AI vs ML vs DL vs Generative Ai

Summary

In this video, Kush Naak explores the distinctions between AI, machine learning, deep learning, and generative AI, highlighting their roles and applications. He explains that AI focuses on building autonomous applications, while machine learning, as a subset of AI, employs statistical tools for data analysis. Deep learning, in turn, mimics the human brain using multi-layered neural networks. Kush emphasizes generative AI's capacity to create new content based on extensive training data, detailing its models and the competitive landscape among leading companies like OpenAI and Google. He also touches on the LangChain framework for developing AI applications.

Takeaways

• 😀 AI (Artificial Intelligence) aims to create applications that can perform tasks independently without human intervention.
• 🤖 Machine Learning (ML) is a subset of AI focused on statistical tools for tasks like data analysis and predictions.
• 🧠 Deep Learning (DL) is a further subset of ML that mimics the human brain through multi-layered neural networks.
• 📊 ML provides essential tools for the complete lifecycle of a data science project, including data transformation and feature engineering.
• 🚗 Examples of AI applications include recommendation systems like Netflix and self-driving cars that operate autonomously.
• 🔍 Deep Learning techniques include ANN, CNN for image processing, and RNN for text and time series data.
• ✍️ Generative AI is a subset of deep learning that generates new content based on previously learned data.
• 📚 There are two main types of models in AI: discriminative models for classification and generative models for content generation.
• 🌐 Large Language Models (LLMs) are advanced models developed by companies like OpenAI, Google, and Meta, trained on vast datasets.
• 🔗 The LangChain framework facilitates the development of applications using LLMs, enabling automated responses and various use cases.

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