{"id":"W3087761062","doi":"10.4018/978-1-7998-3499-1.ch012","title":"Applications of Artificial Intelligence in Media and Entertainment","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Entertainment; Production (economics); Virtual reality; Media industry; Affect (linguistics); Engineering; Political science; Computer science; Multimedia; Sociology; Public relations; Artificial intelligence; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005510004,0.0008019357,0.0003367015,0.001750693,0.001252052,0.005918664,0.0007905196,0.001500868,0.01360196],"category_scores_gemma":[0.001019689,0.0003020107,0.0003765151,0.00198423,0.004383308,0.004586343,0.001802846,0.002779392,0.00341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002104242,"about_ca_system_score_gemma":0.001287052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001808983,"about_ca_topic_score_gemma":0.003280311,"domain_scores_codex":[0.9995149,0.0001772526,0.00001715983,0.00005247829,0.0002031384,0.00003507462],"domain_scores_gemma":[0.9995236,0.0003320547,0.00001390572,0.00004555643,0.00005957673,0.00002523179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005267164,0.00001848896,0.00009674294,0.0002604962,0.000008155189,0.00008118489,0.001030042,0.0006651097,0.0003067695,0.8853562,0.04295174,0.06921975],"study_design_scores_gemma":[0.00000252365,0.000007004626,0.0001941782,0.0003619819,0.000003942836,0.0001548279,0.000320759,0.0006842825,0.0002181492,0.2251389,0.7729058,0.00000779993],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0008980962,0.06825948,0.01009906,0.004439909,0.001115477,0.00003704037,0.00004363873,0.00009598668,0.9150113],"genre_scores_gemma":[0.08395129,0.18615,0.03318649,0.005069961,0.002752172,0.0002461328,0.0002334133,0.0002333457,0.6881772],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01360196,"threshold_uncertainty_score":0.04550308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04354225603601848,"score_gpt":0.3509634847120224,"score_spread":0.3074212286760039,"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."}}