✨New to Generative AI?

Are you a beginner in artificial intelligence? Don't worry, we are here to explain some key concepts to help you get started quickly.

#What is LLM?

LLM stands for Large Language Model. These models, such as GPT-3 developed by OpenAI, are a type of artificial intelligence model that uses machine learning to generate human-like text.

Large language models are trained on a massive amount of textual data and can predict the likelihood of a word based on the words used previously in the text, allowing them to generate sentences. They can be fine-tuned for various tasks, including translation, question answering, and writing assistance.

These models are called "large" because they have a vast number of parameters. For instance, as of my knowledge in September 2021, GPT-3 was one of the largest known models, with 175 billion parameters. The abundance of parameters enables these models to capture a wide range of language patterns and subtle nuances, but it also requires significant computational resources for training and usage.

#What is Generative Artificial Intelligence?

Generative Artificial Intelligence refers to a type of AI that is capable of creating content. It involves using trained models to generate new data that mimics the distribution of the training data. Generative AI can create a wide range of content, including but not limited to text, images, music, and even synthetic sounds.

What types of applications can I build with Generative Artificial Intelligence?

Generative Artificial Intelligence models have a wide range of potential applications across various fields. Here are some examples:

• Content Creation: These models can generate new text, music, or artworks. For instance, AI can compose music for video games, generate scripts for movies, or produce articles and reports.

• Chatbots and Virtual Assistants: Generative models can be used to create conversational agents that interact with users and generate responses to their queries in a natural, human-like manner.

• Image Generation and Editing: Generative Adversarial Networks (GANs) can produce realistic images, design graphics, and even significantly modify existing images, such as altering day to night or generating human faces in the style of specific artists.

• Product Design: AI can be utilized to generate new product designs or modify existing ones, potentially speeding up the design process and introducing new possibilities that human designers may not have considered.

• Medical Applications: Generative AI can be employed to create synthetic medical data, simulate patient conditions, or predict disease progression.

• Personalized Recommendations: AI models can generate personalized content or product recommendations based on user data.

• Video Games: In the gaming industry, AI can be used to generate new levels, characters, or entire environments, making games more diverse and replayable as new content can be generated in real-time.

• Data Augmentation: In situations with scarce data, generative models can create synthetic data to complement real data for training other machine learning models.

These are just a few examples of the potential applications of Generative Artificial Intelligence across different domains. The versatility of these models opens up exciting possibilities for innovation and creativity in various industries.

#What is embedding?

Embedding is the numerical representation of concepts transformed into a sequence of numbers, enabling computers to easily understand the relationships between these concepts. They can capture the context of words in a document, their semantic and syntactic similarity, and their relationships with other words.

#What is a vector store?

In the context of machine learning, a vector store is a storage system or database designed to efficiently handle vector data. Vector data is commonly used in fields like natural language processing and computer vision, where high-dimensional vectors represent complex data such as words, sentences, or images.

Vector stores are often optimized for common operations in machine learning, such as nearest neighbor search, where the goal is to find the vector in the store closest to a given vector. This is particularly useful in tasks like recommendation systems, where you may want to find items most similar to a given item.

#What is the memory of LLM?

If a Large Language Model (LLM) has memory, it means it can generate text based on previously seen content. In this context, "memory" refers to how much previous text the model can take into account when generating new text.

Memory is a separate concept from the training set used to train the model. The model can answer questions based on what it has learned from the given training set. Additionally, considering you are chatting with ChatGPT, the model also takes into account the last response and query to respond to your queries. This kind of "memory" in handling long texts or conversations is crucial as it determines how much previous context the model can use to generate accurate and coherent responses.