Top Free Artificial Intelligence Tools for Unleashing the Power of AI

Artificial Intelligence (AI) is transforming the way we live and work, and its applications are vast, ranging from simple chatbots to complex self-driving cars. The AI landscape is evolving rapidly, and as a result, many organizations and individuals are looking to adopt AI technologies and tools. Fortunately, there is no shortage of free and open-source tools to help you get started with AI and Machine Learning (ML). In this article, we will highlight the best free AI tools available today.

1. Accord.NET: Accord.NET is a framework for developing machine learning, computer vision, and computer audition applications with C#.
2. AI Writer: An AI-powered text generation tool that creates content for presentations, reports, and other documents.
3. AIsummarizer: An AI-powered summarization tool that creates summaries of articles and reports.
4. Apache MXNet: This is a deep learning framework that is designed to be flexible and scalable. It supports a variety of programming languages including Python, C++, and R, making it accessible to a wide range of users.
5. Apache Spark: Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs for distributed data processing, machine learning, and graph processing.
6. Apache Storm: Apache Storm is a distributed real-time computation system. It is used for processing big data streams, such as log data and social media feeds, in real-time.
7. Articoolo: An AI-powered writing tool that creates articles, summaries, and reports.
8. Automated Insights: An AI-powered content generation platform that creates reports, summaries, and articles.
9. AWS SageMaker: Amazon Web Services SageMaker is a fully managed machine learning platform. It provides tools and services for building, deploying, and managing machine learning models in the cloud.
10. Caffe: Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). It is designed for computer vision and image processing tasks and is used by researchers, students, and businesses alike.
11. Caffe2: This is a deep learning framework that is designed for high performance and scalability. It can be used for a variety of applications including image and video classification, speech recognition, and natural language processing.
12. Clara.ai: An open-source platform for building conversational AI, offering components for natural language processing, dialogue management, and more.
13. CNTK (Cognitive Toolkit): CNTK is a deep learning framework developed by Microsoft Research. It is designed for large-scale and complex ML tasks and supports a wide range of programming languages, including Python, C++, and Julia.
14. ConvAI2: A conversational AI competition organized by Facebook AI Research, providing open-source data, models, and tools for building and evaluating chatbots.
15. Copy.ai: An AI-powered copywriting tool that generates high-quality content for presentations, websites, and other documents.
16. Databricks: Databricks is a web-based platform for data engineering, machine learning, and analytics. It provides a collaborative environment for working with big data and offers a variety of machine learning tools and services.
17. Deeplearning4j: An open-source, distributed deep learning framework for building and deploying AI models. It’s written in Java and has support for GPU acceleration.
18. FastAI: FastAI is a high-level library for deep learning built on top of PyTorch. It is designed to make deep learning accessible to everyone, regardless of their programming experience or ML knowledge.
19. Gensim: This is a library for topic modeling and document similarity in Python. It is designed to be fast and efficient, and is used for a variety of NLP tasks such as document classification, topic modeling, and text summarization.
20. Google Cloud AI Platform: Google Cloud AI Platform is a suite of machine learning services for building and deploying AI models. It offers a range of tools for data processing, model training, and deployment, as well as services for managing and scaling machine learning models.
21. GPT-3 Sandbox: A platform for experimenting with OpenAI's GPT-3 model for text generation.
22. Grover: A deep learning model that generates news articles, summaries, and stories.
23. H2O.ai: This is an open-source platform for machine learning and AI that is designed to be easy to use and scalable. It provides a suite of machine learning algorithms including gradient boosting, random forests, and deep learning.
24. Hubtype: A platform for building chatbots and conversational interfaces, offering features for natural language processing, machine learning, and more.
25. IBM Watson Studio: A cloud-based platform for data science and machine learning, offering a range of tools and resources for AI development.
26. Jarvis: A conversational AI that helps users to create, edit, and publish content.
27. Jupyter Notebook: This is an open-source web application for interactive computing that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. It is widely used for data analysis, machine learning, and scientific computing.
28. Keras: Keras is a high-level neural network API written in Python. It was developed with a focus on enabling fast experimentation and is capable of running on top of TensorFlow, CNTK, or Theano.
29. KNIME Analytics Platform: KNIME Analytics Platform is an open-source data analytics platform for machine learning and data exploration. It provides a visual interface for working with data and building machine learning models.
30. Kukka: An open-source tool for building and deploying chatbots, offering a focus on simplicity and flexibility.
31. LUIS.ai: A cloud-based platform for building conversational AI, offering features for natural language processing, machine learning, and more.
