Go Nomadism, Evolutionary Computation and Natural Selection: A Reply to Jay Lampert
Bibliographic record
Abstract
In response to a 2023 article by Jay Lampert in Deleuze and Guattari Studies, this paper develops the question of what Deleuze and Guattari might make of AlphaGo, the artificial intelligence developed by Google which defeated one of the top human Go players in 2016. It approaches the question in a way that supplements and complements Lampert’s analysis, by noting the well-worn analogy between computer programming and evolutionary biology and then cross-referencing it with Deleuze and Guattari’s attitude towards the latter in A Thousand Plateaus. The results reinforce Lampert’s conclusion that AlphaGo is not ‘nomadic’, but they show that this is precisely because AlphaGo’s algorithms are reminiscent of Darwin’s mechanism of natural selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".