A thousand AI flowers to bloom for OSS?

AI startups are becoming cheaper and easier to build, because many of the underlying technologies are now mature enough to apply predictably, and because of the declining cost of cloud computing – including many AI as a service products on AWS and Google Cloud.
I liken this development to the time when cloud computing first emerged around ten years ago. Resources that were previously the preserve of cash rich companies became available to anyone who could pull together a few grand and a thousand flowers bloomed. I think we will see something similar again now
Nic Brisbourne

I have absolutely no question that artificial intelligence (AI) and machine learning (ML) are going to revolutionise the world of OSS. Despite having some very clever algorithms in OSS already, we’ve barely scratched the surface so far.

To Nic’s point, AI/ML was previously the preserve of cash-rich companies and there aren’t too many OSS companies that tick this box, not to mention having the resources to tackle these two big challenges (OSS and AI/ML) simultaneously. Further to Nic’s point, AI is now becoming more accessible and OSS is likely to start leveraging these tools more heavily, just as we have in the recent past with cloud tech. Bring it on!

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2 Responses

  1. some pilots are proving quite interesting in enhancing Network Operations. Especially when AI / ML is used in the form of Natural langauge processing – NOC operators are talking to a ML system in natural language and asking questions in context to an alarm about deep topics ( BGP, 4G eUTRAN, etc). Do more with L1 engineers.
    – Use of familiar chat front ends like FB Messenger, Hangouts to have conversation with a OSSChatBot which learns using ML, continuously, the intents of the questions being asked and responds by bringing information back from OSS using APIs. (Performance of a cell site, or Fuel level on a tower site, Weather information in a cluster of sites etc). Ease of use, Open new channels to use OSS
    – Training the AI system in TAC tickets, Vendor manuals, Historical tickets, – ability to search and index unstructured data and recommend solutions for problems when they occur or about to occur – informed via OSS. efficiency increase, Lower NOC costs.
    – Network planning using ML apis. Ticket prioritization using ML analytics ( which ones to act upon first – certainly not only by severity)

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