{"id":"W2786183742","doi":"10.1109/crv.2018.00058","title":"Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Nature versus nurture; MNIST database; Modern evolutionary synthesis; Artificial neural network; Computer science; Process (computing); Artificial intelligence; Evolutionary algorithm; Deep neural networks; Machine learning; Biology; Evolutionary biology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006705278,0.0003501562,0.0002698854,0.0001712138,0.0002204276,0.0007150474,0.0004778554,0.000487087,0.001440071],"category_scores_gemma":[0.003363986,0.0002169623,0.000219382,0.0002216375,0.0005274679,0.001414039,0.0005447909,0.0006739564,0.0001466565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004004576,"about_ca_system_score_gemma":0.0002767261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005074704,"about_ca_topic_score_gemma":0.001332424,"domain_scores_codex":[0.9998394,0.00005832355,0.000008069174,0.00004321934,0.00003253155,0.00001833409],"domain_scores_gemma":[0.9991512,0.0005669031,0.00005215023,0.0001286104,0.00006791885,0.00003320208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002623562,0.0001265163,0.007251061,0.00020602,0.0001049886,0.0002870881,0.0002217277,0.7362297,0.08651606,0.02801244,0.0005844687,0.1401976],"study_design_scores_gemma":[0.00002824423,0.0002373606,0.001745118,0.00002066447,0.00004175906,0.0001118516,0.0000806276,0.9458093,0.03614351,0.01308858,0.002675546,0.00001743438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8043154,0.0005631505,0.18581,0.0005963637,0.00004322724,0.00003124171,0.00009722234,0.0002750688,0.00826838],"genre_scores_gemma":[0.9630757,0.0001299375,0.03599193,0.00004798377,0.000005310339,0.00002313708,0.00005178979,0.00004301688,0.0006312281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001440071,"threshold_uncertainty_score":0.004817545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00489399679339767,"score_gpt":0.2192119511954768,"score_spread":0.2143179544020791,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}