{"id":"W4313458650","doi":"10.34133/icomputing.0006","title":"Intelligent Computing: The Latest Advances, Challenges, and Future","year":2023,"lang":"en","type":"article","venue":"Intelligent Computing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":254,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Cognitive computing; Computer science; End-user computing; Big data; Data science; Intelligent decision support system; Marketing and artificial intelligence; Scope (computer science); Autonomic computing; Human intelligence; Soft computing; Affective computing; Computational intelligence; Cloud computing; Artificial intelligence; Utility computing; Cognition; Artificial neural network; Cloud computing security","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005118215,0.0003726586,0.00031974,0.0001412477,0.0003969058,0.00007305806,0.0003990169,0.000103545,0.00001158503],"category_scores_gemma":[0.00004590855,0.0002992332,0.00009995715,0.000484393,0.00009081266,0.0001320151,0.0003371458,0.0006145096,0.0001533468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005972322,"about_ca_system_score_gemma":0.000009566528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001915184,"about_ca_topic_score_gemma":0.000006816702,"domain_scores_codex":[0.9980517,0.00007144583,0.0005021396,0.0004474361,0.0002444253,0.0006828321],"domain_scores_gemma":[0.9987072,0.0006334019,0.00009073816,0.0003672635,0.00005805165,0.0001433735],"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":[0.000005769261,0.00001130499,0.0000541044,0.0001591189,0.00003680456,0.00003621378,0.002513562,0.222967,0.0003631297,0.002101436,0.0002693661,0.7714822],"study_design_scores_gemma":[0.000233588,0.0001033239,0.001660419,0.0003483887,0.0000306167,0.0001781596,0.004096107,0.6046306,0.01351639,0.002565654,0.3717735,0.0008631623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.690366,0.189437,0.08224161,0.003947997,0.01147139,0.001486972,0.00001062701,0.008139804,0.01289861],"genre_scores_gemma":[0.9752982,0.02179637,0.0007567592,0.0001559669,0.001837935,0.000003466691,0.00001134972,0.00007965723,0.00006024049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.770619,"threshold_uncertainty_score":0.999946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03612038669627207,"score_gpt":0.2695648683049892,"score_spread":0.2334444816087171,"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."}}