Conférence de Matthew Battles (Harvard University)

Conférence de Matthew Battles (Harvard University)

🎙 Matthew Battles 👥 318 📅 April 15, 2019 ⏱ 71 min 👁 92 📄 expert opinion 🧭 2026-10-10
Available in: English (current) Français

Keywords

William JamesNabokovCatalogue of Liferecurrent neural networkdark abundance

Summary

In this lecture given at the CRIHN (Centre de recherche sur l’intermédialité historique) in Montreal, Matthew Battles, associate director of metaLAB at Harvard, explores the cultural and technological dimensions of biodiversity, highlighting the concept of ‘dark abundance’—the vast number of species unknown to science. He begins by citing William James’s notion that ’nature is but another name for excess,’ contrasting the historical belief in nature’s inexhaustibility with the current awareness of environmental precarity. Battles traces how taxonomic practices, exemplified by the Catalogue of Life, have catalogued millions of species, yet notes that a large fraction of insects remain undescribed. He discusses Vladimir Nabokov’s work as an entomologist at Harvard’s Museum of Comparative Zoology, including his discovery of the Karner blue butterfly, to illustrate the craft practices of naming and collecting. The talk then shifts to the parallels between the abundance of natural life and the perceived abundance of computational resources, citing David Weinberger’s idea of a ‘post-scarcity’ phase in computing. Battles demonstrates a simple recurrent neural network trained on moth genus names that generates plausible but invented names, which he interprets as echoing the passions and dead languages of past taxonomists. He concludes with a poem by W.S. Merwin, suggesting that these algorithm-generated names are shards of a fading world, and invites reflection on what remains when language and species disappear.

221 words

Critical Evaluation

Value of the Information & Strength of the Argument

The lecture offers a valuable and original perspective by bridging the fields of natural history, cultural history, and computational technology. Battles effectively argues that the practices of taxonomy are deeply embedded in human culture and craft, using concrete examples like Nabokov’s scientific work and the demonstration of a neural network to illuminate how meaning is constructed through naming. His argumentation is solid and nuanced, particularly in his refusal to fully endorse the ideology of machine learning as a natural phenomenon, instead viewing it as a cultural artifact. The use of literary and historical references, from Jules Verne to W.S. Merwin, enriches the discussion and provides a poetic layer that reinforces the central themes. However, the lecture is more reflective than rigorously analytical, with some points relying on poetic associations rather than empirical data, which slightly weakens the overall argumentative force.

Scientific Rigor, Source Quality, Title Accuracy

Battles demonstrates strong intellectual rigor by referencing a range of sources, including William James, David Weinberger, and André Karpathy, and he explicitly notes that he will share a list of resources after the talk. The quality of sources is high, drawing from established academic and public intellectual work. There is no evident fabrication or misrepresentation of facts; the historical and scientific claims are generally accurate. The title ‘Earth Measurer’ directly ties to the theme of taxonomy and to the winter moth’s family name Geometridae, meaning ’earth measurer,’ and the subtitle accurately previews the discussion of biodiversity, affect (emotional resonances), and technology amid mass extinction. The title is well-adapted to the content, and the presentation successfully delivers on its promise. The talk is not a formal scientific study but rather an expert opinion, which is clearly communicated.

291 words

Title / Content Match

The title 'Earth Measurer: Biodiversity, Affect, and Technology in the Midst of Mass Extinction' accurately reflects the content, as the talk explores the intersection of natural history collections, machine learning, and the cultural dimensions of biodiversity loss.

Quality & Reliability

8/10

The speaker is a recognized scholar with a strong publication record in the cultural history of libraries and writing. The lecture is well-argued, referencing a range of historical and contemporary works, and demonstrates a thoughtful integration of theory and practice. However, it is an opinion-based talk rather than a peer-reviewed study, and some interpretations are subjective, which slightly reduces the reliability score.

Key Moments

Cited Sources

Contribution & Novelties

This talk provides a unique interdisciplinary contribution by connecting the cultural history of natural history collections with contemporary machine learning techniques. Battles introduces the concept of ‘dark abundance’—the vast number of undescribed species—and uses a concrete algorithmic demonstration to reflect on how naming and classification are both scientific and poetic acts. The juxtaposition of Nabokov’s entomological practice with neural network outputs offers a novel perspective on the nature of scientific knowledge and its technological mediation.

Pour aller plus loin :

  • Recurrent neural network — Provides a technical overview of the algorithm used in the talk.
  • Biodiversity — Overview of the concept and its significance in the context of mass extinction.
  • Mass extinction — Background on the current biodiversity crisis referenced throughout the lecture.
  • Vladimir Nabokov’s lepidoptery — Details of Nabokov’s scientific contributions, contextualizing the examples in the talk.
  • Linnaean taxonomy — Philosophical and historical framework underlying the taxonomic practices discussed.

150 words

Radar Profile

The radar profile shows high scores in information quality and reliability, reflecting the speaker's expertise and thoughtful presentation, but moderate scores in quantity and technical depth, consistent with a reflective humanities-oriented talk rather than a data-heavy technical lecture.

Reliability 8/10