{"id":"W4312243191","doi":"10.1609/aiide.v8i4.12561","title":"Embracing the Bias of the Machine: Exploring Non-Human Fitness Functions","year":2012,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Holy Grail; Generative grammar; Creativity; Computer science; Fitness function; Framing (construction); Computational creativity; Artificial intelligence; Fitness landscape; Selection (genetic algorithm); Cognitive science; Human–computer interaction; Function (biology); Process (computing); Psychology; Machine learning; Sociology; Engineering; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002638989,0.0001822658,0.0001963045,0.00006752727,0.0004442856,0.000191107,0.00104132,0.00004110405,0.00001160303],"category_scores_gemma":[0.0002209472,0.0000914616,0.0001344576,0.0002659636,0.000431947,0.001038625,0.0008934998,0.0003005668,0.000008175788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003941492,"about_ca_system_score_gemma":0.00001618439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003274368,"about_ca_topic_score_gemma":0.000008375141,"domain_scores_codex":[0.9988195,0.00001516175,0.000388478,0.0002383783,0.0002793222,0.0002591576],"domain_scores_gemma":[0.9990078,0.0001405099,0.000343503,0.0002680142,0.0002013462,0.00003878131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008961466,0.0005218292,0.01372783,0.00005557255,0.0001591528,1.573418e-7,0.0260143,0.00001534417,0.009281815,0.871846,0.0001889037,0.07809947],"study_design_scores_gemma":[0.0001593015,0.0007617381,0.02626841,0.00129198,0.0000888916,0.00002721383,0.04948933,0.008137046,0.7893137,0.1218395,0.00196898,0.0006539008],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743736,0.00003990271,0.005009667,0.002541848,0.0008458754,0.0003460019,0.000008684641,0.00002837292,0.01680601],"genre_scores_gemma":[0.9993933,0.00001431074,0.00002159932,0.0001380326,0.00004730436,0.00005922704,3.140157e-7,0.000005777446,0.0003200903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7800319,"threshold_uncertainty_score":0.3729696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1009582886409539,"score_gpt":0.2979899776287279,"score_spread":0.197031688987774,"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."}}