Interview
Noam Chomsky: Deep Learning is Useful but It Doesn't Tell You Anything about Human Language
- Deep learning is characterized by its opacity, making it difficult to formulate definitive theorems regarding its capabilities and limits.
- The core mechanism of deep learning involves identifying patterns within massive datasets rather than deriving scientific understanding.
- Classification of Utility
- Deep learning is deemed effective as an engineering tool for practical applications (e.g., Google Translate).
- It is distinguished from science because it does not elucidate the fundamental nature of the subject matter (e.g., human language).
- Critique of Methodology
- Scientific inquiry prioritizes "critical experiments" designed to test specific theoretical questions, whereas deep learning relies on the sheer volume of unguided data.
- Success metrics for deep learning focus on performance on existing corpora (e.g., the Wall Street Journal) rather than performance on data that violates system rules.
- Case Study: Structure Dependence
- Neural networks may perform well on languages relying on "linear proximity," a mode that violates actual human linguistic structure.
- This performance is characterized as a scientific failure because it suggests the model fails to discover the true nature of the system, successfully approximating incorrect patterns.
- Relationship to Behavioral Science
- Claims by some researchers that deep learning vindicates Skinnerian behaviorism (Terry Sejnowski) are rejected; the technology does not validate behavioral theories.
- Potential for Scientific Insight
- Neural networks may occasionally reveal previously unnoticed patterns within complex structures.
- Analogy to Other Fields
- This approach is compared to "corpus linguistics" and "paleoanthropology," where researchers must derive conclusions from available records when critical experiments are impossible.
- While these are considered serious studies, they are inherently limited compared to methodologies allowing for direct, critical experimentation with living subjects.