In an age of increasingly sophisticated artificial intelligence, what can an 18th-century Scottish philosopher tell us about its fundamental limitations? How we acquire knowledge through experience rather than pure reason. David Hume’s analysis of how modern AI systems learn from data rather than explicit rules shows an interesting parallel.
In his groundbreaking work, On Human Nature, Hume argued that “all knowledge degenerates into probability.” This statement was revolutionary for its time, challenging the prevailing Cartesian paradigm that certain knowledge could be achieved through pure reason. Hume’s empiricism went further than his contemporaries in emphasizing how knowledge about factual matters (as opposed to relations of ideas, such as mathematics) depends on experience. there was.
This perspective provides parallels with the nature of modern artificial intelligence, particularly large-scale language models and deep learning systems. Consider the phenomenon of AI “hallucinations,” where a model produces information that it is confident in but is actually inaccurate. These are not just technical glitches, but reflect fundamental aspects of how neural networks, like human cognition, operate probabilistically rather than deterministically. When GPT-4 or Claude generates text, it samples from a probability distribution learned from training data, rather than accessing a database of specific facts.
This similarity deepens when we consider the architecture of modern AI systems. Neural networks learn by adjusting weights and biases based on statistical patterns in the training data, essentially creating a probabilistic model of the relationship between inputs and outputs. This has some similarities to Hume’s explanation of how humans learn cause and effect through repeated experience rather than logical deduction, although the specific mechanisms are very different.
These philosophical insights have practical implications for the development and deployment of AI. As these systems become increasingly integrated into critical areas, from medical diagnosis to financial decision-making, understanding their probabilistic nature becomes important. Just as Hume warned against exaggerating the certainty of human knowledge, we must be careful about ascribing inappropriate levels of trust to the output of AI.
Current research on AI coordination and safety reflects these Humean considerations. Efforts to develop neural network uncertainty quantification techniques that allow systems to express confidence in their outputs are consistent with Hume’s emphasis on probability analysis and the role of experience in belief formation. The goal of AI interpretability research is to understand how neural networks arrive at their outputs by examining their internal mechanisms and the effects of training.
The generalization challenge in AI systems—performing well on training data but failing in new situations—is similar to Hume’s famous induction problem. Just as Hume questioned the logical validity of extending past patterns to predict the future, AI researchers are working to ensure robust generalization beyond training distributions. The development of few-shot learning (in which an AI system learns from a minimal number of examples) and transfer learning (in which knowledge from one task is applied to another) offers technical approaches to this central challenge of generalization. represents. While Hume identified the logical problem of justifying inductive reasoning, AI researchers face the specific engineering challenge of building systems that can reliably generalize beyond training data.
Hume’s skepticism about causality and analysis of the limits of human knowledge remain important when analyzing the capabilities of AI. Large-scale language models can produce sophisticated output that appears to demonstrate understanding, but they are essentially pattern-matching systems trained on text, and are designed to provide statistical correlation rather than causal understanding. Works based on relationships. This is consistent with Hume’s insight that even human knowledge of cause and effect is based on observed patterns.
As we continue to advance our AI capabilities, Hume’s philosophical framework remains important. This is a reminder to approach AI-generated information with a skeptical attitude and to design systems that are aware of its probabilistic underpinnings. It also suggests that even if we invest more money and energy into our models, we may soon reach the limits of AI. As we understand, intelligence may have limits. The set of data that can be provided to an LLM can quickly be exhausted if it is limited to human-written text. If your biggest concern is the existential threat posed by AI, this may sound like good news. But if you expect AI to contribute to economic development for decades to come, it might help to think about an 18th century philosopher. Hume’s analysis of human knowledge and its dependence on experience rather than pure reason helps us think about the limitations inherent in artificial intelligence.
Related links
My hallucination article – https://journals.sagepub.com/doi/10.1177/05694345231218454
Russ Roberts on AI – https://www.econtalk.org/eliezer-yudkowsky-on-the-dangers-of-ai/
Cowen of Dwarkesh – https://www.dwarkeshpatel.com/p/tyler-cowen-3
Freedom Fund Blog on AI
Joy Buchanan is an associate professor of quantitative analysis and economics at Samford University’s Brock School of Business. She is also a frequent contributor to our sister site, AdamSmithWorks.
