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Saturday, September 16, 2017

Considerations with Combining AI and Data

An interesting and lengthy piece in InfoQ on key issues with the construction and delivery of AI solutions. Use of data ontologies are mentioned,  a good idea.   Their key takeaways are instructive:

- Machine learning in turn is frequently fueled by big data but can also be fueled by traditional data sources.

- No matter what the scope is, we have to select data that is appropriate to the domain of the problem space

- Information from highly diverse sources needs to be parsed, curated, packaged, contextualized, and componentized for consumption by users or ingested by systems.

- While machine algorithms play an important role in both the preparation of data and interpretation of user intent, these types of applications require a significant amount of knowledge engineering to be successful.

- Thinking about data as a service and the platform as an orchestration layer between business problems and technology solutions can help organizations achieve dramatic improvement in data scientists productivity. .... " 

Much more at the link.

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