Watch this introductory video from Wharton School about AI, its history and impacts on education.
Whether you're using a voice assistant like Alexa or Siri, getting personalized recommendations in social media and streaming platforms, or interacting with a chatbot during a purchase, Artificial Intelligence (AI) is becoming part of everyday life. But what exactly is AI?
At its core, AI is a branch of computer science focused on building systems that can perform tasks typically requiring human intelligence. These systems are designed to act with autonomy (making decisions without human input) and adaptivity (learning and improving from data). Because the field is evolving so quickly, there’s no single agreed-upon definition of AI. This is partly because the boundaries of what counts as “intelligent” shift over time. Tasks once considered uniquely human, like recognizing faces or translating languages, are now routinely and easily done by machines.
One of the most exciting developments in recent years is Generative AI. It is a type of AI that creates completely new content in different formats, such as text, images, audio, and code. Unlike traditional AI systems that classify or predict based on existing data, generative models (like ChatGPT or DALL-E) generate outputs based on patterns they’ve learned but looking completely new. This “creativity” capacity is now what most people would consider intelligence.
The world of AI comes with a lot of new vocabulary. To help you feel more confident navigating conversations and tools, here’s a quick guide to the most common terms:
| Term |
Definition |
|---|---|
| Artificial Intelligence (AI) |
The broad field of computer science focused on creating systems that simulate human intelligence. |
| Machine Learning (ML) |
A subfield of AI where computers learn from data to improve performance on a task. |
| Generative AI |
AI that creates new content based on patterns learned from data. |
| Natural Language Processing (NLP) |
A branch of ML focused on understanding and generating human language. |
| Large Language Models (LLMs) | AI models trained on massive text datasets to generate human-like responses. Examples: ChatGPT, Claude, Gemini. |
| Productivity Tools | Everyday software enhanced with AI, like smart email suggestions or design assistants. |
| Multimodal AI | AI systems that can process and generate multiple format of data: text, images, audio, and video. |
| Reinforcement Learning | A type of ML where agents learn by trial and error, receiving rewards or penalties for actions, often used in robotics and game-playing AIs. |
| Neural Networks (NN) | Algorithms structured like the human brain, used in deep learning to recognize patterns. |
| Deep Learning | A type of ML that uses neural networks to process complex data like images or speech. |
| IoT (Internet of Things) | A network of connected devices that collect and exchange data, often enhanced with AI for automation. |
| Industry 4.0 | The current industrial revolution, driven by smart technologies like AI, IoT, robotics, and automation. |
Understanding AI isn’t just about knowing what it is: it’s about thinking deeply about how it works, what it reflects, and what it means for society.
The Turing Test, proposed in 1950, suggested that if a machine could carry on a conversation indistinguishable from a human, it could be considered intelligent. While this idea helped shape the field, it also reveals a limitation: AI can mimic human behavior without truly understanding it. This is especially true for today’s narrow AI systems, which are designed for specific tasks like translation or image generation. In contrast, general AI, the kind that could reason and learn across domains like a human, remains a theoretical goal.
Generative AI, despite its impressive outputs, doesn’t create from scratch. It learns from massive datasets and predicts what comes next based on patterns in that data. This means it can also reproduce the biases present in its training material. Biases related to race, gender, culture, and more. These aren’t just technical issues; they’re social and ethical ones. When AI is used in hiring, education, or justice systems, these biases can have real-world consequences.
AI literacy isn’t just about fancy math or using tools, but it’s about asking philosophical questions: What is the impact of this results? Who built this system? What data was it trained on? What assumptions are embedded in its design? As you explore AI tools and platforms, keep a critical mindset. The future of AI is not just about automation, it’s about responsibility, fairness, and the kind of world we want to shape with every prompt we make.
Watch this introductory video from Wharton School about AI, its history and impacts on education.