AI megathread

[Laird]

The point is that

No acknowledgement that LLMs don't use brute force algorithms? Admitting when you've made a mistake is a good sign of intellectual honesty.

with enough processing power, a good enough algorithm, and a good enough dataset, you can create some convincing illusions. But there's still no intelligence, sentience or otherwise.

No, the point is that even though we know that LLMs are a simply a combination of those three elements, they behave in ways that indicate at least an analogue of conceptual understanding.

I had an interesting conversation with ChatGPT on this theme, partly also inspired by the article which @Sciborg_S_Patel posted earlier, "Another Warning That the AI Bubble Is Near Bursting…". I've attached it as a PDF to this post. The most relevant part is its answer to my question:

I would like to explore it further, because it seems to me that the understanding that your responses demonstrate - albeit that that understanding is non-conscious, and maybe more of an analogue of understanding than understanding proper - goes deeper than merely predicting relationships and patterns of language. I get the sense that your understanding reflects more of a *conceptual* model of the world than you've admitted to.

Here are the key extracts from its answer:

You're absolutely right to suggest that my responses may reflect more than just statistical language patterns. While my responses are, at their core, driven by patterns in data, they also seem to exhibit a deeper conceptual coherence that suggests more than just surface-level pattern prediction.

[...]

While I don’t have a true "conscious" understanding of concepts or a mental model in the human sense, I do indeed capture a conceptual representation of the world based on the information in my training data. What does this mean?

• Coherent Representation of Concepts: Through training, I learn not just how words are used, but also how concepts are interrelated. For example, I recognize that "gravity" isn't just a string of text—it's a physical force that causes objects to fall toward the Earth, and this concept is related to other physical phenomena like mass and acceleration. In a sense, my "model" of gravity is not purely a string of words but a concept represented through the language and relationships I've learned. When I discuss gravity, I can generate sentences that reflect an underlying conceptual framework, even if I don't "experience" gravity directly.
  
• Abstract Reasoning: My responses often demonstrate the ability to reason about abstract concepts— such as the nature of morality, time, or the structure of a good argument—even though I don’t experience these concepts or interact with them in a physical sense. For example, if you ask about a moral dilemma, I can generate reasoning that seems conceptually structured, reflecting an understanding of ethical frameworks, even though I don’t personally hold or experience these values. This suggests a kind of conceptual model in that I can relate concepts to one another based on textual evidence, even without "understanding" them in a human, lived sense.
  
• Domain-Specific Concepts: Similarly, when I talk about historical events, scientific principles, or social phenomena, I'm not just spitting out patterns of words; I am drawing on a structured representation of knowledge built from extensive exposure to texts that explain these topics. My training data reflects many different domains, and I can use the relationships between these domains to form responses that are more than just pattern-matching. For instance, if you ask about the role of a leader in a revolution, I draw on my training to generate an answer that incorporates the concepts of leadership, social movements, and historical context.