Artificial intelligence concepts: Understand the basics
Artificial intelligence (AI) is a field of computer science that enables machines to perform tasks learning from their environment, evolving their behavior based on patterns, adapting, and making decisions autonomously [1].
AI systems attempt to mimic how the human brain functions by simulating processes similar to its development. For instance, voice assistants like Siri or Alexa can understand and respond to voice commands through AI, improving their responses over time based on user interactions.
As AI revolutionizes different industries, understanding its core concepts is vital. In this post, we'll break down three essential ideas behind AI. Also, if you're interested in learning more about how AI can benefit businesses, check out our related post, "Advantages of AI in Business."
Concept #1: Machine learning
Machine learning revolves around algorithms that allow machines to learn from data and make decisions without being explicitly programmed for every situation. These algorithms identify patterns, trends, and insights from data, then use this knowledge to predict outcomes or make informed decisions [2].
Rather than following fixed rules, machine learning systems learn by example. For instance, a machine learning model designed for email spam detection can be trained by analyzing thousands of emails. It learns to differentiate between spam and non-spam messages based on characteristics it observes, improving its accuracy over time.
Machine learning has a wide range of applications, from recommendation engines on streaming services to detecting fraudulent transactions in banking. This concept is the engine driving many AI systems that rely on large datasets to make decisions and predictions.
Concept #2: Generative AI
Generative AI is all about creating new content based on the data it has learned from. It is capable of producing original material, such as text, images, and even videos [2]. This technology works by recognizing and studying patterns in large datasets, which allows it to generate content that closely resembles its input.
For example, a generative AI model trained on a vast number of images can create new, realistic-looking images that haven't existed before. Chatbots that can write articles or artwork-generating programs are real-world examples of generative AI in action.
Concept #3: Neural networks
Neural networks are inspired by the structure of the human brain. They consist of layers of nodes, which are interconnected and designed to process information in a way that resembles the neural pathways in the brain [3]. These networks enable machines to recognize and learn from patterns within data, much like the brain learns from sensory input.
Neural networks excel at tasks like image and speech recognition. For example, when a neural network is trained with thousands of images of dogs, it learns to detect specific features, such as ears and tails, allowing it to identify a dog in a new image. These networks play a vital role in many modern AI applications, from voice recognition in virtual assistants to predictive text in messaging apps.
Conclusion
By understanding key AI concepts like machine learning, generative AI, and neural networks, we can better appreciate the potential and challenges of this technology. Don't forget to check out our other post, "Artificial intelligence in education," to explore how AI is transforming the learning field.
References
[1] Understanding basic principles of artificial intelligence: a practical guide for intensivists, 2022. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9686179/
[2] MIT Sloan Teaching & Learning Technologies Glossary of Terms: Generative AI Basics. https://mitsloanedtech.mit.edu/ai/basics/glossary/
[3] IBM, What is artificial intelligence (AI)?, 2024. https://www.ibm.com/topics/artificial-intelligence
