AI GLOSSARY

A neural network is a computer system designed to mimic how the human brain works. It consists of layers of connected nodes (like neurons) that process data, learn patterns, and make decisions. Neural networks are used in tasks like recognizing images, translating languages, and making predictions by adjusting connections based on the data they receive.

A neuron in a neural network is a basic unit that processes information. It receives input, applies weights to the input, passes the result through an activation function, and produces an output. Similar to a brain cell, it's part of a larger network that learns patterns from data.

Parameters in a neural network are the values that the model learns during training. They include weights and biases that adjust how input data is processed to make predictions. By updating these parameters, the neural network improves its accuracy over time.

Weights and biases are key components in a neural network. Weights determine the importance of each input in the network, while biases help shift the output to better fit the data. Together, they are adjusted during training to improve the network's ability to make accurate predictions.

Transformers are a type of model used in machine learning, especially for tasks involving language. They use attention mechanisms to focus on important parts of input data, like words in a sentence, to understand context and relationships. Transformers are highly effective for tasks like translation, text generation, and language understanding, and are the foundation for models like GPT and BERT.

Large Language Models (LLMs) are advanced AI models trained on vast amounts of text data to understand and generate human language. They can perform tasks like answering questions, writing text, and translating languages by recognizing patterns in the data they've learned.

Small Language Models (SLMs) are AI models designed to understand and generate text but are smaller and less complex than Large Language Models (LLMs). They typically have fewer parameters, making them faster and more efficient for specific tasks, though they may not capture as much nuanced language understanding as larger models. SLMs are often used in applications where resources are limited or where quick responses are essential, such as chatbots or simple text generation tasks.

Small Language Models (SLMs) have fewer parameters and are simpler, making them faster and more efficient for specific tasks but less capable of understanding complex language nuances. In contrast, Large Language Models (LLMs) have millions or billions of parameters, allowing them to perform better on a wider range of tasks, including complex text generation and contextual understanding. While SLMs are ideal for lightweight applications, LLMs are better suited for demanding applications that require deeper language comprehension.

Model development is the process of creating and training a machine learning or AI model to perform specific tasks. It involves several steps, including defining the problem, selecting the right algorithms, preparing and processing data, training the model on that data, and evaluating its performance. The goal is to build a model that can accurately make predictions or decisions based on new input data.

Model training is the process of teaching a machine learning or AI model to make predictions or decisions based on data. During training, the model learns patterns by adjusting its parameters (like weights and biases) using labeled data, where the correct outputs are known. This process continues until the model performs well on the training data and can generalize effectively to new, unseen data

Model retraining is the process of updating an existing machine learning or AI model with new data to improve its performance or adapt to changes. As more data becomes available or as the environment changes, retraining helps the model learn from this new information, ensuring it remains accurate and relevant. This is important for maintaining the model's effectiveness over time in dynamic situations.

AI model deployment is the process of integrating a trained machine learning or AI model into a production environment where it can be used to make predictions or decisions in real-time. This involves setting up the necessary infrastructure, such as servers or cloud services, to host the model and ensuring it can receive input data and return results efficiently. The goal of deployment is to make the AI model accessible and usable for end-users or applications.

Transfer learning is a technique in machine learning where a model trained on one task is reused or fine-tuned for a different but related task. Instead of starting from scratch, the model leverages the knowledge it gained during its initial training to improve performance and reduce training time on the new task. This approach is especially useful when there is limited data available for the new task, as it allows the model to adapt its learned features to new situations effectively.

Pre-training is the initial phase of training a machine learning or AI model where it learns general features and patterns from a large dataset without a specific task in mind. During this phase, the model develops a foundational understanding of the data, which can then be fine-tuned for specific tasks in subsequent training phases. Pre-training helps improve the model's performance and efficiency when it is later adapted for more specialized applications.

Pre-trained models are machine learning or AI models that have already been trained on large datasets to learn general features and patterns. These models can be used as a starting point for specific tasks, allowing users to save time and resources. Instead of training a model from scratch, users can fine-tune a pre-trained model on their own data, which often leads to better performance and quicker results for tasks like image classification or natural language processing.

Continuous pre-training is an ongoing process where a machine learning or AI model is regularly updated with new data after its initial training. This approach allows the model to keep learning and adapting to new information and changing environments over time. By continuously incorporating fresh data, the model can improve its performance and maintain its relevance for specific tasks or applications.

In the context of AI and machine learning, "spectrum" typically refers to a range or continuum of models, techniques, or features that vary in complexity, size, or capabilities. For example, it can describe the diversity of language models, from small, lightweight models suitable for specific tasks to large, complex models capable of understanding and generating human language. The spectrum concept helps in choosing the right model or approach based on specific needs, resources, and performance requirements.