Foundational concepts of AI
AI continues to shape industries and influence new aspects of daily life. Having a solid grasp of fundamental AI concepts will help you understand how AI works and how you can integrate AI into your work. Familiarity with AI's foundational concepts empowers you to navigate complex discussions, critically assess AI applications, and harness AI‘s potential for innovation.
Below is a list of foundational concepts that are important for understanding AI:
Data. Data is the raw material that AI systems use to analyze and make predictions. The data set used to train algorithms directly impacts the accuracy of the trained AI models. Hence, high-quality data that are diverse and representative are essential to develop advanced AI models.
Algorithm. An algorithm is a set of step-by-step instructions that guide an AI model to perform tasks. Algorithms are used in data processing, feature extraction, model training, and decision-making.
Machine learning. Machine learning is the process by which machines learn from data and improve their performance over time. Machine learning utilizes different types of techniques such as supervised learning, unsupervised learning, and reinforcement learning.
Model. An AI model is a program (or algorithm) that results from training on a given set of data. In other words, the AI model represents the patterns and relationships that the machine learning algorithm discovered during the training.
Feature extraction. Feature extraction involves selecting the most relevant attributes or features from the given data, so that they can be fed into the model. Feature extraction aims to improve an AI model's performance by reducing the number of features that the model needs to process.
Feature engineering. Feature engineering is the process of selecting and modifying features (attributes and characteristics) from data to enhance an AI model's performance and improve the AI model's ability to capture patterns and relationships within the data.
Prediction and classification. Prediction is the process of guessing future outcomes, while classification is the process of assigning labels or categories to data points based on what the AI models have learned from the training data.
Neural networks. Neural networks consist of interconnected nodes in a layered structure. This structure is loosely inspired by the structure of the human brain. Neural networks are particularly effective for tasks like image and speech recognition.
Training and testing. AI models are trained on a subset of data and tested on another subset of data to evaluate their performance. Training and testing ensure that the AI model can generalize new data.
Overfitting and underfitting. Overfitting occurs when an AI model becomes too specialized to the training data and performs poorly with new data. Underfitting happens when an AI model is too simple to capture the underlying patterns in the data.
Optimization. Optimization is the process of finding the best parameters for an AI model. Optimization often involves adjusting an AI model's internal settings to minimize errors.