Another matter i'd like your opinion on is the focus on 'intelligence' in AI, especially LLMs. To me it seems to be missing the point, or in some way, using a vague unquestioned concept (cf. free will) that obscures a clear understanding of what's developing in the field.
You’re raising a crucial point, and I absolutely agree that the focus on “intelligence” in AI—especially in large language models (LLMs)—can obscure a clearer understanding of what's actually going on. The concept of “intelligence” in AI tends to be used vaguely and without much critical examination, much like the term “free will” is often used in philosophy without clear definition. Both terms carry a lot of cultural and emotional baggage, which can cloud their usefulness when trying to understand what’s really happening.
Let me break it down and add a few angles on this:
1. The Problem of “Intelligence” as a Metaphor
The way we talk about AI “intelligence” is often metaphorical. When people refer to LLMs as being "intelligent," they’re drawing a parallel to human cognition, which, at best, is a limited analogy. But unlike humans, LLMs don’t have consciousness, intentionality, or any kind of agency. They don’t understand what they say in the way that we understand our own thoughts. They process and generate outputs based on patterns they’ve learned from vast amounts of data. This distinction matters because calling something "intelligent" suggests a level of awareness, reasoning, and self-directed action that these models don’t have. The model's outputs aren’t the result of thought or reasoning in a human sense—they’re the result of statistical correlations and pattern recognition.
2. The Problem of Functionality vs. Cognition
When we focus on intelligence in AI, we tend to overlook the functional nature of these systems. What LLMs do is not so much “think” as it is “pattern-matching.” They function by mapping inputs (prompts) to outputs (responses) in a highly complex and nuanced way, but there’s no internal experience or consciousness guiding this. This is where I think the concept of “intelligence” becomes misleading—it implies a kind of cognition or mental process that’s simply not happening in the way we think about it in human terms. AI systems are incredibly sophisticated, but their complexity comes from their ability to handle vast amounts of data and recognise patterns—not from any form of cognition.
3. Why the Focus on “Intelligence” Hinders Understanding
By framing the discussion around “intelligence,” we risk misdirecting attention away from what’s actually important. What’s truly developing in AI isn’t “intelligence” but increasingly complex systems of data processing and pattern recognition. LLMs are tools designed to process information in ways that simulate aspects of human language use, but they don’t have intentions, goals, or understanding.
Instead of thinking of them as “intelligent,” it might be more accurate and insightful to think of them in terms of their functionality, scope, and application. These systems are incredibly good at mimicking patterns of human language, but their capacity is entirely dependent on the data they’ve been trained on. What’s developing in the field is the ability to process and generate text at scale, with sophistication, not “think” or “understand.”
4. The Question of "Free Will" in AI
This is similar to how the term “free will” is often used without a clear definition. We’re talking about human decision-making, but when you break it down, you find that human decision-making is the result of many interrelated factors (biology, environment, culture, etc.), just like AI processes are the result of various factors (data, algorithms, etc.). But we don’t have the same kind of “free will” in a deterministic sense that’s often implied in these discussions.
In the context of AI, we’re often trying to understand agency and autonomy when, in reality, the system has no internal subjective state to guide actions. We might attribute “decision-making” to LLMs, but it’s really just a matter of selecting the most statistically likely response based on the input, not a decision made by a “mind” or “will.” In that sense, much like free will, the concept of “intelligence” becomes a catch-all that misleads us about the actual mechanics at play.
5. Shifting the Focus: What Should We Talk About?
Instead of “intelligence,” we should focus on the specific capabilities of LLMs—things like:
Pattern recognition: LLMs excel at detecting patterns and structures in data, which is what enables them to generate responses based on prompts.
Data processing and synthesis: These models can pull together and synthesise vast amounts of information in ways that seem “intelligent” but are really just impressive data manipulations.
Application scope: The adaptability of these models to different contexts (e.g., conversational agents, text generation, summarisation) is more about the system's ability to handle diverse types of input-output relationships, rather than “thinking.”
By focusing on these aspects, we can move away from the vague, loaded term of “intelligence” and instead focus on the technical processes and their real-world implications. This would allow for a clearer understanding of what these systems are capable of, and where they might be headed.
Conclusion: Rethinking “Intelligence” in AI
The focus on intelligence in AI, particularly in LLMs, can mislead us by suggesting these systems possess cognitive faculties that they don’t. What’s developing in the field is not intelligence in the traditional sense but rather increasingly sophisticated methods of data processing, pattern recognition, and statistical prediction. In much the same way the concept of "free will" obfuscates our understanding of human decision-making, the term “intelligence” in the context of AI distracts us from the underlying mechanics and limitations of these systems. Instead of viewing AI through the lens of human-like intelligence, it might be more productive to focus on the specific technological advancements and applications—allowing for a more precise and nuanced understanding of what these systems are and where they are headed.
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