Fourth Industrial Revolution

In this post, I explain Introduction to Prompt Engineering for ChatGPT, Key Concepts and Prompt Types (such as zero-shot, few-shot, chain-of-thought prompting), Best Practices, Advanced Prompt Engineering Tactics, Prompt Engineering for Coding and Testing, Multi‑modal and Complex Prompts and Evaluating and Iterating Prompts. You can test your knowledge of Prompt Engineering by attempting the Quiz after every set of Questions and Answers. If you want my complete Prompt Engineering for ChatGPT document that additionally includes the following important topics, you can message me on LinkedIn : Prompt Engineering Tools and Frameworks (GitHub repositories, APIs), Ethics and Prompt Safety, Use Cases and Workflows and Interview Preparation and Prompt Engineering Quiz. Question : What is prompt engineering for ChatGPT? Answer : Prompt engineering for ChatGPT is the deliberate design and structuring of input text to guide the model’s behavior toward desired outputs. By crafting precise...

In this post, I explain What is LLM?, Language Modeling Basics, Tokenization & Words in LLMs, Neural Network Foundations, Transformer Architecture, Scaling: Parameters, FLOPs, Emergent Abilities, Architectural Variants, Training Paradigms, Sampling & Decoding Techniques, In-context Learning & Prompting, Hallucinations, Bias & Reliability, Explainability & Interpretability, Retrieval-Augmented Generation RAG, Multimodality & Multimodal LLMs MLLMs, and Domain-Specialization & Fine-tuning. If you want my complete Large Language Models (LLM) Concepts document that additionally explains the following topics, please message me on LinkedIn : Top Models Overview (GPT-series, BERT family, PaLM, LLaMA, Claude, Gemini), Prompt Engineering Strategies, LLM Usage Patterns, Best Practices for Reliability, Efficiency Optimization and Integration & Tooling Question : What is a Large Language Model (LLM)? Answer : A Large Language Model (LLM) is a type of neural network...

Summary: The Fourth Industrial Revolution has the convergence of advanced technologies, including Artificial Intelligence (AI) and Internet of Things (IoT). While IoT devices generate vast amounts of data, AI can analyze and act upon the IoT data, which enables automation. In this blog post, I explore how AI and IoT complement each other and provide examples of how they are being used in the Fourth Industrial Revolution, also known as Industry 4.0 . How AI and IoT complement each other : IoT devices generate massive amounts of data, but analyzing and acting upon this data in real-time is complex. However, AI, specifically artificial neural networks, can be trained to analyze IoT data and identify patterns and anomalies, enabling intelligent connectivity and automation. Examples of AI and IoT working together In manufacturing, sensors embedded in machines can collect data such as temperature, pressure and vibration. AI algorithms can analyze this data and identify patterns that indi...