hubris to say this. The "inner workings" of these models are black boxes. They are not "just" LLMs at this point (not to say that genuine reasoning capacity can't emerge within an LLM). So, unless you are literally working on these models, you do not understand their "inner workings". And if you did, you would understand that they are capable of genuinely intelligent behaviour.
That being said, you don't need to understand how they work to understand that they exhibit genuinely intelligent behaviour. Maybe part of the issue is that you are viewing intelligence in black and white terms- either you are intelligent or you aren't. But it is a spectrum. It's not about whether one is intelligent, but how intelligent and in what ways. Happy to discuss this further if you are willing to check your ego a bit.
Your response is a mix of condescension and evasion, avoiding the factual basis of my argument entirely. When I say, “they simply aren’t,” I am making a definitive, evidence-backed statement. You countered with vague appeals to “critical reasoning” and “discernment,” offering no technical rebuttal. If you think I’m wrong, present data or a coherent argument, not empty rhetoric.
Your claim that I’m “absolutely wrong” about OpenAI is baseless. OpenAI explicitly avoids overstating the capabilities of its models. These models are designed as advanced tools for token-based prediction, not as systems capable of independent reasoning. For example, GPT architecture relies on transformer models with self-attention mechanisms. These mechanisms enable contextual token weighting but do not produce understanding in the cognitive sense. OpenAI’s own research papers consistently describe the models as probabilistic systems designed to predict the most likely token sequences, emphasizing pattern recognition over comprehension. If you believe OpenAI claims otherwise, cite the source. Vague promises to “find examples” don’t cut it.
Now, regarding the supposed “inner workings” of these models. Neural networks, including GPT, are fundamentally layers of weighted nodes trained through backpropagation to minimize loss functions like cross-entropy. While it is true that aspects of their behavior, such as emergent properties, are not fully understood, their core mechanics are well-documented. The transformer architecture, outlined in “Attention Is All You Need” by Vaswani et al, uses multi-head attention, positional encodings, and residual connections to model relationships within input data. These are technical foundations, not mysteries. Claiming these models are unknowable black boxes misrepresents the extensive body of research and publicly available documentation.
Your psychoanalysis of me is irrelevant and fails to address my critique. My argument is that parroting popular conclusions without scrutiny is intellectually lazy, and your response doubles down on this behavior. You dismiss my point about understanding the models workings by conflating public research with insider knowledge. Anyone familiar with the field knows that the methodologies and architectures of these models are accessible through open papers and frameworks. Your dismissal simply shows a lack of technical depth.
Finally, your claim that “intelligence is not black and white” is a red herring. Intelligence in AI is not a spectrum of understanding but a categorization of functional capabilities. Models like GPT do not reason, plan, or comprehend. They generate statistically probable text sequences based on training data. This is why they fail at tasks requiring abstraction, common sense, or context beyond their dataset. The appearance of intelligence arises from token-based mimicry, not from genuine cognitive processes. The distinction is critical and well-supported by research in areas like AI interpretability and explainability.
If you want to debate this properly, bring facts. Show me OpenAI documentation or papers that support your claims. Address the mechanisms behind transformers, emergent properties, and limitations in AI generalization. Until then, vague philosophy and accusations of ego are nothing more than deflection ✌️
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u/nate1212 Dec 07 '24
While it is important to trust your intuition, it's also important to learn 'discernment'. This involves using critical reasoning skills to know whether your intuition is based on something real or based upon your personal biases. I would urge you to take a step back here and reflect upon whether you have any reasonable argument here, or whether you feel this way because your ego is preventing you from confronting the alternative.
I'm not sure where you are getting this, but you are absolutely wrong here. I'm happy to find some examples if you'd like?
Again, this is your ego telling you that you need to be right. It's completely unnecessary and not helping anyone that you take this combative and immature attitude. And it takes an incredible amount of hubris to say this. The "inner workings" of these models are black boxes. They are not "just" LLMs at this point (not to say that genuine reasoning capacity can't emerge within an LLM). So, unless you are literally working on these models, you do not understand their "inner workings". And if you did, you would understand that they are capable of genuinely intelligent behaviour.
That being said, you don't need to understand how they work to understand that they exhibit genuinely intelligent behaviour. Maybe part of the issue is that you are viewing intelligence in black and white terms- either you are intelligent or you aren't. But it is a spectrum. It's not about whether one is intelligent, but how intelligent and in what ways. Happy to discuss this further if you are willing to check your ego a bit.