Built For The Future
AI Terms
Embeddings
Embeddings are a fundamental concept in machine learning that involves converting raw data into a structured, often lower-dimensional space, typically called the embedding space. These numerical representations retain meaningful relationships and properties of the original data, making them invaluable for various machine learning tasks.
Vector Database
A vector database is a specialized type of database designed to handle vector embeddings, which are high-dimensional vectors representing data items. These databases are built to efficiently index and search through massive collections of vectors, making them essential in fields such as AI and machine learning for tasks like similarity search, recommendation systems, and more.
Inference
In machine learning, inference refers to the process where a trained model is used to make predictions on new data. This is distinct from the training phase where the model learns from a dataset by adjusting its weights and parameters to minimize error. Inference is critical as it’s how the model actually gets applied to real-world tasks, demonstrating its utility by making decisions or predictions based on its learned patterns.
Machine Learning (ML)
Machine Learning (ML) is a dynamic and integral field of artificial intelligence that focuses on developing algorithms and statistical models that enable computers to perform specific tasks without explicit instructions. Instead, these models learn and make predictions or decisions based on data.
Deep Learning
Deep learning is an advanced subset of machine learning that employs artificial neural networks with multiple layers, or “deep” networks, to model complex patterns and perform cognitive tasks like human brain functions. This technology excels in areas such as image recognition, natural language processing, and speech recognition
Tokens
A token in programming can be described as the smallest string of characters that can be treated as a unit in the syntax of the programming languages. Tokens include keyword, identifiers, constants, string literals, and symbols such as operators and punctuations. Each token corresponds to a pattern that helps the compiler or interpreter to analyze the text of the program to understand its structure.
Quantization
Quantization in machine learning is a crucial technique for optimizing models, particularly for deployment on devices with limited computational power and storage capacity. Here’s a comprehensive explanation based on the latest insights
Neural Networks
Neural networks are complex structures modeled after the human brain, designed to recognize patterns and solve various types of problems within the field of artificial intelligence (AI).
Training
Training a program involves feeding it lots of data. This could be images, texts, or any type of information depending on what it needs to learn. Some data might tell the program exactly what to learn, and other times it just practices until it starts to see patterns.
Foundation Model
A foundation model in AI is a large-scale artificial intelligence system trained on vast amounts of diverse data, serving as a versatile base for multiple downstream tasks. These models, such as GPT-3 and BERT, can be fine-tuned for specific applications like language translation, text summarization, or question answering, making them powerful and adaptable tools in the field of artificial intelligence.
Finetuning
Fine-tuning is the process of adapting a pre-trained foundation model to a specific task or domain by training it further on a smaller, specialized dataset. This technique allows developers to leverage the broad knowledge of a large model while optimizing its performance for particular applications, resulting in more accurate and efficient AI solutions tailored to specific needs.
RAG (Retrieval Augmented Generation)
RAG, which stands for Retrieval-Augmented Generation, is an AI technique that combines information retrieval with text generation. This approach enhances language models by allowing them to access and incorporate relevant external knowledge when generating responses, improving accuracy and reducing hallucinations in AI-generated content.