Turning Big Data into knowledge, and helpful applications — how?

The more hype about “AI” and “big data”, the more it confuses me.

I work in Artificial Intelligence. Our team work with data, big, medium and small. Everyday, dozens of articles flood my inbox, social media feed, Quora and Medium digests: big data, AI, deep learning, data overflow, AI is exciting, AI is scary…. I know, AI is the next frontier, the next technology revolution after the Internet. By keeping up with the chatter, I feel a sense of satisfaction, that I am keeping up and being part of the next big thing.

But a sense of hunger grows beneath the surface of this artificial satisfaction. There are too many blanket statements. Too many silver bullets that claim to solve all your data problems. I’m fed with information, but not enough depth to abstract connections among them, and transform them into actionable knowledge. I want more.

I want concrete examples. I want to understand how AI has successfully solved specific business problems, and how it has failed too.

And my BHAG: Building from specific examples, can I develop frameworks that I can use to strategically plan for successful AI application, suitable for unique business problems?

This blog is my attempt fulfill my thirst of knowledge in that. To collect examples of successful and unsuccessful AI applications, and build a knowledge system around it.

For all of you who have medals and battle scars, I'm eager to learn from you. I stand to be corrected for my thoughts, and I live to learn.

Thank you for learning with me.

Like what you read? Give Luyao Li a round of applause.

From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.