{"id":"W4353091694","doi":"10.1038/s41467-023-37180-x","title":"Catalyzing next-generation Artificial Intelligence through NeuroAI","year":2023,"lang":"en","type":"review","venue":"Nature Communications","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":283,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute; Ontario Brain Institute; Montreal Neurological Institute and Hospital","funders":"National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Eye Institute; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Intelligence Advanced Research Projects Activity; Defense Advanced Research Projects Agency; Office of Naval Research; Cold Spring Harbor Laboratory; Multidisciplinary University Research Initiative; James S. McDonnell Foundation; Canadian Institute for Advanced Research; National Science Foundation; Semiconductor Research Corporation; National Institutes of Health; Lourie Foundation; Howard Hughes Medical Institute","keywords":"Computer science; Artificial intelligence; Data science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006930418,0.000734062,0.0006269018,0.001257634,0.000316023,0.001393294,0.001072227,0.001498413,0.005223297],"category_scores_gemma":[0.001063838,0.0002627664,0.0004309948,0.001353084,0.0009479374,0.002788434,0.001143421,0.002682745,0.003612658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000856575,"about_ca_system_score_gemma":0.001020122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008005393,"about_ca_topic_score_gemma":0.001305722,"domain_scores_codex":[0.999777,0.00004886367,0.00001563775,0.00003552277,0.00009696603,0.00002602099],"domain_scores_gemma":[0.9996234,0.0002269635,0.00003227388,0.00002226788,0.00006275129,0.00003229309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004503164,0.00006884576,0.0001540263,0.008527664,0.00008248502,0.0001618843,0.0001169647,0.00171156,0.001760368,0.1022786,0.04317736,0.8419152],"study_design_scores_gemma":[0.00000731559,0.00002946114,0.0001409241,0.001224535,0.00002401191,0.0002513051,0.00003154576,0.0002637689,0.0004269246,0.02009925,0.9774889,0.00001203702],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002474122,0.9869784,0.001881419,0.001302565,0.0004892366,0.000008023307,0.00002432773,0.00003586673,0.009032658],"genre_scores_gemma":[0.003970777,0.9912853,0.001106015,0.0007732622,0.0003747559,0.0000216406,0.0000517338,0.00001093849,0.002405642],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005223297,"threshold_uncertainty_score":0.0174737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3747334748993592,"score_gpt":0.4284879568866756,"score_spread":0.05375448198731642,"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."}}