Post 1 (Nov 28):
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"The problem with AI, as I see it, is that they are… fundamentally incapable of providing us with anything meaningful."
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Inaccuracy: Bateman dismisses the meaningfulness of AI outputs, but this is problematic because meaning is a flexible, context-dependent construct. While LLMs don’t generate meaning in the same sense humans do (since they aren’t conscious), they still generate tokens that are meaningful within specific contexts. So, this blanket statement ignores the complexity of meaning production in LLMs.
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"They're taking vast swathes of data, mixing it all together, and then spewing out random token-sequences without any actual understanding."
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Inaccuracy: While it's true that LLMs use vast amounts of data and rely on patterns, they're not simply spitting out random sequences. They generate responses based on statistical associations, with internal mechanisms (like attention layers) governing which tokens are more likely to follow others. The output isn’t random but based on probabilities and past training.
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"We must remember that, despite how convincing these models can be, they are ultimately nothing more than sophisticated search engines..."
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Inaccuracy: This oversimplifies the nature of LLMs. Search engines look up pre-existing data and return it. LLMs, however, generate novel responses based on their training data. They're not merely retrieving information but synthesising it based on learned patterns, which distinguishes them from search engines. Bateman’s comparison here is misleading and fails to capture the generative nature of LLMs.
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Post 2 (Feb 15):
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"All of these generative processes are essentially just ‘statistical’ manipulations of data, with no conceptual or cognitive depth whatsoever."
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Inaccuracy: This statement ignores the rich structure LLMs create through deep learning. While LLMs are not conscious and don’t have cognition in the human sense, they exhibit a form of emergent structure based on the large amounts of data they've processed. This includes syntactical patterns, semantic structures, and the ability to generate coherent text, which goes beyond basic statistical manipulation.
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"To make it simpler, think of it like a huge statistical machine that operates entirely according to probabilities."
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Inaccuracy: This reduces the complexity of LLMs. While probabilities are a key part of their function, LLMs involve intricate mechanisms like transformers, attention mechanisms, and training on vast corpora of data. Calling them "statistical machines" ignores how these mechanisms enable LLMs to generate creative and contextually relevant outputs, which aren't mere probability calculations.
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"It is one thing to say that these models produce meaningful-sounding sequences of words. But they do not actually ‘understand’ the meaning of those words."
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Inaccuracy: Bateman is correct that LLMs don't understand meaning in the human sense, but this statement could be interpreted as ignoring the nuanced way LLMs engage with meaning. They instantiate meaning from data based on the contexts they’ve been trained on. Saying they don't produce "meaningful-sounding sequences" is misleading, as the text they generate can often be highly relevant, even if it's not conscious meaning.
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Post 4 (Feb 16):
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"There is only so much one can say without knowledge, although even without knowledge both people and LLMs can produce inordinately long sequences of linguistic-seeming tokens!"
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Inaccuracy: This could be interpreted as implying that LLMs produce text without any structure or purpose. While LLMs generate linguistic sequences, those sequences are governed by probabilistic structures and not purely random. The idea that they're "linguistic-seeming" but meaningless is inaccurate because they often maintain coherence and relevance, depending on the prompt.
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"ChatGPT is not ChatGPT is not ChatGPT."
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Inaccuracy: This seems to refer to different versions or settings of ChatGPT, but it’s unclear what Bateman means by this statement. It’s true that different versions of ChatGPT (or models with different parameters) might perform differently, but it’s misleading to suggest that ChatGPT, in a broader sense, isn’t consistent. The underlying mechanisms remain the same; it’s the training data and tuning that differ.
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"Without this, one can literally do nothing with the result."
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Inaccuracy: Bateman is implying that without knowledge of how an LLM operates (model, parameters, prompts), you can’t interpret or make use of its output. While understanding the technical background can help interpret LLM output more deeply, it’s not true that one can do “nothing” with the result. People can engage with LLM outputs meaningfully without knowing the exact settings of the model—context and application often matter more.
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Post 5 (Feb 17):
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"The AI tool has exposed nothing, and even to suggest it has is deeply problematic."
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Self-Contradiction/Inaccuracy: Bateman claims that the AI tool hasn’t exposed anything, yet throughout his responses, he’s detailing how ChatGPT operates and offering analysis on its outputs. By his own admission, he's interacting with the model and reflecting on its role, which contradicts the claim that it has "exposed nothing."
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"The kinds of textuality that they are pushed to produce... require attention, and even scientific investigation."
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Inaccuracy: While LLM outputs are worthy of analysis, Bateman's implication here seems to suggest that the outputs themselves are inherently flawed or lack value, which dismisses the potential utility of LLMs in generating insight. The outputs are shaped by the data and the model’s architecture, not some intrinsic flaw in the AI itself.
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"It would be impressive if some of the expertise on this list could add insight to dealing with these challenges as discourse is meant to be what we are good at...""
Self-Contradiction: Bateman is advocating for discourse about the limitations of AI, yet he seems to dismiss the value of such discourse by undermining the AI’s outputs as non-meaningful. This is a contradiction: if discourse is meant to deal with challenges, he should be engaging with the outputs in a way that acknowledges their potential contributions, rather than dismissing them outright.
Summary of Issues:
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Over-simplification of LLMs: Bateman repeatedly oversimplifies how LLMs operate (e.g., calling them "statistical machines" or "search engines"), which misrepresents their complexity and the ways they generate outputs.
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Self-Contradictions: Bateman criticises the AI for not producing meaning, yet in other parts of his emails, he acknowledges that the outputs can be useful, pointing to a contradiction in his stance.
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