Generative AI? GPT?
Why Should I care?

Glossary of AI Terms

GPT-4.0:

• Definition: GPT-4 (Generative Pre-trained Transformer 4) is the fourth generation of OpenAI’s language models, designed to understand and generate human-like text based on deep learning techniques.
• Application: Used in natural language understanding, text generation, translation, and more.

LLM (Large Language Model):

• Definition: A type of AI model trained on vast amounts of text data to understand, generate, and manipulate human language. These models are based on architectures like transformers.
• Application: Used for a variety of natural language processing (NLP) tasks such as text completion, question answering, and sentiment analysis.

Claude:

• Definition: Claude is a family of AI language models developed by Anthropic. Named presumably after Claude Shannon, the father of information theory, these models are designed for conversational and language understanding tasks.
• Application: Similar to other language models, Claude is used for generating text, answering questions, and other conversational AI applications.

Generative AI:

• Definition: A type of artificial intelligence that can generate new content, such as text, images, or music, based on its training data. These models create novel outputs rather than merely processing or analyzing existing data.
• Application: Used in creative industries, content creation, design, and any field where new data or content generation is valuable.

NLP (Natural Language Processing):

• Definition: A branch of AI focused on the interaction between computers and human language. It involves the ability to understand, interpret, and generate human language in a way that is both meaningful and useful.
• Application: Includes tasks such as machine translation, sentiment analysis, speech recognition, and text summarization.

Transformer:

• Definition: A type of neural network architecture introduced in the paper “Attention is All You Need” by Vaswani et al., which has become the foundation for many large language models, including GPT.
• Application: Used extensively in natural language processing for tasks like language translation, text summarization, and more due to its efficiency and scalability.

Deep Learning:

• Definition: A subset of machine learning involving neural networks with many layers (hence “deep”) that can learn and make decisions on their own by processing vast amounts of data.
• Application: Used in various AI applications, from image and speech recognition to natural language processing and beyond.

Fine-Tuning:

• Definition: The process of taking a pre-trained model and further training it on a more specific dataset to adapt it to a particular task or domain.
• Application: Enhances the performance of models in specific applications, such as adapting a general language model to understand legal or medical terminology.

Reinforcement Learning:

• Definition: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward.
• Application: Used in robotics, game playing, and other areas where decision-making and adaptation are crucial.

Supervised Learning:

• Definition: A machine learning task where a model is trained on labeled data, meaning each training example is paired with an output label.
• Application: Common in classification tasks, such as spam detection, image recognition, and predictive analytics.

Unsupervised Learning:

• Definition: A type of machine learning that deals with unlabeled data. The system tries to learn the patterns and the structure from the data without any explicit instructions on what to predict.
• Application: Used in clustering, anomaly detection, and association tasks.

These terms encompass some of the key concepts and technologies in the field of AI and natural language processing.