What Bateman's View Misses

Let's dive deeper into the additional layers of nuance that Bateman's posts don't fully address, particularly around the role of AI as a co-creator, the emergent properties of LLMs, and their potential for augmenting human tasks in specific domains.

1. The Role of AI in Co-Creating Meaning with Humans

Bateman’s view largely treats AI as a passive or external entity that generates outputs which humans then interpret, but it misses the possibility of co-creation of meaning. This can be seen as a more dynamic, collaborative interaction between the AI and the human user.

Co-Creation of Meaning:

• Meaning in Interaction: As noted before, in Systemic Functional Linguistics (SFL), meaning is not just the result of a solitary act of communication. Instead, it is co-constructed through interaction. In the case of LLMs, this interaction is dynamic—when humans prompt the AI and provide feedback, the output is shaped by both the human's input and the AI's generative process. In this sense, meaning doesn't just “come from” the AI, nor is it solely the product of human cognition. Instead, it emerges from the interaction between the two agents.

• A Tool for Human Augmentation, Not Replacement: AI outputs often stimulate new ways of thinking, question assumptions, and offer perspectives humans might not have come up with on their own. The process of "dialogue" with AI can lead to richer meaning-making when humans actively interpret, critique, and modify the AI-generated content. This collaborative approach allows for the augmentation of human creativity, expanding what’s possible in areas such as writing, scientific discovery, or even emotional expression.

• Example in Creative Writing: An author might use an LLM to generate a first draft or an opening paragraph for a story. The LLM doesn’t write the full narrative in the way a human author would, but its output offers a springboard for the human writer to jump off, critique, and evolve into something uniquely human. The author shapes and interprets the AI’s contribution, creating meaning together rather than having the AI provide meaning that must be passively accepted.

Bateman’s Omission:

• Misses the Role of Active Human Engagement: Bateman’s posts imply that AI outputs are static and meaningless unless human intention is behind them. However, in the co-creation process, human engagement is what provides the AI output with its meaning. The meaning is therefore not just in the AI’s output, but in how it is used and interacted with by the human.

2. Emergent Properties of LLMs

Bateman’s posts focus a lot on the potential failures or limitations of LLMs, especially regarding factual accuracy or intentionality. However, emergence—a core property of these systems—does not fully come through in his critique. LLMs exhibit complex behaviours that arise from the interaction of many simpler rules and processes, and these behaviours are not always easily predictable or reducible to a set of human-like cognitive actions.

What is Emergence in LLMs?

• Emergent Complexity: LLMs work by drawing on vast datasets and finding patterns in language use. When trained on vast amounts of textual data, these models can generate outputs that seem highly complex and contextually rich, even though they lack an underlying "understanding" of the content. The meaning or value that emerges from an LLM’s output is the result of these hidden processes working in tandem with the user’s input.

• Unpredictable Results: The nature of these models is such that, in some instances, the generated output might appear more insightful or coherent than expected, while in other cases, it might produce nonsensical text. This variability is part of the emergent process—the same prompt can lead to different results based on the complexity of the statistical models and the data they are drawing from. The model doesn’t "intend" these results, but they still emerge from the way the model is designed and the training data it has been exposed to.

• Relevance in Context: Bateman’s focus on the AI’s “lack of meaning” overlooks how LLMs can create meaning dynamically in context. The emergent properties in their generation process can result in highly relevant, creative, or novel combinations of ideas that users can engage with meaningfully, even if those combinations don’t come from a place of human-like intentionality.

Bateman’s Oversight:

• Underestimates the Value of Emergence: Bateman emphasizes that AI is “not like humans” and “doesn’t understand meaning.” While this is true, it overlooks the fact that the value of LLMs lies in their ability to generate emergent content that can often exceed simple human input. His analysis misses the fact that emergent meaning comes from a complex blend of factors (such as training data, algorithms, and user input), and that this complexity allows for creativity and problem-solving in ways that are distinct from human cognition.

3. AI as a Tool for Augmenting Human Tasks

Bateman discusses the limitations of LLMs in terms of their lack of intentionality and factuality, but there is less attention paid to the extensive ways in which LLMs can augment human capabilities across a variety of domains.

AI’s Augmentation in Specific Domains:

• Creative Industries: In fields like art, writing, and music, LLMs and other AI systems are already being used as tools to augment creativity. AI can generate new ideas, offer alternative viewpoints, or provide initial drafts that spark further human creativity. This is an area where the emergent properties of AI are highly beneficial.

• Research and Discovery: AI models are being used to sift through vast amounts of data in fields like genomics, chemistry, and social sciences, speeding up research and making connections that humans might overlook. In this way, AI acts as a powerful tool for researchers to explore new hypotheses or conduct data analysis at scales that would be impossible for individuals alone.

• Everyday Productivity: In more practical applications, AI tools help with tasks like writing code, summarizing texts, creating content for marketing, or even generating product names. These are examples of AI augmenting human tasks, freeing up time for more complex thinking, decision-making, or creative work.

Bateman’s Narrow Framing:

• Misses AI's Utility in Specific Contexts: Bateman’s critique doesn’t fully engage with the practical applications of AI, especially in areas where its purpose is to enhance human work rather than replace it. His posts imply that AI should behave more like a human thinker, which creates a narrow view of its value. AI doesn’t need to “think” like a human to be useful. It can still significantly enhance human productivity by automating repetitive tasks or generating ideas that would take humans much longer to arrive at.

• Fails to Consider Collaboration Potential: Bateman focuses heavily on AI's failures without recognizing the possibilities for collaboration between human and machine. As a collaborator, AI is valuable not just as a source of raw material but as a dynamic partner that can evolve alongside humans as they refine their ideas.

4. AI and the Misconception of Human-Like Intentionality

A final nuance lies in Bateman’s focus on the human-like qualities of AI. He seems to lean heavily on the assumption that AI must show signs of human-like intentionality to be meaningful, useful, or interesting. But this assumption overlooks how AI can function differently and still contribute meaningfully.

Rethinking Meaning and Intentionality:

• Meaning is Contextual: As we’ve discussed earlier, meaning in communication is not solely dependent on human intentionality. In AI, meaning is often a result of interaction and context, with humans interpreting and co-creating meaning from the AI’s output. AI doesn’t need to have human-like intentionality to create useful meaning in context. The meaning is the product of the user's engagement and the way the AI is used.

• Reframing Intentionality: AI doesn’t need to “understand” like humans do to provide value in creative, analytical, or functional tasks. It doesn’t need to be conscious or intentional. It only needs to function as a tool that supports humans in their processes, with the interaction producing meaning that is useful, even if it is not “human” in origin.

Conclusion

These additional layers of nuance show that Bateman’s posts primarily focus on AI’s limitations without acknowledging its potential as a co-creative tool or its emergent properties that produce dynamic, useful, and even surprising outputs. AI can augment human creativity, productivity, and problem-solving by offering new ideas, automating tasks, and interacting with human input in ways that extend human capabilities, rather than merely replicating human cognition. Bateman's analysis could be expanded by considering AI as a partner in the co-creation of meaning, rather than focusing solely on its failures to mimic human thought.