Updates in September 2021, many several powerful AI tools available for various applications. The AI landscape is rapidly evolving, and new tools have been emerging since then. Here are some notable AI tools that were popular up to that point:
1. TensorFlow: An open-source machine learning framework developed by Google. It's widely used for various AI tasks, including deep learning.
2. PyTorch: Another popular open-source machine learning framework that's known for its dynamic computation graph, making it particularly well-suited for research and experimentation.
3. scikit-learn: A simple and efficient library for machine learning in Python. It provides a wide range of tools for tasks like classification, regression, clustering, and more.
4. Keras: Originally an independent library, Keras was integrated into TensorFlow. It provides a high-level interface for building and training neural networks.
5. OpenCV: Open Source Computer Vision Library is used for computer vision applications. It contains various functions for image and video processing.
6. NLTK (Natural Language Toolkit): A leading platform for building Python programs to work with human language data, used for tasks like tokenization, stemming, tagging, parsing, and more.
7. spaCy: Another popular natural language processing library, spaCy is known for its speed and efficiency in performing tasks like tokenization, part-of-speech tagging, named entity recognition, and more.
8. Hugging Face Transformers: This library provides pre-trained models and utilities for working with transformer-based architectures, which are widely used for tasks like natural language understanding and generation.
9. AutoML Tools (AutoKeras, TPOT, Auto-Sklearn): These tools automate aspects of the machine learning process, from feature selection to hyperparameter tuning, making it easier for non-experts to develop effective models.
10. Fast.ai: Known for its practical approach to deep learning education, Fast.ai provides a high-level API that simplifies the process of training complex models.
11. IBM Watson: A suite of AI services and tools that enable developers to build and deploy AI-powered applications, covering areas like natural language processing, computer vision, and more.
12. Microsoft Azure Cognitive Services: A collection of APIs and services that enable developers to integrate AI capabilities into their applications, including vision, speech, language, and decision services.
13. Google Cloud AI: Google offers a variety of AI tools and services, including Vision AI, Speech-to-Text, Natural Language Processing, AutoML, and more.
14. Amazon AI Services: Amazon's suite of AI services includes tools for speech recognition, natural language processing, image analysis, and more, designed to be integrated into applications and services.
Remember that the "best" tool can depend on your specific needs, the problem we're trying to solve, our level of expertise, and the current state of the AI landscape. Always evaluate tools based on their compatibility with the project requirements, ease of use, community support, and documentation. It's a good idea to stay updated with the latest developments in the field to make informed decisions.