{"id":"W6920479254","doi":"10.60692/bfg29-hd855","title":"Roadmap on emerging hardware and technology for machine learning","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Engineering and Physical Sciences Research Council","keywords":"Von Neumann architecture; Neuromorphic engineering; Artificial neural network; Applications of artificial intelligence; Architecture; Efficient energy use; Unconventional computing; Reconfigurable computing; Emerging technologies","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":[],"consensus_categories":[],"category_scores_codex":[0.00005115948,0.000102777,0.0001259275,0.0001030258,0.0001109818,0.0000328555,0.00005372312,0.00005039839,0.000001916851],"category_scores_gemma":[0.00002653036,0.00009349505,0.0000197728,0.0001340333,0.000006304633,0.0002186303,0.00002369269,0.0001210197,0.00004621512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001911009,"about_ca_system_score_gemma":0.000001891718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.580256e-8,"about_ca_topic_score_gemma":8.625207e-9,"domain_scores_codex":[0.9995179,0.000006559608,0.0002101486,0.00007481638,0.00005595307,0.0001346476],"domain_scores_gemma":[0.9998029,0.00000651273,0.00005162312,0.00006290208,0.00003011114,0.00004594504],"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":[0.0002333545,0.00000133134,0.04608556,0.006618938,0.0001457412,0.00001379477,0.1286374,0.7610994,0.0008248002,0.001833057,0.0002402003,0.05426646],"study_design_scores_gemma":[0.001067837,0.0001540662,0.0003541536,0.0001959482,0.00001409911,0.00002801687,0.007349594,0.9643713,0.01873854,0.000002848499,0.007420683,0.0003029157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7670493,0.00002270127,0.2295945,0.0002371679,0.0002247091,0.0003536401,0.00002830412,0.0016513,0.0008383259],"genre_scores_gemma":[0.999381,2.054779e-7,0.0004235088,0.00008591883,0.00005557248,0.00001936533,0.000007838082,0.00001112277,0.000015443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2323317,"threshold_uncertainty_score":0.3812618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02624336764231178,"score_gpt":0.2040163267182287,"score_spread":0.1777729590759169,"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."}}