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What is generative AI?

Generative AI refers to a type of artificial intelligence that is capable of creating content. It involves the use of models trained to generate new data that mimic the distribution of the training data. Generative AI can create a wide array of content, including but not limited to text, images, music, and even synthetic voices.

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What is an LLM?

LLM stands for Large Language Model. These models, such as GPT-4, are a type of artificial intelligence model that uses machine learning to produce human-like text. Large Language Models are trained on vast amounts of text data and can generate sentences by predicting the likelihood of a word given the previous words used in the text. They can be fine-tuned for a variety of tasks, including translation, question-answering, and writing assistance. These models are called “large” because they have a huge number of parameters. For example, GPT-4, one of the largest models as of today, has about 1.8 trillion adjustable parameters. Their large parameter count allows these models to capture a wide range of language patterns and nuances, but also makes them computationally expensive to train and use.

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What type of applications can I build with Generative AI?

Generative AI models have a wide range of potential applications across numerous fields. Here are some examples:
  1. Content Creation: These models can generate new pieces of text, music, or artwork. For example, AI could create music for a video game, generate a script for a movie, or produce articles or reports.
  2. Chatbots and Virtual Assistants: Generative models can be used to create conversational agents that can carry on a dialogue with users, generating responses to user queries in a natural, human-like manner.
  3. Image Generation and Editing: Generative Adversarial Networks (GANs) can generate realistic images, design graphics, or even modify existing images in significant ways, such as changing day to night or generating a person’s image in the style of a specific artist.
  4. Product Design: AI can be used to generate new product designs or modify existing ones, potentially speeding up the design process and introducing new possibilities that human designers might not consider.
  5. Medical Applications: Generative AI can be used to create synthetic medical data, simulate patient conditions, or predict the development of diseases.
  6. Personalized Recommendations: AI models can generate personalized content or product recommendations based on user data.
  7. Data Augmentation: In situations where data is scarce, generative models can be used to create synthetic data to supplement real data for training other machine learning models.

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What is an embedding?

Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts. They are capable of capturing the context of a word in a document, its semantic and syntactic similarity, and its relation with other words.

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What is a vector store?

A vector store in the context of machine learning is a storage system or database designed to handle vector data efficiently. Vector data is commonly used in fields like natural language processing and computer vision, where high-dimensional vectors are used to represent complex data like words, sentences, or images. Vectors stores are often optimized for operations that are common in machine learning, like nearest neighbor search, which involves finding the vectors in the store that are closest to a given vector. This is particularly useful in tasks like recommendation systems, where you might want to find the items that are most similar to a given item.

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What is a multimodal model?

A multimodal model in the field of artificial intelligence is a model that can handle and integrate data from multiple different modalities, or types, of input. These types of inputs can include text, images, audio, video, and more. The main advantage of multimodal models is that they can leverage the strengths of different data types to make better predictions. For example, a model that takes both text and image data as input might be able to understand the context better than a model that only uses one or the other.

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What is the memory of an LLM?

A Large Language Model (LLM) can generate text based on what it has seen before if it has memory. The term “memory” in this context refers to how much of the previous text the model can consider when producing new text. Memory is a different concept than the training set used to train the model. The model can answer things from what it knows given the training set that is was given. Additionally, considering that you are chatting with chatGPT, the model will respond to your queries considering the last responses and queries as well. This “memory” of the model is indeed crucial when dealing with long pieces of text or conversations, as it determines how much of the previous context the model can use to generate accurate and coherent responses.