（AIにおける）スケーリング法則は、 人工知能 (AI) refer to the observable patterns that indicate how the performance of 機械学習 models improves as the size of the model, the amount of 訓練データ, or both increase. These laws suggest that larger models trained on more data tend to achieve better performance, often following a predictable curve.
In AI研究, scaling laws have been particularly influential in understanding the capabilities of large ニューラルネットワーク. For instance, as the number of parameters in a model increases, the model’s ability to generalize from training data to unseen data often improves. This relationship can be quantified mathematically, typically expressed in terms of power laws, where 性能指標 （精度や損失など）がモデルやデータセットのサイズに対してどのように改善されるかを示す観測可能なパターンを指します。
Researchers have found that these scaling relationships can help predict how a model’s performance will change with varying sizes or amounts of data, allowing for more efficient resource allocation when developing AI systems. For example, if a model’s performance improves consistently with increased size, a team might decide to invest in more 計算資源 より良い結果を得るためにモデルを拡大する。
However, it’s important to note that scaling laws do not hold indefinitely; there are diminishing returns at very large scales where performance improvements may plateau despite increasing model sizes or data. Understanding these limits is crucial for AI practitioners to avoid wasted resources and to implement models that are both efficient and effective.