AI and ML Terminologies: Part 1

1. What is Artificial Intelligence (AI)?

    AI is the broader field focused on creating systems that can perform tasks that typically require human intelligence. This includes reasoning, problem-solving, understanding natural language, and perception. AI can be rule-based (using predefined rules to make decisions) or data-driven.

        Choose the AI applications which you have used or are aware of:

        • Self-driving cars (Tesla)
        • Smart home devices(Amazon Alexa)
        • Google Assistant
        • Apple Siri
        • IBM Watson

        2. What is Machine Learning (ML)?

        ML is a subset of AI that focuses specifically on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. ML algorithms improve their performance over time as they are exposed to more data.

        Choose the ML applications which you have used or are aware of:

        • Spam filtering (Gmail)
        • Recommendation systems (Netflix)
        • Fraud detection (banks)
        • Predictive maintenance
        • Personalized ads (Facebook)

        3. What is Deep learning (DL)?

        DL is a subset of machine learning that uses neural networks with many layers (hence “deep”) to analyze and interpret complex data. It mimics the way the human brain processes information, making it particularly effective for tasks like image and speech recognition, natural language processing, and more.

        Choose the DL applications which you have used or are aware of:

        • Image classification (Google Photos)
        • Object detection (YOLO)
        • Face recognition (iPhone)
        • Medical image analysis
        • Deepfake generation

        4. What is Natural Language Processing (NLP)?

        Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language in a way that is both meaningful and useful.

        Meaningful: This refers to the ability of NLP systems to grasp the context, nuances, and intent behind words. For example, understanding that “bank” can refer to a financial institution or the side of a river, depending on the context.

        Useful: This means that the output or insights generated by the NLP system should be relevant and actionable. For instance, if a customer service chatbot accurately identifies a customer’s question about a product, it should be able to provide helpful information or solutions.

        Choose the NLP applications which you have used or are aware of:

        • Chatbots (ChatGPT)
        • Sentiment analysis (Twitter)
        • Language translation (Google Translate)
        • Text summarization
        • Speech recognition (Cortana)

        5. What is Generative AI(GenAI)?

        GenAI refers to a class of artificial intelligence models that can create new content such as text, images, audio, video, and more, by learning from existing data. Unlike traditional AI systems that classify or predict based on input, generative AI models produce new data that is similar to the data they were trained on.

        Choose the GenAI applications which you have used or are aware of:

        • Text generation (ChatGPT)
        • Image generation (DALL·E)
        • Code generation (GitHub Copilot)
        • Music composition (AIVA)
        • Video generation (Runway ML)

        Happy Learning !!!

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