Published by: Forbes

Modern businesses possess complicated networks of data, connecting information like customer behavior to marketing campaigns or fraud detection. But, to run useful AI predictions on the data often requires untangling the web of data connections. A new Stanford-bred startup says it has a solution using a new class of artificial intelligence to solve that problem.

Kumo announced itself to the world on Thursday with $18.5 million in Series A funding that it hopes will help it become the go-to software for AI prediction in the “modern data stack,” a set of cloud computing tools to store and harness large quantities of data. Sequoia Capital led the round at a valuation of $100 million; additional participation came from Ron Conway’s SV Angel and his son Ronny Conway’s A Capital.

The Mountain View, California-based startup was launched four months ago by founders Vanja Josifovski (formerly chief technology officer at Pinterest and Airbnb’s Homes business), Hema Raghavan (an ex-LinkedIn engineering director) and Stanford professor Jure Leskovec, who was also previously Pinterest’s chief scientist. The company comes as the culmination of five years of academic research conducted by a Stanford team featuring Leskovec, in conjunction with Germany’s Dortmund University. They focused on a budding form of AI, termed “graph neural networks,” which approaches machine learning by treating the data as if it were a complex graph network. Older forms of neural networks have become good at tasks with “structured data,” like image recognition or speech detection, but are hampered by data with unordered connections.

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https://www.forbes.com/sites/kenrickcai/2022/04/07/kumo-18-million-series-a-from-sequoia-to-reinvent-how-to-use-ai-to-make-predictions/?sh=5313edf12770

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