32. Lynt: An AI-powered content creation platform that uses deep learning to create documents and presentations.
33. Meya: An open-source platform for building chatbots, offering features for conversation flow management, integrations, and more.
34. MLflow: MLflow is an open-source platform for the complete machine learning lifecycle, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.
35. MLlib: Apache Spark's scalable machine learning library, providing algorithms for classification, regression, clustering, and more.
36. MxNet: An open-source, flexible, and scalable deep learning framework that supports a variety of languages, including Python, R, and Julia.
37. MyTranscript: An AI-powered transcription tool that creates accurate transcripts of audio and video files.
38. NLP.js: A natural language processing library for JavaScript, supporting a variety of NLP tasks, such as sentiment analysis, entity recognition, and more.
39. NLTK (Natural Language Toolkit): NLTK is a popular Python library for working with human language data. It provides tools and resources for tokenization, stemming, tagging, parsing, semantic reasoning, and much more.
40. OpenAI Codex: An AI-powered platform for generating code snippets and documents.
41. OpenCV (Open Computer Vision): OpenCV is an open-source computer vision library that provides tools and algorithms for image and video processing. It is widely used in computer vision research, robotics, and surveillance.
42. Orange: Orange is an open-source data visualization and analysis tool. It offers a range of machine learning algorithms and visualizations for exploring and analyzing data.
43. PyCharm: PyCharm is a Python-specific integrated development environment (IDE) designed for professional Python developers. It provides tools for code editing, debugging, and testing, as well as integrations with popular machine learning libraries.
44. PyTorch: PyTorch is an open-source machine learning library developed by Facebook's AI Research lab. It is designed for research, experimentation, and production and has become one of the most popular ML frameworks.
45. Quill: An AI-powered writing assistant that creates content for presentations, reports, and other documents.
46. R Studio: R Studio is an integrated development environment (IDE) for R, a popular language for statistical computing and data analysis. It provides a range of tools for coding, debugging, and data visualization, and supports the use of machine learning libraries.
47. RapidMiner: RapidMiner is a data science platform for building and deploying machine learning models. It provides a visual interface for working with data, building models, and deploying to production.
48. Rasa: An open-source framework for building conversational AI, offering components for natural language understanding, dialogue management, and more.
49. Replika: An AI-powered writing tool that creates documents, presentations, and reports.
50. Retrieval-Based Chatbot: A simple and fast implementation of a retrieval-based chatbot in Python, using a bag of words approach and a cosine similarity function.
51. Scikit-learn: Scikit-learn is a machine learning library for Python that provides simple and efficient tools for data mining and data analysis. It supports a wide range of ML algorithms and is easy to use, even for beginners.
52. Scikit-Surprise: A Python library for building and analyzing recommender systems, offering algorithms for collaborative filtering, matrix factorization, and more.
53. SciPy: SciPy is a Python library for scientific computing. It provides tools for numerical computation, optimization, signal processing, and more, as well as integrations with popular machine learning libraries.
54. Shogun: A free and open-source machine learning library for multiple programming languages, offering a wide range of algorithms for classification, regression, clustering, and more.
55. SnatchBot: A cloud-based platform for building and deploying chatbots, offering features for conversation design, integrations, and more.
56. Spark MLlib: Spark MLlib is a machine learning library for Apache Spark. It provides a range of algorithms for classification, regression, clustering, and more, and is designed for distributed data processing.
57. TalkToTransformer: A platform for experimenting with OpenAI's GPT-3 model for text generation.
58. Tars: An open-source platform for building chatbots, offering features for conversation design, integrations, and more.
59. TensorFlow: TensorFlow is an open-source software library for machine learning developed by Google. It is widely used for research and production and supports a wide range of ML tasks, including image recognition, natural language processing, and reinforcement learning.
60. Theano: Theano is a Python library for fast numerical computation, especially well-suited for large-scale numerical computations on multi-GPU systems.
61. Turi Create: An open-source platform for building custom machine learning models and deploying them in various applications.
62. Weka: Weka is a collection of machine learning algorithms for data mining tasks. It provides a visual interface for working with data and building models, as well as a range of algorithms for classification, clustering, and more.
63. Wordsmith: An AI-powered content generation platform that creates reports, summaries, and articles.
64. Yellowbrick: Yellowbrick is a visualization library for machine learning. It provides a range of visualizations for model evaluation and interpretation, and is designed to be used in conjunction with scikit-learn, a popular machine learning library for Python.