Learn with Purpose
Build your AI literacy through structured learning paths and mental models
Our learning approach combines timeless mental models with practical applications, helping you develop both theoretical understanding and real-world skills. Choose your path below to begin.
Mental Models
Timeless frameworks for thinking critically about AI and its applications
Practical Application
Real-world case studies and scenarios to test your understanding
Ethical Considerations
Frameworks for responsible AI development and deployment
Continuous Learning
Stay updated with evolving AI concepts and challenges
What is Artificial Intelligence?
Artificial intelligence refers to computer systems that can learn, reason, and make decisions rather than simply following prewritten instructions. Instead of being told exactly what to do at every step, AI systems improve by analyzing data, recognizing patterns, and adjusting their behavior over time. Most AI today is designed for narrow purposes. It powers tools like search engines, recommendation systems, voice assistants, fraud detection, and image recognition. These systems excel at specific tasks but lack a general understanding. A more ambitious idea, often called artificial general intelligence, would involve machines that can reason across domains the way humans do. That level of AI remains theoretical and has not yet been achieved.
What is Computer Vision?
Computer vision is a field of artificial intelligence (AI) that enables computers to interpret and make decisions based on visual data. It seeks to replicate the human ability to see and understand the world through the use of digital images, videos and other visual inputs. By using a range of techniques, such as image processing, machine learning and deep learning, computer vision systems are able to identify objects, track movement, analyze scenes and even recognize facial expressions. The ability to bridge the gap between physical and digital realms has made computer vision a cornerstone of modern AI systems.
What is a Large Language Model?
A large language model is a type of artificial intelligence that employs deep learning to comprehend and produce human language. These models are called “large” because of the vast amount of information they must be trained on to learn how to perform a wide range of language-related tasks, such as responding to queries, translating languages and creating other forms of content. LLMs are sometimes referred to as neural networks (NN) — computer systems that emulate the human brain — because they rely on a network of neuron-like nodes. They are constantly being trained and upgraded to improve their problem-solving abilities and make them as fluid and effective as possible.
What is Natural Language Processing?
Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to interpret and respond to human language in contextually meaningful ways. The process, which combines machine learning and computational linguistics gives computers and other digital devices the ability to rapidly process and understand large volumes of information and provide meaningful responses to queries. NLP teaches computers how to understand and communicate using our language. It improves the way humans and machines interact by enabling digital devices to function much like our brains, with processing programs and microphones taking the place of eyes and ears. It is capable of automating and enhancing many aspects of our increasingly complicated world.
What are Neural Networks?
Neural networks are computer systems inspired by the human brain. They are designed to recognize patterns and process data through interconnected layers of nodes or "neurons." Each layer processes input, adjusting weights (importance) and thresholds (activation levels). Over multiple layers, these networks are trained to understand complex relationships and improve their performance in tasks such as image and speech recognition.