Michael Tamir and Elay Shech embark on an ambitious journey to explore the notion of understanding in the context of deep learning algorithms. They attempt to ascertain if the impressive achievements of deep learning, particularly in areas where these algorithms can compete with human performance, can be construed as an indication of genuine understanding. To this end, the authors delve into the philosophy of understanding and seek to establish criteria for evaluating machine understanding. This is done in the hope of determining whether the trends and patterns exhibited by deep learning algorithms in representation and information compression can be equated with partial or full satisfaction of these criteria.
The authors identify three key factors in understanding: reliability and robustness, information relevance, and well-structured representation. They argue that these factors can be observed in deep learning algorithms, providing a basis for evaluating their presence in direct task performance or in analyzing the representations learned within the neural networks’ hidden layers. In order to assess understanding in machines, the authors draw upon various concepts and techniques from the realm of deep learning, such as generalization error, information bottleneck analysis, and the notion of disentanglement.
The authors consider and address three possible objections to their arguments. First, they acknowledge the narrow scope of deep learning models’ success in achieving human competitive performance, as well as their limitations in other tasks. However, they contend that their goal is to provide a framework for evaluating potential machine understanding, rather than claiming that any specific algorithm exhibits a certain degree of understanding. Second, they respond to concerns about the constitutive nature of the three key factors by emphasizing that their work serves as a critical tool for quantifying and comparing evidence of understanding, rather than making conclusive judgments. Finally, the authors address the objection that understanding requires a “mentality,” highlighting the value of an aspect-sensitive account of machine understanding that is independent of presupposed mentality.
When it comes to broader philosophical issues, the paper contributes to ongoing debates surrounding the nature of understanding in both human and artificial agents. The authors draw connections between deep learning algorithms and philosophical accounts of understanding, showing how concepts from the latter can be utilized to develop evaluation criteria for the former. By doing so, they provide a valuable philosophical framework for approaching the topic of machine understanding, allowing for a more nuanced analysis of the similarities and differences between human and machine cognition.
The paper’s findings open up several avenues for future research and investigation. One possible direction is to delve deeper into the interplay between the key factors of understanding, exploring how they might be combined or weighted to better assess the relative strengths and weaknesses of various deep learning algorithms. Another promising area for exploration is the application of the authors’ framework to other types of artificial intelligence, such as reinforcement learning or unsupervised learning. Additionally, examining the potential impact of advancements in AI hardware, neural network architectures, or training methodologies on the key factors of understanding could further enrich our understanding of the relationship between deep learning and the philosophy of understanding.
Abstract
Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for identifying these factors in deep learning representations provides a framework for discussing and critically evaluating potential machine understanding given the continually improving task performance enabled by such algorithms.
Machine understanding and deep learning representation