{"id":"W4323035956","doi":"10.1002/9781119790686.ch1","title":"An Introduction to AI for Non‐Experts","year":2023,"lang":"en","type":"other","venue":"AI in Clinical Medicine","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Key (lock); Machine learning; Focus (optics); Simple (philosophy); Training set; Data science","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001285298,0.0002845523,0.0006812559,0.0006358994,0.00003866663,0.00005560439,0.0005347635,0.0004166395,0.002243384],"category_scores_gemma":[0.002965868,0.0002197331,0.00007727987,0.000783535,0.0001526412,0.0003154464,0.0001310356,0.0003317798,0.000824282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002134784,"about_ca_system_score_gemma":0.0000238703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001749732,"about_ca_topic_score_gemma":0.004187503,"domain_scores_codex":[0.9977242,0.0000126585,0.0008210694,0.0008147524,0.0002924828,0.0003348438],"domain_scores_gemma":[0.9985957,0.0002163976,0.0002580337,0.0007346641,0.0001564051,0.00003885123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006468409,0.0001040164,0.001754685,0.0001990747,0.00001733247,0.000006828484,0.000007932623,0.000002833903,0.00001183135,0.001153468,0.929326,0.0673513],"study_design_scores_gemma":[0.000521353,0.0000998432,0.002226789,0.0006927279,0.00005806786,4.985989e-7,0.00004182266,0.0005708991,0.000001919666,0.001033939,0.9944878,0.0002643442],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"editorial","genre_scores_codex":[0.0006844699,0.0004692893,0.03851842,0.7303224,0.08929358,0.00623017,0.00008325303,0.0031659,0.1312325],"genre_scores_gemma":[0.01344139,0.0007358736,0.001046434,0.1794306,0.4392593,0.0006793381,0.00163483,0.002723495,0.3610487],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.5508918,"threshold_uncertainty_score":0.9999537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.136727195487171,"score_gpt":0.4542541477799338,"score_spread":0.3175269522927628,"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."}}