Nature vs. Nurture: The Role of Environmental Resources in Evolutionary\n Deep Intelligence
Why this work is in the frame
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Bibliographic record
Abstract
Evolutionary deep intelligence synthesizes highly efficient deep neural\nnetworks architectures over successive generations. Inspired by the nature\nversus nurture debate, we propose a study to examine the role of external\nfactors on the network synthesis process by varying the availability of\nsimulated environmental resources. Experimental results were obtained for\nnetworks synthesized via asexual evolutionary synthesis (1-parent) and sexual\nevolutionary synthesis (2-parent, 3-parent, and 5-parent) using a 10% subset of\nthe MNIST dataset. Results show that a lower environmental factor model\nresulted in a more gradual loss in performance accuracy and decrease in storage\nsize. This potentially allows significantly reduced storage size with minimal\nto no drop in performance accuracy, and the best networks were synthesized\nusing the lowest environmental factor models.\n
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it