The Basics of Artificial Intelligence (AI) 

There’s a pretty good chance that you’ve heard about the advancements that are being made in the world of AI. Whether you find it exciting, scary, or both – it's a technology that is making huge strides and fast. If you’re still not quite sure what artificial intelligence is exactly, no worries, we’ve got you covered. In this blog post, we’ll provide a basic understanding of AI along with a glossary of terms, phrases, and concepts that we hope you’ll find useful in understanding artificial intelligence. 

What is AI? 

Artificial Intelligence (AI) is a branch of computer science that focuses on the development of intelligent machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. 

At a basic level, AI works by using algorithms and mathematical models to analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. The algorithms used in AI can be broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning. 

Supervised learning involves training an AI model on labeled data, where the correct answers are provided. The model learns to recognize patterns in the data and can then make predictions on new, unseen data. 

Unsupervised learning involves training an AI model on unlabeled data, where no correct answers are provided. The model learns to identify patterns and relationships in the data, allowing it to cluster similar data points and identify outliers. 

Reinforcement learning involves training an AI model to make decisions in a specific environment by providing rewards or punishments based on the model's actions. The model learns to optimize its decision-making process to maximize the reward it receives. 

Terms to Know 

This glossary provides an overview of some common artificial intelligence terms and concepts. It's designed to be accessible for those without a technical background but still provides a solid foundation for understanding the field of AI. 

1. AI (Artificial Intelligence) - The development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. 

2. Algorithm - A set of rules or instructions followed by a computer to solve a problem or perform a task. 

3. ANN (Artificial Neural Network) - A computing system inspired by the human brain's neural networks, designed to recognize patterns and learn from data. 

4. Chatbot - A computer program designed to simulate human-like conversations, often used for customer service or general information purposes. 

5. Computer Vision - A field of AI that enables computers to interpret and understand visual information from the world, such as images and videos. 

6. Data Mining - The process of discovering patterns, trends, and relationships in large sets of data using computational techniques. 

7. Deep Learning - A subset of machine learning that uses artificial neural networks to model and solve complex problems by automatically learning features and patterns from data. 

8. GAN (Generative Adversarial Network) - A class of AI algorithms that consist of two neural networks, a generator and a discriminator, which compete against each other to create new, realistic data samples. 

9. Machine Learning - A subset of AI that uses statistical techniques and algorithms to enable computers to learn and improve from experience without being explicitly programmed. 

10. NLP (Natural Language Processing) - A field of AI that focuses on the interaction between computers and humans through natural language, enabling computers to understand, interpret, and generate human language. 

11. Reinforcement Learning - A type of machine learning where an AI agent learns to make decisions by interacting with its environment and receiving feedback in the form of rewards or penalties. 

12. Robotics - A field that combines engineering, computer science, and AI to create machines capable of performing complex tasks autonomously or semi-autonomously. 

13. Supervised Learning - A type of machine learning where the AI is trained using labeled data, meaning the input data has a known output or result. 

14. Unsupervised Learning - A type of machine learning where the AI learns from unlabeled data, discovering patterns and relationships without prior knowledge of the output or result. 

15. Turing Test - A test devised by British mathematician and computer scientist Alan Turing to determine if a machine can exhibit intelligent behavior indistinguishable from that of a human. 

Wrapping IT Up 

AI is used in a wide range of applications, including image recognition, natural language processing, robotics, and autonomous vehicles. As the field of AI continues to advance, it has the potential to transform industries and revolutionize the way we live and work.  

At Cimatri, we understand that this is a technology that can seem overwhelming to implement, which is why it’s important to have a plan. That’s why we specialize in artificial intelligence strategy development for associations and non-profit organizations. Our team of AI experts will work closely with your team to develop a comprehensive AI strategy that aligns with your mission, goals, and objectives. Learn more.