{"id":"W2751881915","doi":"10.14236/ewic/eva2017.60","title":"Engagement with Artificial Intelligence through Natural Interaction Models","year":2017,"lang":"en","type":"article","venue":"Electronic workshops in computing","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Avatar; Conversation; Human–computer interaction; Creativity; Natural (archaeology); Dialog system; Ambient intelligence; Chatbot; Expression (computer science); Virtual agent; Multimedia; Artificial intelligence; World Wide Web; Psychology; Communication; Dialog box","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":[0.0005111598,0.0001633769,0.0001550184,0.00009600678,0.0007340575,0.0006674131,0.0008163603,0.00005205587,0.00001361721],"category_scores_gemma":[0.00003927481,0.0001505819,0.00004609512,0.0001784447,0.00004828977,0.001649221,0.0002652803,0.0008234948,0.00002886543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000228226,"about_ca_system_score_gemma":0.0001014044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004391071,"about_ca_topic_score_gemma":0.0002027227,"domain_scores_codex":[0.9984098,0.00008777141,0.000288918,0.0004515288,0.0002344207,0.0005275405],"domain_scores_gemma":[0.999001,0.0001284496,0.0002427404,0.0005345029,0.00006356102,0.00002973386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003094224,0.00009692288,0.00006692371,0.000008578475,0.00002639604,0.00002084902,0.00178153,0.03540979,0.00009436113,0.2189686,0.00004638242,0.7434488],"study_design_scores_gemma":[0.0001255398,0.00008385054,0.0002633865,0.0001810599,0.000004941428,0.00002920051,0.0002509131,0.8899466,0.002621797,0.1059274,0.0003132376,0.0002521188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1101795,0.0001045661,0.8848099,0.0007877056,0.0005800363,0.0001670828,1.002581e-7,0.0001262828,0.003244803],"genre_scores_gemma":[0.9866264,0.00002611273,0.01289785,0.0001658266,0.0002213592,0.000008154027,0.00000265445,0.00001076636,0.00004084085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8764469,"threshold_uncertainty_score":0.6435879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06165613229559615,"score_gpt":0.3242827823577143,"score_spread":0.2626266500621182,"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."}}