跳到主要內容

什麼是「人類參與流程」?

Human-in-the-loop (HITL) is a critical process in data annotation that blends human expertise with machine automation. This hybrid approach is highly effective in ensuring high-quality labeled datasets, particularly for machine learning models that power AI (artificial intelligence) applications.

Benefits of human-in-the-loop

Increased accuracy

Combining human expertise with machine automation ensures the highest possible data quality

Efficient handling of edge cases

Machines may struggle with ambiguous or rare cases, but human annotators can provide the necessary context for accurate labeling

Cost efficiency

While human involvement is necessary, the automation of simple tasks reduces the overall cost, making the process efficient and scalable

The HITL process explained

Human-in-the-loop involves a cycle where machines first attempt to label the data using automated techniques. Human annotators then review and correct the results. Here's a breakdown of the HITL process:

  • Machines attempt to label a dataset using pretrained models. This is particularly effective for large volumes of data that contain well-known patterns.

  • Human experts step in to audit and correct any errors or inconsistencies. This is crucial for edge cases where machine predictions might fail.

  • Once human corrections are made, their outcomes are fed back into the ML model to help it improve its future labeling accuracy. The system becomes more intelligent over time, gradually reducing the reliance on human intervention for repetitive tasks.

How Uber AI Solutions can help with human-in-the-loop

We’re uniquely positioned to enhance the HITL process with our comprehensive data annotation services. As AI and ML models become more complex, HITL becomes an essential component for ensuring accurate, reliable data labeling. Here’s how Uber AI Solutions can help streamline and optimize your HITL workflows:

結論

Uber AI Solutions is your trusted partner for scaling human-in-the-loop processes. By combining a global human workforce with advanced tools like uLabel, Uber ensures that your machine learning models are trained with the highest quality data. With scalable, customizable, and cost-efficient HITL workflows, Uber AI Solutions can help you optimize your data annotation pipeline and enhance the performance of your AI applications.

Uber 人工智能解決方案

我們在管理大規模資料標籤作業方面擁有超過 9 年的經驗, 並提供 30 多種進階功能, 包括影像和影片註記、文字標籤、3D 點雲處理、語意分割、意圖標籤、情緒偵測、文件抄錄、合成資料生成、物體追踪和 LiDAR 標註。

我們的多語言支援支援 100 多種語言, 涵蓋歐洲、亞洲、中東和拉丁美洲的方言, 確保為全球各種應用程式提供全面的 AI 模型訓練。

我們的解決方案包括:

  • 資料註解和標籤: 為文字、音訊、影像、影片和其他技術提供專業且精準的註解服務

  • 產品測試: 透過彈性的服務等級協議、多樣化的架構、超過 3,000 台測試裝置, 簡化所有流程, 縮短髮布週期, 提高產品測試效率

  • 語言和本地化: 為世界各地的所有人提供世界級的使用者體驗