Libraries in Python for AI (Artificial Intelligence) develoment are

1. Numpy:

   Working with arrays of data, commonly used in machine learning.

2. Pandas:

   Working with data frames and data analysis.

3. Matplotlib:

   Creating visualizations and graphs

4. Scikit-learn:

   A library for machine learning Algorithm

5. TensorFlow:

   Creating and running machine learning models

6. Keras:

   A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.

7. PyTorch:

   Used for deep learning, developed by Facebook.

8. OpenCV:

   A library for computer vision tasks such as object detection and face recognition.

9. NLTK:

   A NLP library for text analysis and language understranding.

10. Gensim:

    A library for topic modeling and document similarity analysis

11. SciPy:

    A library for topic modeling and document similarity analysis

12. MXNet:

    A deep learning framework designed for both efficiency and flexibility, created by Apache

13. Hugging Face Transformers:

    A librar that provides a wide range of pre-trained models for NLP task

14. FastAI:

    A deep learning library designed for making AI more accessible and easier to use.

15. AllenNLP:

    A NLP library built on PyTorch that allows for easy experimentation with models.

16. SpaCy:

    A NLP library design for high-performance text processing.

17. Caffe:

    A deep learning framework used for image recognition and other visual tasks.

18. Theano:

    A library for fast numerical computation, commonly used in deep learning.

19. PyBrain:

    A library for neural networks and other machine learning algorithms.

20. Chainer:

    A flexible deep learning framework for Python.

Some of the most commonly used packages in Python, apart from the ones specifically related to AI (Artificial Intelligence) and machine learning, include:

1. Request:

   A package for making HTTP requests.

2. Beautiful Soup:

   A package for web scraping and parsing HTML and XML documents.

3. Pygame:

   Building games and multimedia applications.

4. Flask:

   A lightweight web framework for building web applications in Python.

5. Django:

   A full-stack web framework for building complex web application.

6. Pillow:

   Processing and manupulating images.

7. NumPy:

   For numerical computing with Python.

8. Pandas:

   For data manipulation and analysis.

9. Matplotlib:

   For creating visualizations and plots.

10. SciPy:

    For scientific computing and advanced mathematics.