{"id":"W2151300461","doi":"10.1016/j.mehy.2005.02.019","title":"Humans can consciously generate random number sequences: A possible test for artificial intelligence","year":2005,"lang":"en","type":"article","venue":"Medical Hypotheses","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Pseudorandom number generator; Artificial neural network; Random sequence; Computer science; Artificial intelligence; Random seed; Random variate; Random number generation; Independence (probability theory); Mathematics; Algorithm; Random variable; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002146046,0.0003652274,0.0002927344,0.0005267005,0.000309887,0.001648394,0.001124817,0.001789353,0.007812199],"category_scores_gemma":[0.04299819,0.0002164652,0.0004873568,0.0002352403,0.003286113,0.004379652,0.000863751,0.001026981,0.0009518692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002274999,"about_ca_system_score_gemma":0.0003472025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002181343,"about_ca_topic_score_gemma":0.00009068834,"domain_scores_codex":[0.9989648,0.0004678087,0.00004774088,0.0002442347,0.0002262842,0.00004920432],"domain_scores_gemma":[0.97737,0.01707501,0.001499816,0.002804049,0.0008264166,0.0004246671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001130973,0.0003258484,0.01513124,0.0005847656,0.0002740466,0.0005515888,0.001428812,0.01122668,0.02002233,0.8018438,0.01160164,0.1358783],"study_design_scores_gemma":[0.0002080364,0.0002656303,0.006392905,0.00007136916,0.00004797516,0.0006759592,0.0002711395,0.04328588,0.005843214,0.9360185,0.006867831,0.00005151379],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4304764,0.001657237,0.4294119,0.03910617,0.001636227,0.0002320135,0.001064327,0.001334383,0.09508126],"genre_scores_gemma":[0.9665763,0.0003114426,0.02887904,0.001897751,0.0003072651,0.00007724376,0.0002266675,0.00007011747,0.00165417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007812199,"threshold_uncertainty_score":0.02613443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07959807063185541,"score_gpt":0.3252406063723793,"score_spread":0.2456425357405239,"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."}}