Best AI tools for daily use

The best AI tool to use depends heavily on your specific needs, technical expertise, budget, and the nature of the tasks you want to accomplish. Here are some widely recognized AI tools and platforms across different categories:

Natural Language Processing (NLP) and Text Analysis:

1. OpenAI GPT (Generative Pre-trained Transformer) - For natural language understanding and generation tasks.
2. BERT (Bidirectional Encoder Representations from Transformers) - Especially useful for language understanding tasks.
3. NLTK (Natural Language Toolkit) - Python library with modules for text processing tasks like tokenization, tagging, and parsing.

Machine Learning and Data Science:

1. TensorFlow - An open-source machine learning framework by Google for building and training machine learning models.
2. PyTorch - A deep learning framework by Facebook's AI Research lab that provides flexibility and speed.
3. Scikit-learn - A powerful Python library for classical machine learning algorithms and data preprocessing.

Computer Vision:

1. OpenCV (Open Source Computer Vision Library) - For real-time computer vision tasks, image processing, and machine learning.
2. TensorFlow Object Detection API - A framework built on TensorFlow for building and deploying object detection models.
3. YOLO (You Only Look Once) - A real-time object detection system for detecting multiple objects in real-time.

Conversational AI and Chatbots:

1. Dialogflow (by Google) - A platform for building conversational interfaces and chatbots using natural language understanding.
2. Microsoft Bot Framework - A comprehensive framework for building and deploying AI-powered chatbots across multiple channels.
3. Rasa - An open-source framework for building conversational AI assistants and chatbots.

AI Platforms and Services:

1. AWS AI Services - Amazon's suite of AI services including text-to-speech, translation, image recognition, etc.
2. Azure AI - Microsoft's AI platform offering various AI services, machine learning tools, and APIs.
3. Google AI Platform - Google Cloud's suite of AI tools and services including machine learning, natural language processing, and computer vision.

Data Analytics and Business Intelligence:

1. Tableau - A powerful data visualization tool that now includes AI-powered features for data analysis.
2. Power BI - Microsoft's business analytics service providing interactive visualizations and business intelligence capabilities.
3. Qlik - A data analytics platform that includes AI-driven insights and data visualization capabilities.

Recommendation Engines:

1. Apache Mahout - A distributed linear algebra framework and recommendation engine.
2. TensorFlow Recommenders - A TensorFlow-based library for building recommendation systems.
3. Surprise - A Python scikit for building and analyzing recommender systems.

AI for Voice Recognition:

1. Google Speech-to-Text and Text-to-Speech - For converting speech into text and vice versa.
2. IBM Watson Speech to Text and Text to Speech - IBM's AI services for speech recognition and synthesis.
3. Mozilla DeepSpeech - An open-source speech-to-text engine based on deep learning.

Autonomous Systems and Robotics:

1. ROS (Robot Operating System) - A flexible framework for writing robot software.
2. Unity ML-Agents - Unity's toolkit for developing intelligent agents (such as robots) using machine learning.
3. PyRobot - A Python-based robot control toolbox developed by Facebook AI Research.

AI Tools for Business Applications:

1. IBM Watson Studio - A comprehensive platform for AI and data science.
2. Alteryx - A data blending and advanced analytics platform with AI and machine learning capabilities.
3. H2O.ai - An open-source AI and machine learning platform for enterprise use.

Personal Assistants and Smart Devices:

1. Amazon Alexa Skills Kit - For building voice-controlled applications for Alexa-enabled devices.
2. Google Actions - For building conversational actions for Google Assistant.
3. Apple SiriKit - Apple's framework for integrating apps with Siri.

Ethics and Bias Mitigation:

1. IBM AI Fairness 360 - A toolkit for detecting and mitigating bias in AI models.
2. Google AI Ethics - Guidelines and tools for responsible AI development.
3. Microsoft Responsible AI - Framework and tools for ensuring ethical AI practices.

Emerging Tools and Platforms:

1. Fast.ai - A high-level deep learning library built on top of PyTorch for quick experimentation.
2. AI2 (Allen Institute for AI) - Research institute providing AI tools and resources.
3. Baidu AI Cloud - Baidu's suite of AI services including image recognition, NLP, and more.

Choosing the Right Tool:

• Evaluate Needs: Determine the specific tasks you want to accomplish with AI.
• Technical Skills: Consider your team's expertise and the learning curve of the tools.
• Scalability and Integration: Ensure the tool fits into your existing infrastructure and scales as needed.
• Cost and Support: Evaluate the cost of the tool or platform and the support available.

Ultimately, the "best" AI tool depends on how well it meets your specific requirements and integrates with your workflow. Experimentation and prototyping with different tools can help you find the best fit for your projects.