{"id":"W3033835801","doi":"10.1016/j.neucom.2020.05.078","title":"Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications","year":2020,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":313,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Universidad de Granada; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Comunidad de Madrid; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Michael J. Fox Foundation for Parkinson's Research","keywords":"Artificial intelligence; Field (mathematics); Computer science; Robotics; Applications of artificial intelligence; Data science; Artificial Intelligence System; Robot","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":[],"consensus_categories":[],"category_scores_codex":[0.0004084203,0.0001137556,0.0001241688,0.0001408637,0.000523085,0.0003550083,0.00107751,0.00002539991,6.020371e-7],"category_scores_gemma":[0.00004366566,0.00009375159,0.0000134669,0.001731704,0.0003175103,0.0007819366,0.001067445,0.0002861098,0.000002943818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000114273,"about_ca_system_score_gemma":0.00003896319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006242945,"about_ca_topic_score_gemma":0.0000138113,"domain_scores_codex":[0.9985521,0.00004248833,0.0003651961,0.0006712376,0.0001892549,0.0001796696],"domain_scores_gemma":[0.9991974,0.0001951561,0.0001448835,0.0003335793,0.00004402177,0.00008495492],"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":[0.000001737798,0.000008978476,0.000390715,0.000005533065,0.000001093768,7.13375e-7,0.0003863818,0.000460877,0.000380693,0.05650598,0.000007246281,0.9418501],"study_design_scores_gemma":[0.00002301916,0.00004538807,0.00364927,0.00001055743,0.000004694674,0.00001586911,0.0001370919,0.9807264,0.00210779,0.01109923,0.002031003,0.0001496794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01789731,0.0001490392,0.9759074,0.005346313,0.00005905168,0.0002396745,0.000004217949,0.0001784154,0.000218604],"genre_scores_gemma":[0.9755641,0.00001168994,0.02380428,0.0003796904,0.0002098151,0.00001915104,0.000004163246,0.000005535858,0.000001528732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9802656,"threshold_uncertainty_score":0.40232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0473477852512321,"score_gpt":0.3485424178329651,"score_spread":0.301194632581733,"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."}}