{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005342766,0.0002247608,0.0003099805,0.00006361676,0.0003032372,0.0001899862,0.0006907196,0.0001309151,0.001283727],"category_scores_gemma":[0.004953492,0.0001677132,0.0001225944,0.0002098937,0.0006711556,0.0001422912,0.00008481455,0.0002372837,0.0002798497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000422983,"about_ca_system_score_gemma":0.0002249697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007135727,"about_ca_topic_score_gemma":0.0006358055,"domain_scores_codex":[0.9976854,0.0001305263,0.0004824678,0.0005654941,0.000598414,0.0005376962],"domain_scores_gemma":[0.9958195,0.003488137,0.0001010675,0.0002409516,0.00006276308,0.0002875669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005751793,0.002172182,0.002540848,0.0002212342,0.00006878424,0.0004056732,0.007454759,0.000836678,0.2759284,0.06451928,0.02379829,0.6214787],"study_design_scores_gemma":[0.000622693,0.0002800066,0.00002572948,0.0001612548,0.00002155613,0.0001693428,0.0002011148,0.03221542,0.9130522,0.01898428,0.03379812,0.0004682849],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9085372,0.0001616513,0.06333483,0.01914333,0.001153014,0.000956949,0.0001685199,0.0004284034,0.00611609],"genre_scores_gemma":[0.9889582,0.00005355917,0.002281358,0.006251919,0.0009985667,0.00008525722,0.000002435159,0.00002638528,0.0013423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6371238,"threshold_uncertainty_score":0.9996293,"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."}}