AI systems learn and improve through exposure to vast amounts of data, identifying patterns and relationships that humans may miss. This learning process often involves algorithms, which are sets of rules or instructions that guide the AI's analysis and decision-making.
AI Definition: AI is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience ( Copeland, 2020). In simple terms these machines can learn, think, solve problems, and understand language just like people do.
Generative AI: is a type of artificial intelligence that can create new content. It can write text, make images, compose music, or even generate videos. Some of the examples of Generative AI are ChatGPT, QuillBot and Grammarly.
Machine Learning (ML): is a branch of artificial intelligence that enables systems to learn from data, identify patterns, and enhance their performance on specific tasks over time—without being explicitly programmed. Common applications include spam detection, fraud prevention, and delivering personalized content recommendations.
Natural Language Processing (NLP): is a field within artificial intelligence that focuses on enabling machines to comprehend, interpret, and generate human language. It underpins technologies such as virtual assistants, language translation tools, and sentiment analysis systems.
Generative AI: is a rapidly advancing field within artificial intelligence that focuses on creating original content such as text, images, and code. Tools like ChatGPT, Copilot, and Google Gemini are prominent examples of this technology.
Deep Learning: is an advanced subset of machine learning that leverages neural networks with multiple layers—hence the term "deep"—to interpret and process complex data. It is particularly effective in areas such as image recognition, speech analysis, and natural language processing. Technologies like facial recognition and autonomous vehicles heavily depend on deep learning models.