{"id":"W4400646540","doi":"10.1109/mnet.2024.3425594","title":"Telecom’s Artificial General Intelligence (AGI) Vision: Beyond the GenAI Frontier","year":2024,"lang":"en","type":"article","venue":"IEEE Network","topic":"Satellite Communication Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Frontier; Telecommunications; Computer science; Artificial intelligence; Political 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":[],"category_scores_codex":[0.0004254778,0.0001790758,0.0001659174,0.00004754088,0.0001341332,0.000247844,0.0005459209,0.000105421,0.00009941218],"category_scores_gemma":[0.000006088538,0.0001366916,0.00009889517,0.0005017055,0.00006295145,0.0001187492,0.00005166803,0.000365349,0.001117388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007457603,"about_ca_system_score_gemma":0.00002067199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001090285,"about_ca_topic_score_gemma":0.00003358709,"domain_scores_codex":[0.9987737,0.00009392979,0.0003791644,0.0002067362,0.0002048857,0.0003416101],"domain_scores_gemma":[0.9989136,0.0001991482,0.00002225857,0.0007659795,0.00003404665,0.00006491396],"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":[0.00001033043,0.00001246466,0.0001334304,0.00007726771,0.0001438112,0.00001833384,0.00108347,0.2777256,0.001032322,0.003074245,0.5586786,0.1580101],"study_design_scores_gemma":[0.00001758643,0.00001292012,0.0001696368,0.00007753847,0.0000160918,0.00001846322,0.00004200404,0.195902,0.00141144,0.005549767,0.7965686,0.0002140174],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.08280685,0.4036887,0.2034658,0.003418059,0.179033,0.001931586,0.00003832482,0.005343272,0.1202745],"genre_scores_gemma":[0.9778838,0.002042018,0.001497797,0.0003481203,0.01630014,0.00008730312,0.0000160216,0.000111634,0.001713197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8950769,"threshold_uncertainty_score":0.9996604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235411912861577,"score_gpt":0.2656998672625484,"score_spread":0.2433457481339326,"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."}}