AI Glossary for Beginners
Start your AI journey with A-Z AI terms and build solid knowledge in no time.
Artificial intelligence seems complicated and futuristic to those starting their journey in this field. If you feel overwhelmed by learning about this new technology that has become integral to our everyday lives, you’ve come to the right place. FSA’s AI glossary is the perfect place to start your AI journey, with every AI-related term explained simply and comprehensively.
Our team of experts gathered all the relevant AI terms for beginners looking to broaden their knowledge and gain confidence in learning the critical topics that would shape the world’s future. This glossary of essential terms related to AI will help you develop your AI vocabulary and prepare you for AI training courses, certification programs, and AI jobs.
A-Z AI Definitions
Currently, there are 25+ AI definitions in this glossary, let’s get started.
A
Artificial General Intelligence
Artificial General Intelligence, or AGI, is one of the exciting terms in an AI glossary that represents a vision for the future of AI. It is the next stage in the evolution of AI, where artificial intelligence can do any task that requires cognitive abilities and human intelligence.
Artificial Neural Network
Artificial Neural Network (ANN) is a core component of deep learning algorithms that resembles the processing capabilities of the human brain. The structure of ANNs includes nodes, similar to neurons or the core blocks of the human nervous system.
Attention Mechanisms
Attention mechanisms are an essential addition to every modern AI dictionary because they are integral to the functioning of neural networks. The primary goal of attention mechanisms focuses on drawing the attention of models towards important parts of the input to generate the output.
B
Bias
Bias is a notable setback for the adoption of AI systems, as AI models can make different assumptions about the data they analyze. Machine learning algorithms can make assumptions according to the underlying data distribution in the training data, thereby leading to unethical, incorrect, or offensive outputs.
Back Propagation
Backpropagation is a trusted algorithm used to train neural networks. The meaning of backpropagation is the calculation of the gradient of the loss function in comparison to network weights.
C
Convolutional Neural Networks
Convolutional Neural Networks are another important component in the world of deep learning. CNNs are neural networks that have one or multiple layers and serve as the best picks for image recognition and image processing applications.
Conversational AI
Conversational AI is another common addition among AI terms that can help developers create conversational user interfaces, virtual assistants, and chatbots for different use cases. You can also find developer AI on conversational AI platforms that enable third parties to extend the functionalities of the platform.
Chain of Thought
Chain of Thought is a crucial concept underlying the working mechanism of AI models. It refers to the sequence of steps that an AI model follows for reasoning to reach a specific conclusion.
CLIP
CLIP, or Contrastive Language-Image Pre-training, is a crucial term in any AI dictionary for beginners owing to its role in improving generative AI. OpenAI has created the CLIP model to connect images and text. It can use its capabilities to understand and generate different image descriptions.
D
Deep Learning
Deep learning is a subdomain of machine learning. It involves the training of neural networks featuring multiple layers, thereby empowering them to learn complex patterns. For example, deep learning can help with the classification of concepts from text, audio, or images.
Read moreData Augmentation
The significance of data augmentation in an AI glossary revolves around its ability to enhance the quality of AI models. Data augmentation involves improving the diversity and amount of data used in training sets of AI models. It consists of the addition of copies of existing data with minor modifications.
Diffusion
Diffusion is an essential technique in AI and machine learning that creates new data from real-world data. You can think of a diffusion model as a generative model with a neural network trained to predict the reverse process during the addition of noise to the data.
E
Embedding
Embedding is also one of the standard AI terms you must know as a beginner. It focuses on representing data in a new form, generally in a vector space. You must note that similar data points are likely to have the exact embeddings.
Expert Systems
Expert systems are the AI systems that provide the most effective solutions to complex issues in a particular domain.
F
Fine-tuning
Fine-tuning is a mandatory addition to any AI dictionary as it helps in expanding the utility of pre-trained AI models. The process of fine-tuning involves adjusting the parameters of a model with the help of smaller and task-centric datasets. As a result, it can learn task-specific patterns and achieve better performance on new tasks.
Forward Propagation
Forward propagation, or the opposite of backpropagation in neural networks, involves feeding the input data into the network. The input data then passes through all the layers, from input to hidden layers, and then to the output layer. The network also uses biases and adds weights to inputs alongside activation functions.
G
Generative Adversarial Networks
Generative Adversarial Networks, or GANs, are also notable entries in every modern AI glossary because of their capability to generate new data that is similar to existing data. GANs use two different neural networks, such as generators and discriminators, to create new data.
Generative AI
Generative AI is one of the most popular AI terms you will come across in the initial stages of learning AI. It represents a domain of AI that involves the development of models with the capability to generate new and original content, including text, images, and music.
Read moreGradient Descent
Gradient descent refers to an optimization method that ensures gradual adjustment of the parameters of a model according to the direction of maximum growth in the loss function.
L
Large Language Models
Large Language Models, or LLMs, are AI models that can use natural language understanding and natural language generation capabilities to create human-like text. LLMs use a broader dataset for training to achieve their desired objectives.
M
Multimodal AI
Multimodal AI is an essential trait in the domain of generative AI. It describes generative AI models that have the capability to understand and generate information across different types of data, including text, audio, videos, and images.
T
Transformer Architecture
Transformer architecture is an exceptional neural network architecture used to process sequential data. You are likely to find transformer architecture in generative AI tools such as ChatGPT, which work with natural language.