{"id":"W1947291763","doi":"10.48550/arxiv.1412.6564","title":"Move Evaluation in Go Using Deep Convolutional Neural Networks","year":2014,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Monte Carlo tree search; Computer science; Convolutional neural network; Artificial intelligence; Deep learning; Machine learning; Monte Carlo method","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000950059,0.001295962,0.0008494803,0.000867118,0.0004775526,0.001069458,0.001542206,0.001325308,0.003208261],"category_scores_gemma":[0.004178477,0.0003640311,0.0004906448,0.0004536841,0.0007068012,0.00190095,0.001091831,0.001463105,0.0007068433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001936008,"about_ca_system_score_gemma":0.001097381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03758287,"about_ca_topic_score_gemma":0.06316879,"domain_scores_codex":[0.9994316,0.0001201366,0.00002353027,0.0001707867,0.000120692,0.0001333733],"domain_scores_gemma":[0.9989748,0.0005549147,0.0001198225,0.00008702573,0.0001374291,0.0001260344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00114066,0.0004773985,0.01797825,0.0001795877,0.0001835196,0.0002061333,0.0001321581,0.7351831,0.004690301,0.0068114,0.006989859,0.2260276],"study_design_scores_gemma":[0.00002226111,0.00009041029,0.00143688,0.00001620542,0.00001063098,0.00001586206,0.00002607417,0.9942147,0.001143748,0.002532569,0.0004824771,0.000008187921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7719266,0.001706428,0.1963063,0.0009407909,0.0002072606,0.0002649304,0.0009953735,0.005389265,0.02226303],"genre_scores_gemma":[0.9744539,0.0001092083,0.01981727,0.0001447054,0.00001528263,0.00003802832,0.000646906,0.00006147315,0.004713184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03758287,"threshold_uncertainty_score":0.07472825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116652315484495,"score_gpt":0.227260463415021,"score_spread":0.1155952318665715,"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."}}