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関連する概念動画

Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

744
Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

381
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
381
Language and Cognition01:27

Language and Cognition

349
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
349
Castigliano's Theorem: Problem Solving01:14

Castigliano's Theorem: Problem Solving

658
The deflection of a simply supported beam that carries a central point load can be analyzed using structural mechanics principles, particularly by applying Castigliano's theorem. This theorem relates the displacement at the load application point to the partial derivatives of the strain energy in the structure. The simply supported beam with a point load at its center has symmetric reaction forces at the supports, each bearing half of the load. The bending moment at any point along the beam...
658
Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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大型言語モデルによるプログラム検索による数学的発見

Bernardino Romera-Paredes1, Mohammadamin Barekatain2, Alexander Novikov2

  • 1Google DeepMind, London, UK. brp@google.com.

Nature
|December 14, 2023
PubMed
まとめ
この要約は機械生成です。

大型言語モデル (LLM) は FunSearch を使って科学的な発見をすることができます.これは,LLM と評価器を組み合わせて,複雑な問題を解くための進化的方法です.

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The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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科学分野:

  • 人工知能
  • コンビネタリック
  • コンピュータ科学

背景:

  • 大型言語モデル (LLM) は複雑なタスクに優れているが,しばしば混同され,科学的な応用が制限されます.
  • 現存するLLMアプローチは,不正確さのために科学的発見と闘っています.

研究 の 目的:

  • 科学的発見のためのLLMの能力を高めるための進化的手順であるFunSearchを導入します.
  • FunSearchの有効性を実証する.

主な方法:

  • 進化論的な探求に際して 体系的な評価者とのペアリング
  • FunSearchを極限コンビネタリック (キャップセット問題) とアルゴリズム問題 (オンライン・ビン・パッキング) に適用する.

主要な成果:

  • 限られた次元と非対称的なケースの両方で大きなキャップセットのための新しい改良された構造を発見した.
  • 既存のベースラインを上回る オンライン・ビン・パッキングの新たなヒューリスティックを特定した.
  • 問題を解くために解釈可能なプログラムを生成するFunSearchの能力を示しました.

結論:

  • FunSearchは,LLMが確立された科学分野において重要な発見を行うことを可能にします.
  • このアプローチは,LLMの混乱を克服し,科学的アプリケーションの信頼性を高めます.
  • FunSearchは AIによる科学的発見のための 拡張可能で解釈可能な方法を提供します