ChatGPT & Generative AI Tools Collaborative Guide: Home

This collaborative guide offers an overview of artificial intelligence, including its history, key concepts, and terminology, with a focus on generative AI, its ethical considerations, and its applications in academics.

Artificial Intelligence (AI) enables computers to perceive, reason, learn, interact, problem-solve, and exercise creativity, allowing them to perform tasks traditionally associated with human intelligence. Recognized as a foundational technology—on par with electricity and the internet—AI is driving advancements across industries and scientific fields.

• 1950s: AI emerged as a formal field, with Alan Turing introducing the Turing Test to evaluate machine intelligence.


• 1956: John McCarthy coined the term "Artificial Intelligence" at the Dartmouth Conference, establishing AI as an academic discipline.


• Present: AI has evolved from early theoretical concepts to a transformative force in higher education, revolutionizing research, teaching, and innovation.

AI Categories	Example Models	Use Cases
Generative AI	GPT-4o, DALL-E, GANs	AI models that generate text, images, music, code, or videos based on learned patterns.
Predictive AI	Fraud detection, stock market forecasting	AI models that analyze historical data to predict future trends or behaviors.
Rule-Based AI	Chatbots with scripted responses, expert systems in healthcare	AI systems that operate based on if-then rules rather than learning from data.
Computer Vision AI	Facial recognition, autonomous vehicles	AI that processes and interprets visual data.
Reinforcement AI	AlphaGo, robotics	AI that learns through trial and error to optimize decision-making.

 

Generative AI (GAI) refers to artificial intelligence systems that create new content, such as text, images, music, and video, by learning patterns from vast datasets. It has evolved beyond simple chatbots into agentic AI, where models can autonomously complete tasks, interact dynamically, and refine outputs based on user feedback, making it a transformative force across industries.

GAI Categories	Examples	Use Cases
Generative Adversarial Networks (GANs)	Artbreeder, FaceApp, etc.	Used for realistic image generative and deepfake creation.
Variational Autoencoders (VAEs)	Adobe Photoshop Neural Filters, AI-generated music	Helps in compressing and reconstructing data while introducing variations.
Autoregressive Models	Google Bard, Bing chat, GitHub, Copilot, conversational AI	Used in text generation, such as GPT models for conversational AI.
Diffusion Models	Stable Diffusion, MidJourney, Google's VideoPoet	Generates high-quality images by refining noisy data over multiple steps.
Tranformer-Based Models	Google Search, Bing, DALL-E, Google Translate, DeepL	Includes models like GPT-4o and DALL-E, which generate text, images, and code.