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There are many kinds of AI you interact with every day such as LLMs, (Large Language Models) Image generation models, or even your cellphone's autocorrect! Artifical Intelligance or AI is a computer program that generates something until it matches what it was given just like humans (A Neural Network) Neural Networks consists of 3 types of layers as shown in the model to the right. Input layer, Hidden layers and Output layers. The input layer is the input and the output layer is the output but the Hidden layers act as filters that the content passes through until it ends up as the output. For example you ask an AI model "Create me an image of a cat" the input is the prompt and the output is the image and the prompt passes thorugh layers until it ends up as the image desired. Different kinds of AI models have different architectures and are trained on different kinds of data to perform different tasks. Some AI models are trained on text data, some on image data, and some on both text and image data. The training process involves feeding the model large amounts of data and adjusting the model's parameters to minimize the difference between the model's output and the desired output. This process is repeated many times until the model learns to generate outputs that are similar to the desired outputs. Once trained, AI models can be used for a variety of tasks such as text generation, image generation, translation, summarization, and question answering. AI has many practical applications in various industries such as healthcare, finance, marketing, and entertainment. This means models specialized for certain tasks can be more effective than general-purpose models such as OpenAI's GPT-5.
Large Language Models are the kind of AI people use everyday from customer support, to helping with a cooking recipe, or even writing code. LLMs are trained on large amounts of text data from the internet and other sources to understand and generate human-like text. Some popular LLMs include Deepseek r1, OpenAIs GPT-3, GPT-4, and ChatGPT, as well as Google Gemini, and Meta's LLaMA. These models can be used for a variety of tasks such as text generation, translation, summarization, and question answering. LLMs have revolutionized the field of natural language processing and have many practical applications in various industries.
Image generation models are a type of AI that can create image and video content from text descriptions or other input data. These models use deep learning techniques to generate high-quality images that can be used for various purposes such as art, design, and advertising. Some popular image generation models include DALL-E 2 by OpenAI, Midjourney, and Stable Diffusion. A popular video generation model is OpenAI's Sora. These models have the ability to create realistic and imaginative images based on the input provided by the user. Image generation models have opened up new possibilities for creativity and innovation in the field of visual arts.
AI models can be used to analyze medical images, predict disease outcomes, and assist in drug discovery. These models are trained on large datasets of medical records and imaging data to provide accurate predictions and insights. Some popular health AI models include DeepMind's AlphaFold for protein structure prediction and IBM Watson Health for medical diagnosis. These models have the potential to revolutionize healthcare by improving diagnostic accuracy and treatment planning.
Some AI models can be run locally on your computer without an internet connection. This is useful for privacy and security reasons as well as for offline use. Some popular local AI models include Stable Diffusion for image generation and LLaMA 3.1 for a LLM. Local LLMs are typically measured in billions of parameters (B) ranging from about 0.6B to 405b while cloud based LLMs are typically measured in trillions of parameters (T). These models can be downloaded and run on your own hardware, allowing you to have more control over your data and the AI model itself. However, running AI models locally may require more powerful hardware and technical knowledge compared to using cloud-based AI services.