Terms to Know

Generative AI

AI systems that create text, images, music, and other types of content based on given instructions.

Large Language Model (LLM)

A type of AI trained on vast amounts of text to understand and generate human language.

Natural Language Processing (NLP)

AI technology that helps computers understand and respond to human language.

Machine Learning (ML)

A type of AI where computers learn and improve from experience and data.

Deep Learning

A form of machine learning that uses large neural networks to handle complex tasks like recognizing images or understanding speech.

Transformer Models

A type of AI model particularly good at understanding and generating text, forming the basis of many language models.

Prompt Engineering

The method of crafting questions or prompts to get the best responses from AI.

Training Data

Information used to teach AI systems how to understand and generate content.

Inference

The process of an AI generating responses based on what it has learned.

Fine-Tuning

Adjusting a pre-trained AI model with specific data to improve its performance on particular tasks.

Bias and Fairness

Ensuring AI does not unfairly favor certain groups or viewpoints due to biased training data.

Ethical AI

Developing and using AI in ways that are fair, transparent, and beneficial to society.

Explainability

How well the actions and decisions of an AI system can be understood by humans.

Retrieval-Augmented Generation (RAG):

AI that combines searching for relevant information with generating new responses, making the answers more accurate and relevant.

Human-in-the-Loop

Systems where humans oversee and intervene in the AI decision-making process to ensure better outcomes.

Contextual Understanding

AI's ability to understand the context around the information it processes to give relevant responses.

Ethical Frameworks

Guidelines to ensure AI is used ethically, including principles like transparency, accountability, and privacy.

Augmented Intelligence

AI systems designed to enhance human decision-making rather than replace it.

Synthetic Data

Artificially generated data used to train AI models when real data is limited or sensitive.

Multimodal AI

AI that can process and understand multiple types of data, like text, images, and audio, simultaneously.

BERT (Bidirectional Encoder Representations from Transformers)

An AI model that understands the context of words in a sentence, used for tasks like search queries.

Generative Adversarial Networks (GANs):

AI models that create new data by having two networks compete against each other, often used for creating realistic images.

Voice Recognition and Generation:

AI technologies that understand and produce human speech, important for virtual assistants and customer service.

Transfer Learning

Using a pre-trained model and fine-tuning it for a new task, making the training process faster and more efficient.

Personalization

AI techniques that tailor experiences and recommendations to individual users based on their preferences and behavior.