AI’s ability to understand the meaning of text relies on a mechanism called “self-attention.” This system allows the AI to detect relationships between words and grasp the overall context of a sentence. By doing so, it significantly improves the accuracy of natural language processing.
The reason AI can understand text is because of a technology called “Transformer,” which uses a mechanism known as “self-attention” to recognize the relationships between words and grasp the context.
AI technology called BERT helps machines understand the meaning of language by reading the context around words. It plays a key role in making interactions more natural in services like search engines and predictive text input.
GPT is an AI technology that generates text by understanding context through a method called “autoregression.” This allows it to produce natural and coherent conversations.
AI that understands human language—especially Large Language Models (LLMs)—are technologies that learn from vast amounts of text data to generate natural-sounding sentences. These models are becoming deeply integrated into our daily lives.
What is a “feature” that AI uses to make decisions? It’s a crucial clue that helps AI distinguish between things or make predictions, and there are many examples of it in our everyday lives.
Dimensionality reduction is a technique used to organize excessive information and extract only the essential features, making it easier for AI to learn and make predictions efficiently.
When there’s not enough data for AI to learn from, a technique called “data augmentation” can be a powerful solution. By slightly altering existing data, we can generate new variations that help AI models learn more effectively. This approach is especially useful in fields like image and speech recognition, where diverse examples are crucial for building flexible and accurate systems.
Normalization is a crucial process in AI and machine learning that involves adjusting the scale and units of data to ensure consistency. It serves as a foundational step that enables fair and accurate analysis by eliminating imbalances in the data.
To ensure that AI learns correctly, it’s essential to prepare the data in a way that’s easy for it to understand. This process, known as “data preprocessing,” plays a crucial role and has a significant impact on the outcome of AI learning.
