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To AI or not to AI


CERN has developed an amazing technology called Collaboration Spotting. Collaboration Spotting is a visualisation and navigation platform for large and complex datasets. It uses graphs, as well as semantic & structural data abstraction techniques to help domain experts transform big data into actionable insights. This software was developed when CERN was building the largest collaborative scientific project of all time and required a digital tool to further accelerate collaboration. Collaboration Spotting can, for instance, connect millions of patent related documents across the world to reveal new patterns, extract actionable insights, and highlight opportunities for collaboration.

The IDE4 Foundation is now incubated at GCSP, a leading geopolitical think tank, and has a licence on the CERN technology with full capability to sub-licence it for commercial purposes. Have a look at the IDE4 website.

Why is such software highly relevant and needed? Humans are not good, actually totally incapable, at interpreting large data-sets. Machines can handle large amounts of data, but they lack intuition. That is why Bill Gates refers to AI as “advanced curve-fitting”, which we have learned from a valued member of Katalysen’s network who has worked closely with Mr. Gates in the past. Ideally, software such as Collaboration Spotting from CERN can help to make brutally large data sets visual and allow human intelligence, specifically domain experts and not AI, to interpret it. To us, this is a very promising approach for bridging big data and the human brain (without needing Elon's Neurolink or the likes). In addition to lacking intuition, creativity and "aha-moments", AI also creates all sorts of legal issues that can be largely avoided by using a combination of machines and humans. 

Recently, Heiner met the Foundation's representatives together with the technical experts from CERN and discussed how Katalysen can contribute to the deployment of Collaboration Spotting and the next steps of the IDE4 Foundation. If you are working with large-datasets and would like to make sense out of them, or if you are unsatisfied with the outcome of your machine learning tool, don't hesitate to get in contact with us!

Katalysen Nyheter - To AI or not to AI

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