Different types of AI models

AI - System or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.  Artificial Intelligence capable of generating text, images, videos or other data using generative models often in response to prompts.   Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

AGI - Artificial General Intelligence is a type of AI that can understand, learn and apply knowledge across broad range of tasks similar to human cognitive abilities.   

Example of Cloud Machine Learning:

Different AI Types:

Machine Learning:

Simple Input -> Simple output -> Single topic

Deep Learning:

Complex input -> Simple output -> Single topic

Foundation Model:

Complex inputs -> Complex ouputs -> multiple topics

Foundation Model:

A foundation model is a type of large scale artificial intelligence model that is trained on a broad range of data at massive scale, allowing to develop a wide understanding of many topics and tasks.  These models can be adapted or fine-tuned for various specific applications, demonstrating flexibility and efficiency across different domains. 

 Parameters of foundation models:

• Embedding Vectors : The foundation model dealing with categorical variable (like words or user ID in recommendation systems), embedding vectors are a form of parameter represent a categories in a continuous vector space and capturing semantic similarities among categories. 
• Weights:  This is a numerous parameters in neural network.  Weights are used in various layers of a neural network to scale the input data in a meaningful ways.  For example in a convolutional layer commonly used in image processing, weights determine the importance of neighboring pixel values for feature detection. 
• Biases: Bias parameters are added to the output of weighted inputs to shift the activation function curve up or down.  This is crucial for models to accurately represent patterns in data that do not pass through the origin of the coordinate system. 
• Attention Scores: Attention mechanisms use parameters to weigh the significance of different parts of the input data differently.  For instance, in language models attention scores determine how much emphasis the model places on different words when generating a response or translating text.

LLM [Large Language Module]

LLM - It is highly specialized for tasks involving human language.  They are optimized for understanding and generating text which makes them more efficient for language specific tasks like conversation, translation or content generation.