AI and ML are areas of intense focus and experimentation across a wide range of industries, functional disciplines and operational scale. Yet the concrete expression of the work tends to be extremely organization and/or function-specific, as AI models are typically trained on relatively narrow and proprietary datasets.
We see a need for an open source community effort to:
- Open up and standardize development of models and adaptive frameworks, using and creating public datasets for the common good
- Tie together various models and data sources across organizations in a unified framework
- Abstract prediction and forecasting techniques that can be applied across a range of domains, including climate and other scientific endeavors, and enterprise use cases
- Eventually apply AI/ML to wide-scale system analysis, using cross-correlation across different domains to intensify pattern identification and accelerate operational responsiveness
We expect to apply these general principles first to a well-defined set of open models and practice areas, including a self-describing digital asset catalog, a secure AI connectivity fabric, and AI models to benefit the environment (e.g., bee population preservation). This will allow us to test concepts and models at a global scale.
We are also focused on expanding access to the value of AI by improving simplicity and usability of the technologies. The impact of improvements in user experience will provide opportunities to progressively expand the reach of the Enterprise Neurosystem into new upstream domains, including enterprise, government and global initiatives (e.g., climate change analysis).
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