There is a standard story told about the rise and fall of ordinary language philosophy: it was a widespread, if not dominant, approach to philosophy in Great Britain in the aftermath of World War II up until the early 1960s, but with the development of systematic approaches to the study of language—formal semantic theories on one hand and Gricean pragmatics on the other—ordinary language philosophy more or less disappeared. In this paper we present quantitative evidence to evaluate the standard story (...) of the rise and fall of ordinary language philosophy, building on the topic model of over 30,000 philosophy articles in Weatherson (2022). Using a combination of qualitative judgment and a topic-model-based measurement of similarity between individual articles, we find evidence that supports the first part of the standard story, according to which ordinary language philosophy arises in the 1940s, peaks between the early 1950s and the late 1960s, and then rapidly declines. But we argue that there is also evidence of a "new wave" of ordinary language philosophy in the early 21st century that defies the second part of the standard story. (shrink)

Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely studied and critically evaluated, often with an emphasis on ethical concerns. In this article, I analyze three canonical unsupervised learning problems: clustering, abstraction, and generative modeling. I argue that these methods raise unique epistemological and (...) ontological questions, providing data-driven tools for discovering natural kinds and distinguishing essence from contingency. This analysis goes some way toward filling the lacuna in contemporary philosophical discourse on unsupervised learning, as well as bringing conceptual unity to a heterogeneous field more often described by what it isnot(i.e., supervised or reinforcement learning) than by what itis. I submit that unsupervised learning is not just a legitimate subject of philosophical inquiry but perhaps the most fundamental branch of all AI. However, an uncritical overreliance on unsupervised methods poses major epistemic and ethical risks. I conclude by advocating for a pragmatic, error-statistical approach that embraces the opportunities and mitigates the challenges posed by this powerful class of algorithms. (shrink)

Topic model is a well proven tool to investigate the semantic content of textual corpora. Yet corpora sometimes include texts in several languages, making it impossible to apply language-specific computational approaches over their entire content. This is the problem we encountered when setting to analyze a philosophy of science corpus spanning over eight decades and including original articles in Dutch, German and French, on top of a large majority of articles in English. To circumvent this multilingual problem, we use machine-translation (...) tools to bulk translate non-English documents into English. Though largely imperfect, especially syntactically, these translations nevertheless provide correctly translated terms and preserve the semantic proximity of documents with respect to one another. To assess the quality of this translation step, we develop a “semantic topology preservation test” that relies on estimating the extent to which document-to-document distances have been preserved during translation. We then conduct an LDA topic-model analysis over the entire corpus of translated and English original texts, and compare it to a topic-model done over the English original texts only. We thereby identify the specific contribution of the translated texts. These studies reveal a more complete picture of main topics that can found in the philosophy of science literature, especially during the early days of the discipline when numerous articles were published in languages other than English. (shrink)

Formal epistemology is a growing field of philosophical research. It is also evolving, with the subject matter of formal epistemology papers changing considerably over the past two decades. To quantify the ways in which formal epistemology is changing, I generate a stochastic block topic model of the abstracts of papers classified by PhilPapers.org as pertaining to formal epistemology. This model identifies fourteen salient topics of formal epistemology abstracts at a first level of abstraction, and four topics at a second level (...) of abstraction. I then study diachronic trends in the degree to which formal epistemology abstracts written in a given year are likely to contain words associated with a particular topic, beginning in 2000 and continuing to 2020. My findings suggest that there has been a marked decline in the likelihood of a given formal epistemology abstract being about logical approaches to belief revision. On the other hand, over the past two decades, the salience of probabilistic techniques in formal epistemology has increased, as has the salience of work at the intersection of formal epistemology and some areas of philosophy of science. (shrink)