Foundation Model Slides | How to Manage, Extend & Customize

Foundation models are platform model. Task specific and domain specific model can be trained on top of foundation models. On one side out of box foundation model can be used for routine task, on other side these are extensible.

OVERVIEW

OVERVIEW
OVERVIEW

BASE FOUNDATION MODEL

BASE FOUNDATION MODEL
BASE FOUNDATION MODEL

EXAMPLES

EXAMPLES
EXAMPLES

CONSIDERATIONS

CONSIDERATIONS
CONSIDERATIONS

CUSTOM MODEL

CUSTOM MODEL
CUSTOM MODEL

DEMO IMPLEMENTATION

DEMO IMPLEMENTATION
DEMO IMPLEMENTATION

Additional Comments

Foundation Models


Foundation models are called foundation models as these act as platform. These are trained in self supervised manner in borad range of data. Foundation model exhibit transitional properties. These model may be unfinished for task, bt can be adapted for narrow task. Companies can use it and train with domain specific data to make them specific for domain.

Generative AI Foundation Model

Generative AI is a subset of artificial intelligence that leverages machine learning techniques to produce content. It can generate new data instances that resemble your training data. For example, GANs (Generative Adversarial Networks) are a type of generative model that can generate synthetic images, music, speech, and text that seem incredibly realistic.

Overview

Generative AI models are trained on a large corpus of data and learn to generate new data that is similar to the training data. They can be used to generate a wide variety of content, from text to images, music, and more. These models are typically based on deep learning techniques, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs).

Example of Foundation Model

One of the most well-known examples of a generative AI foundation model is GPT-3, developed by OpenAI. GPT-3, or Generative Pretrained Transformer 3, is a language prediction model that uses machine learning to produce human-like text. It's trained on a diverse range of internet text, but it can also be fine-tuned with your own data to perform tasks like translation, question-answering, and more.

How to Select a Foundation Model

Choosing the right foundation model for your generative AI project depends on several factors. These include the type of content you want to generate, the amount and quality of your training data, and the computational resources you have available. It's also important to consider the model's complexity, as more complex models may produce better results but require more resources and training time.

Other Considerations

While generative AI models can produce impressive results, they also raise important ethical and practical considerations. For example, they can be used to create deepfakes or to generate misleading or harmful content. It's also important to ensure that your training data is diverse and representative, to avoid bias in the generated content. Finally, keep in mind that these models require significant computational resources and expertise to train and use effectively.



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