@ChiekoTazuke
Sakana AIが事前学習からLLM開発することを発表しました。加えてフィジカルAIに向けたワールドモデルも開発します。またその開発をどう効率よく加速できるか、Agentic Scientistの研究も行います。AI研究の最前線を走るチームと日本で一緒に働くことができて、わたしもとても嬉しいです。
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"text": "本日のSakana AIのシンポジウムは、おかげさまで満員となりました。足を運んでくださった皆さま、本当にありがとうございました！🐟\n\nユルゲン・シュミットフーバー教授の基調講演や対談を含め、イベントの模様は後日あらためてご報告します。\n\n当日は CEO の David Ha から、AI主権にとって重要となる Sakana AIの取り組みの「3つの柱」についてお話ししました。\n\n・Pre-training：次世代の事前学習モデルの開発にAIエージェントを活用する\n・Post-training：日本語やビジネスシーンにLLMを適応させる事後学習技術（Sakana Namazu）\n・Orchestration：最先端の性能を、単一のAIモデルに依存せずに実現するオーケストレーション技術（Sakana Fugu）\n\n続いてチーフサイエンティストの秋葉拓哉から、事前学習の取り組みである「Sakana Sawara（仮称）」プロジェクトと、その意義についてお話ししました。\n\nSakana AIはこれまで、LLMを最大限に活用する「エージェントの層」での技術革新を強みの一つとしてきました。しかしいま、「モデルの層」が再び重要になっています。オープンモデルと最先端モデルとの差は縮まり、モデルを作るコストは下がり、さらに強化学習によって自社のタスクやツールに合わせて特化させることもできる。こうした状況を踏まえ、モデル作りに改めて取り組むべき時期が来たと考えています。\n\n事前学習のボトルネックは、相変わらず計算機の量にあります。その限られた計算資源をどこに投じるか。モデル開発の進展は、どんな実験をどの順で行い、どのアイデアを捨てるかという判断の精度に大きく左右されるようになりました。秋葉からは、こうした開発を、AIの力も駆使しながら少数精鋭のチームで手がけていくアプローチについてお話ししました。\n\nこの発表は、日経新聞でも取り上げていただきました。\nhttps://t.co/D2gOX7xt3c 🐟",
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