AI engines refer to the underlying software frameworks, platforms, and libraries that power artificial intelligence and machine learning applications. These engines facilitate tasks like data processing, model training, prediction, and more. Here’s a brief overview of some commonly used AI engines:

1. TensorFlow: Developed by Google, TensorFlow is an open-source framework for machine learning and deep learning.
2. PyTorch: Created by Facebook’s AI research group, PyTorch is another popular open-source machine learning framework.
3. Apache MXNet: This is a deep learning framework designed for efficiency, flexibility, and scalability.
4. Keras: Built on top of TensorFlow, Keras is a high-level neural networks API, written in Python, and known for its ease of use.
5. Caffe: Developed by Berkeley Vision and Learning Center, Caffe is a deep learning framework known for its speed and modularity.
6. Microsoft Cognitive Toolkit (CNTK): Microsoft’s CNTK is designed for deep learning and offers efficient model training.
7. Scikit-learn: A machine learning library for Python, Scikit-learn is used for data mining, data analysis, and model training for traditional algorithms like clustering, regression, and classification.
8. ONNX: Open Neural Network Exchange is an open format for AI models, allowing for interchange between models trained in different frameworks.
9. H2O.ai: An open-source AI platform that offers various tools and capabilities for data analysis and machine learning.
10. Apache Mahout: Focused on linear algebra and statistical algorithms, Apache Mahout is more centered on scalable machine learning.

These engines vary in their capabilities, performance, ease of use, and community support. The choice of a particular engine will often depend on the specific requirements of a project, including the type of model to be trained, the available computational resources, and the preferences and expertise of the development team.