Artificial Intelligence (AI) is one of the most talked about topics in the tech world, but do you have a clear understanding of it? To help make AI more accessible, created an AI glossary of terms that frequently come up in conversations about AI and machine learning.
Algorithm: A structured instruction set to perform a specific task or solve a problem. Algorithms play a fundamental role in AI, determining how machines make decisions and solve problems.
Artificial Neural Network (ANN): A mathematical model (inspired by human brain functioning) used in deep learning to perform complex pattern recognition and data processing tasks.
Automation: Replacing or supporting human tasks with computer or mechanical systems. In the context of AI, automation refers to using AI to perform tasks without human intervention.
Convolutional Neural Network (CNN): A neural network designed specifically for image processing and visual pattern recognition.
Deep Learning: A sub-discipline of machine learning that uses deep artificial neural networks to model and solve complex problems. Deep learning is particularly suited to computer vision and natural language processing tasks.
Generative Neural Network (GAN): An AI model composed of two competing neural networks, one generating data and the second evaluating it, used to generate realistic content, such as images and videos.
GPT-4 (Generative Pre-trained Transformer 4): A later version of the GPT-3 multimodal language model of the GPT Chat conversational agent, version 4 being the latest.
Language model: A statistical or machine-learning model designed to predict the probability of a sequence of words or characters. Language models are used in a variety of natural language processing tasks.
Large Language Model (LLM): A machine learning model that aims to generate coherent, relevant text. These models are often used in applications such as automatic authoring and content generation.
Machine Learning: Machine learning is a branch of AI that focuses on developing algorithms and models that enable machines to learn from data. Rather than being explicitly programmed, machines “learn” to improve their performance from experience.
Natural Language Processing (NLP) : A field of AI that focuses on the interaction between computers and human language. NLP aims to enable machines to understand, interpret and generate human language meaningfully.
Overfitting: A phenomenon where an AI model becomes too specific to the training data and fails to generalize correctly to new data.
Prompt: A short text or instruction given to an AI model to solicit a specific response. AI models then generate responses based on these prompts.
Python: A popular programming language used in application development, particularly in the field of artificial intelligence, due to its simplicity and wealth of dedicated libraries.
Recurrent Neural Network (RNN): A type of neural network adapted to processing sequences of data, such as language, by retaining a memory of previous states.
Symbolic AI: Knowledge-based artificial intelligence focuses on the explicit representation of knowledge and symbolic logic to solve complex problems.
Training Data: The information used to train an AI model, usually consisting of real, labeled data, allowing the model to learn from the examples.
The Impact of AI
Without context, some of these AI terms may seem unclear. However, when combined with some basic understanding of the way machine learning works, it can be a powerful tool.
Moreover, as AI continues to propel its way into our lives both professionally and personally, it is increasingly becoming a disruptor all around us. It is present in a variety of industries which makes it imperative that we know the language of AI as it makes its impact.