What a refreshing spring time feeling in the air these days. Not only is a new season of Game of Thrones streaming on HBO. We finally see a breakthrough in terms of a reasonable categorization of Artificial Intelligence (AI) and Machine Learning (ML).
The white walkers of AI
After a cold and long winter of overly hyped AI the world is starting to wake up to the obvious reality that AI is just another expression of the power of data and analytics. We are once again calibrating on having the human in the loop when applying analytical software to industrial data. Aficionados of Game of Thrones may see a collective force being mobilized against the dead white walkers of robotized black box AI.
Where do I find this springtime sensation?
- EU calls for human-centric AI
For once EU just a couple of weeks ago put forward a set of ethical guidelines for AI with the focus of building trust in human-centric AI. It is described like this. “The European AI strategy and the coordinated plan make clear that trust is a prerequisite to ensure a human-centric approach to AI: AI is not an end in itself, but a tool that has to serve people with the ultimate aim of increasing human well-being.”. Read more
- Human-centered AI
We have thought leading universities like MIT and Stanford putting a thick line under human-centered AI. The new Human-Centered Artificial Intelligence (HAI) initiative from Stanford and the HCAI from MIT institutionalize this trend. The idea at Stanford is about setting up a cross-sector institute to advance AI through collaboration with humanists and social scientists.
- Democratized AI
Thirdly Gartner Group calls ‘democratized AI’ a key trend, as it will become more widely available due to cloud computing, open source and the maker community. The notable change will be its availability to the masses. These technologies foster a community of developers, data scientists and AI architects. Read more
Scientists with analytics
There is no doubt about the potential in AI and ML, and for simple analysis like predicting equipment failure it’s fine. But in a complex manufacturing process where analysis is used to solve quality issues based on composition, physical, and sensory properties of a product, we still need the domain knowledge and expertise to find the causalities.
Camo has always been industrial analytics in the hands of the domain expert and scientist. If AI does not build on their experience and science but instead let all meaning derive from data, it is on a troublesome path. Both in terms of value for businesses and in terms of being explainable and therefore socially acceptable, not to mention the documentation requirements in regulated industries.
Summer is coming
While we are on the analytics journey towards full cyber-physical integration and autonomous high-quality production our approach to industrial analytics encourages domain experts and scientists to start off intelligently and use both logic and intuition, and then work with data and analysis to take the next steps. We enable these knowledgeable people to go further with analytics and AI as their tool box, and though we were tempted by the hype we didn’t label our portfolio with “AI inside”.
Summer is coming!
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