AI and ChatGPT: Introduction to Real-life Application

Trace the evolution of GPT models from GPT-1 to GPT-3.5, with generative pre-training transformer and data like Common Crawl, Book Corpus, and Wikipedia, boosted by reinforcement learning from human feedback.

Explore how ChatGPT uses reinforced learning with human feedback, detailing supervised fine-tuning, reward modeling, and PPO to improve responses, while noting limitations and biases.

Compare chatbots ChatGPT, Bing AI, and Google Bot, outlining free vs paid versions, knowledge cutoffs, web browsing, plugins, and practical pros and cons for real-world use.

Explore how prompts initiate chat bot conversations, distinguish inputs and outputs, and master prompt engineering to drive effective AI across customer support, data analysis, corporate training, and health care.

Learn how tokens differ from words, how tokenization works, and why token limits constrain prompts and memory in ChatGPT 3.5 and 4.0.

Learn a practical toolkit for prompt engineering, defining clear objectives, format, and roles, while understanding AI chatbots' capabilities, limitations, and how to avoid hallucinations.

Explore real-life applications of ChatGPT, from summarizing reports and creating Excel formulas to boosting business efficiency with strategies for a finance YouTube channel and a clothing store.

Explore the limitations and biases of chatbots like ChatGPT, including reliance on probabilities, lack of thinking, and potential for hallucinations, while noting differences from human creativity, emotion, and ethical judgment.

Recognize that artificial intelligence relies on pattern recognition, not true autonomy. Acknowledge that primary risks arise from people who misuse AI, like scams with ChatGPT, and from evolving job categories.