@hardmaru
RT @SakanaAILabs: 【Machine Learning Street Talk 出演のお知らせ】 Sakana AIのResearch Scientistである @RobertTLange が、機械学習分野の有名ポッドキャスト @MLStreetTalk に出…
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"text": "【Machine Learning Street Talk 出演のお知らせ】\n\nSakana AIのResearch Scientistである @RobertTLange が、機械学習分野の有名ポッドキャスト @MLStreetTalk に出演いたしました。\n\n🎥動画本編：https://t.co/egnRSVRRim\n\n進化的アルゴリズムとLLMを組み合わせたオープンソースのプログラム最適化 フレームワーク「ShinkaEvolve」やAI Scientistの現在地について、ホストのTimさんと語り合いました。\n\n［ハイライト］\n1．LLM駆動の進化的探索が実用段階に\nポッドキャストでは、ShinkaEvolveの原理について詳しく解説しました。複数のフロンティアモデルをバンディットアルゴリズムで動的に選択し、3種の変異を使い分けます。これにより、ShinkaEvolveは円充填（circle packing）問題で150回未満のLLM呼び出しで最先端の解に到達しました。また、競技プログラミングの問題からなるALE-Benchでも高スコアに。こうした達成を背景に、Robertは「私たちは今、進化的LLMシステムが革新的な科学的発見をもたらせる変曲点にいる」と語っています。\n\n2.「Problem Problem」― 次に超えるべき壁\n一方でRobertは、次の課題についても言及しました。現在の手法は問題が所与であることを前提としていますが、人間の科学的営みでは問題そのものの再定義が発見の核心にあります。Robertはこれを「the problem problem」と呼び、問題と解の共進化を次のステップとして見据えています。\n\n3．AIエージェント時代の研究者のあり方\nRobertは、コーディングアシスタントへの依存について「薬物に頼っている気がすることもある」と率直に語る一方で、理想の未来像も明確に描いています。日中はシステムと協働し、夜間はシステムが自律的に実験を走らせ、朝に結果を確認する「マルチスレッド研究」。研究者は実行者ではなく操舵者になる。「実行はAIに任せ、分析は自分でやる」。AIは人間に取って代わるのではなく、人間に一日の長がある思考の潜在的な次元（latent dimension）を増幅してくれる存在としてAIがあるというのが彼の見立てです。\n\n機械学習研究の多くの部分がAI支援のもとですでに行われており、ますます研究は「with AI」から「by AI」へ、そして研究エージェントの「人間による高次のオーケストレーション」へと移行しつつあります。\n\n内容にご関心がある方は、ぜひ動画をご覧ください。Sakana AIは、この大変革の時代を先導する一員として、研究に引き続き取り組んでいきます。",
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