Key Definitions of Generative AI
Artificial Intelligence (AI) and its subfield, Generative AI, are rapidly evolving areas that are transforming entire industries. To navigate this exciting terrain, it’s crucial to understand the associated vocabulary. This glossary offers clear and accessible definitions of key terms, maintaining a consistent and understandable tone throughout the content.
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Artificial Intelligence (AI)
Refers to the field of computer science dedicated to creating systems capable of performing tasks that, until recently, required human intelligence. These tasks include learning, reasoning, perception, language understanding, and creativity. AI can range
from systems performing a specific task to those with the ability to learn and adapt to a variety of tasks. -
Generative Artificial Intelligence
It is a subfield of AI focused on creating models that can generate new, complex, and original content based on learned patterns from large volumes of data. This includes text, images, music, product designs, and solutions to technical problems, enabling creative and problem-solving applications not predefined.
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Large Language Models (LLMs)
Are AI models specialized in processing and generating text, known for their ability to understand and produce natural language coherently. These models can contain billions of parameters, allowing them to comprehend and generate responses in conversations, content writing, and more.
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Foundational Models
These are large-scale AI models that serve as a foundation for developing customized generative AI applications. They can be adapted to specific tasks without needing to rebuild the model from scratch, providing a versatile platform for innovation.
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Parameter Tuning
It is the process of modifying the parameters of an AI model to improve its performance on specific tasks. This fine-tuning allows for customizing foundational models for particular needs without altering their fundamental structure.
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Reinforcement Learning from Human Feedback (RLHF)
A technique that adjusts AI models based on user feedback, optimizing the models responses to align with human expectations and improve the relevance and accuracy of its outputs.
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Embeddings
Are mathematical representations of data that enable AI models to process and understand complex relationships and nuances, essential for precise and contextual content generation.
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Prompt Design
The ability to formulate specific instructions to an AI model to obtain desired responses or content. Effective prompt design is crucial for maximizing the utility of generative AI models.
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Transformer Architecture
A neural network structure that has revolutionized AI models; ability to understand long-term relationships in data, significantly improving text generation and other forms of content.
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Few-Shot Learning and Zero-Shot Learning
Advanced capabilities that allow AI models to perform tasks with little or no specific training data, demonstrating deep contextual understanding based on prior knowledge.
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Neural Networks
Are the heart of many AI systems, inspired by biological neural networks, enabling models to learn patterns in data and perform complex information processing tasks.
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Generative Adversarial Networks (GANs)
An innovative approach in AI that uses two competing networks to enhance the quality of generated content, particularly useful in creating realistic images, videos, and other media.
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Variational Autoencoders (VAEs)
Specialized techniques for generating new and diverse data from existing datasets, maintaining key characteristics but introducing variations, useful in design, art, and more.
This glossary is designed to provide a solid understanding of key concepts in AI and generative AI, enabling exploration and application of these innovative technologies with greater confidence and insight.
