Thanks for these well-researched examples. I agree completely that Claude’s response is impressive and demonstrates a sophisticated ability to reason through causal relationships based on the vast amount of text it’s trained on, which includes explanations of physics and logical principles.
However, through the blog I am trying to argue that this still operates on a different plane than what a human does. The AI’s ‘causal understanding’ is an emergent property of it having read countless examples of causal reasoning (from textbooks, scientific papers, etc.). It’s essentially a brilliant pattern-matcher for the linguistic representation of causation. A human’s understanding, on the other hand, is built from embodied experience, hands-on experimentation, and the abductive reasoning I mentioned in the post.
So, while the AI can describe the causal relationship flawlessly, could it, for instance, infer a novel, unknown causal relationship from incomplete data in a real-world, messy scenario? That’s where I believe that humans still have a critical edge, and hence it’s the skill at risk of atrophy.
One powerful example is diagnosing a complex, multi-system failure in a large-scale industrial setting, such as a chemical plant or a power grid. A human operator might notice a seemingly unrelated set of events like a subtle temperature fluctuation in one part of the system, a delayed sensor reading in another, and a slight change in the viscosity of a fluid, and, based on years of embodied experience and intuitive pattern recognition, infer a novel, underlying causal chain that an AI wouldn’t detect.
One powerful example is diagnosing a complex, multi-system failure in a large-scale industrial setting, such as a chemical plant or a power grid. A human operator might notice a seemingly unrelated set of events like a subtle temperature fluctuation in one part of the system, a delayed sensor reading in another, and a slight change in the viscosity of a fluid and, based on years of embodied experience and intuitive pattern recognition, infer a novel, underlying causal chain that the current AI models won’t detect. An AI would struggle because:
Incomplete and Messy Data: The data from these systems is often noisy, incomplete, or not logged with the explicit labels an AI would need. A human can interpret a flickering gauge or an unusual sound as a critical data point, something an AI might not be able to easily do.
Abductive, Not Deductive, Reasoning: The operator isn’t working with a clear set of facts to deduce a single answer. They are performing abductive reasoning inferring the most likely explanation from a set of incomplete, messy observations, which requires intuition and experience.
No Existing “Textbook” Answer: The causal relationship might be unique to that specific plant’s design, age, or a recent maintenance change, so there is no existing causal model in its training data to draw from. The human expert is essentially creating a new causal model in real-time.
Thanks for these well-researched examples. I agree completely that Claude’s response is impressive and demonstrates a sophisticated ability to reason through causal relationships based on the vast amount of text it’s trained on, which includes explanations of physics and logical principles.
However, through the blog I am trying to argue that this still operates on a different plane than what a human does. The AI’s ‘causal understanding’ is an emergent property of it having read countless examples of causal reasoning (from textbooks, scientific papers, etc.). It’s essentially a brilliant pattern-matcher for the linguistic representation of causation. A human’s understanding, on the other hand, is built from embodied experience, hands-on experimentation, and the abductive reasoning I mentioned in the post.
So, while the AI can describe the causal relationship flawlessly, could it, for instance, infer a novel, unknown causal relationship from incomplete data in a real-world, messy scenario? That’s where I believe that humans still have a critical edge, and hence it’s the skill at risk of atrophy.
One powerful example is diagnosing a complex, multi-system failure in a large-scale industrial setting, such as a chemical plant or a power grid. A human operator might notice a seemingly unrelated set of events like a subtle temperature fluctuation in one part of the system, a delayed sensor reading in another, and a slight change in the viscosity of a fluid, and, based on years of embodied experience and intuitive pattern recognition, infer a novel, underlying causal chain that an AI wouldn’t detect.
One powerful example is diagnosing a complex, multi-system failure in a large-scale industrial setting, such as a chemical plant or a power grid. A human operator might notice a seemingly unrelated set of events like a subtle temperature fluctuation in one part of the system, a delayed sensor reading in another, and a slight change in the viscosity of a fluid and, based on years of embodied experience and intuitive pattern recognition, infer a novel, underlying causal chain that the current AI models won’t detect. An AI would struggle because:
Incomplete and Messy Data: The data from these systems is often noisy, incomplete, or not logged with the explicit labels an AI would need. A human can interpret a flickering gauge or an unusual sound as a critical data point, something an AI might not be able to easily do.
Abductive, Not Deductive, Reasoning: The operator isn’t working with a clear set of facts to deduce a single answer. They are performing abductive reasoning inferring the most likely explanation from a set of incomplete, messy observations, which requires intuition and experience.
No Existing “Textbook” Answer: The causal relationship might be unique to that specific plant’s design, age, or a recent maintenance change, so there is no existing causal model in its training data to draw from. The human expert is essentially creating a new causal model in real-time.