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第3回WBAシンポジウム 栗原聡氏講演資料

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  1. 1. 3 栗原 聡 慶應義塾大学大学院理工学研究科 電気通信大学人工知能先端研究センター(AIX) OMRON SINIC X HONDA R&D Center X
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  4. 4. Analysis, comparisons and proposals of AI/ML benchmarks and competitions. Lessons learnt. Theoretical or experimental accounts of the space of tasks, abilities and their dependencies. Tasks and methods for evaluating: transfer learning, cognitive growth, development, cumulative learning, structural self-modification and self-programming. Conceptualisations and definitions of generality or abstraction in AI / ML systems. Unified theories for evaluating intelligence and other cognitive abilities, independently of the kind of subject (humans, animals or machines): universal psychometrics. Evaluation of conversational bots, dialogue systems and personal assistants. Evaluation of common sense, reasoning, understanding, causal relations. Evaluation of multi-agent systems in competitive and cooperative scenarios, evaluation of teams, approaches from game theory. Better understanding of the characterisation of task requirements and difficulty (energy, time, trials needed...), beyond algorithmic complexity. Item generation. Item Response Theory (IRT). Evaluation of AI systems using generalised cognitive tests for humans. Computer models taking IQ tests. Psychometric AI. Assessment of replicability, reproducibility and openness in AI / ML systems. Evaluation methods for multiresolutional perception in AI systems and agents. Analysis of progress scenarios, AI progress forecasting, associated risks. •Analysis of requirements for autonomy and generality •Design proposals for cognitive architectures targeting generality and/or autonomy •Complex layered networked systems and architectures •Synergies between AI approaches •Integration of top-down and bottom-up approaches (e.g. logic-based and neural-inspired) •Emergence of (symbolic) logic from neural networks •New programming languages relevant to generality and autonomy •New methodologies relevant to generality and autonomy •New architectural principles relevant to generality and autonomy •Complex (e.g. layered, hierarchical or recursive) network architectures for generality and autonomy •New theoretical insights relevant to generality and autonomy •Motivation (intrinsic, extrinsic) for enabling autonomous behavior selection and learning •Analysis of the potential and limitations of existing approaches •Methods to achieve general ((super)human-like) performance •Methods for epigenetic development •Baby machines and experience-based, continuous, online learning •Seed-based programming and self-programming •Education for systems with general intelligence and high levels of autonomy •Understanding and comprehension •Reasoning and common-sense •Acquisition of causal models •Cumulative knowledge acquisition •Curiosity, emotion and motivation for enabling autonomous behavior and knowledge acquisition •Meta-planning, reflection and self-improvement •Principles of swarm intelligence for generality and autonomy
  5. 5. こなせるタスク数 用途限定型 汎用型
  6. 6. すべての実行手順を予め列挙するのは難しい 想定外には対応できない
  7. 7. 学習(経験)+新たな可能性の探索・予測
  8. 8. タスクを実行する理由は??
  9. 9. エアコンから人まで 自律システムには「目的」が存在する 目的を達成するためのプランが存在する エアコンの目的は予め設定されている 最も自律性の高い存在→生物→人 人の目的はエアコンの目的とは違う→ メタ目的=「適切な目的を導出する能力」 <目的の達成:学習/推論/プランニンク> リアクティブプランニング/実時間プランニング
  10. 10. 脳型=自律分散型=群知能型 タスク1担当! タスク2の実行が 必要になりそう.. 情報収集するよ! 即応案件発生!! → , etc.
  11. 11. <答え=YES> より汎用性の高いAIには自律性が求められる. →生物(人)に向かうということ 開発での問題は?課題は? ・矛盾する目的同士の解消 ・メタ目的の設定 ・メタ目的の変更
  12. 12. 目的同士が競合する場合
  13. 13. <答え=生きること> →生物のメタ目的は生存・種の保存 →精神的なバランス状態の維持 汎用AIに与えるメタ目的は? 家の整理整頓 仕事円滑化 *家の見守り *人を快適にする
  14. 14. まずは作る!(なんとかGより先に) 脳型であるからこそのメリット 分散型であることのバランス性 自己組織化的手法による成長モデル ∼知識の量ではない∼ ∼限られたリソースの使い方∼ 最大限の制御可能性・可読性 進化的手法の適用範囲