AI is a general purpose technology that can be applied to various sectors

AI is like electricity. It has the potential to transform all industries.

It has applications in healthcare, finance, transportation, retail, manufacturing, agriculture, education, energy, media and cybersecurity

Nvidia, Intel, AMD are creating value in the hardware layer

Opportunity for creating value exists for cloud developers and AI foundational tool builders in the foundational layer

Massive opportunity exists in multiple sectors for developers at the application layer

Deep Learning Labelling AI has valuable use-cases

Generative AI has applications and thousands of use-cases

Solving alignment helps humanity optimize for the upside and mitigate risks

Alignment and capabilities are not orthogonal. They can be simultaneously optimized

Providing APIs and tools for training open source models allows for the simultaneous optimization of alignment and capabilities

The world needs fine-tuned models for domain specific use-cases to solve domain-specific problems such as image analysis, video analysis, and vector database data analysis.

Inputs to OpenLibreAI models are raw data.

Outputs of OpenLibreAI API (which wrap the OpenLibreAI models) are generated text, images, predictions, summaries and other value-add artifacts.

OpenLibreAI is a free and open source platform for AI research and development. It is a community-driven project that aims to provide a platform for AI researchers and developers to collaborate and share their work.

What the world needs now is an open source AI stack / cloud platform / application platform.

OpenLibreAI is an open source attempt to collect, deploy, and productionize open source models that are state of the art right now (StabilityAI, etc) as well as the ML models for specific use-cases on HuggingFace.

OpenLibreAI will deploy them under the open source OpenLibreAI platform.

OpenLibreAI's models would be trained and fine-tuned for domain-specific use-cases.

For example, OpenLibre can use skin cancer data and images to train the OpenLibreAI-Carcinoma model that can be deployed as an API and OpenLibreAI gets to charge for the API calls.

OpenLibreAI will also have a marketplace for AI models and APIs.

OpenLibreAI will have a workflow that is similar to the following:

OpenLibreAI models will need to be trained on good data

One of the modules of OpenLibre will be the training data ingestion and annotation module

The training data ingestion and annotation module will take a dump of any docs, spreadsheets and csv files and label it for use in training

The ingestion module will also need to be domain specific, so there will be different modules for different domains and potentially also different use-cases

For example, the cancer prediction model will need labeled carcinoma imagery

Video annotation modules can ingest videos of various events and summarize them, as well as create described-video from them

Once one has training data, the LibreAI models can be fine-tuned for all the various use-cases

The OpenLibreAI APi then directs the API calls to the specific OpenLibreAI deployed models to do either prediction, summarization, or generative tasks

OpenLibreAI has three main categories of capabilities - generative, predictive, and summarization

OpenLibreAI can expose these capabilities as paid APIs as well as a reference application that is deployed as a paid SaaS

Although the models are being open sourced, the OpenLibreAI monetization model is charging enterprise customers for the API usage of the deployed models (including vector embeddings and context searches)

OpenLibreStudio provides tools, APIs and UIs for datasets, training and deployment of models and APIs for interacting with models

OpenLibreGPT is an autonomous agent / personal AI Assistant that runs on mobile devices (Android and iOS) and performs tasks under the supervision of the user

One of the business models for OpenLibreAI will be to charge for the cleaned training data

OpenLibreAI is structured as an open source collective where contributors are compensated in proportional-accrued-value-impact-dollars (PAVIDs).

PAVIDs are credits, which are pegged to USD so that 1 PAVID = 1 USD.