AI Industrial Startups to Watch
While GE pioneered the term “Industrial Internet” when announcing its $1.5 billion R&D commitment back in 2012 more and more startups are attacking the needs of heavy industry by developing machine learning software to do everything from extract insights from the deluge of data to power autonomous intelligent robots on factory floors, notes research firm CB Insights. Read on to learn more about The Innovator’s pick of AI industrial startups to watch.
WHAT IT DOES: Its machine learning technology spots patterns in huge amounts of data gleaned from factory floors and uses it tomake useful
predictions based on those patterns in applications as diverse as fraud
detection and predictive maintenance.
WHAT IT DOES: Uses natural language processing and machine learning to extract and categorize information from over 10 million supply-chain signals everyday. It is attempting to build a map of every supplier, warehouse, factory and port in the world to provide a full picture of the $25 trillion global product economy.
WHAT IT DOES: Uses machine learning technology to automatically detect
hard-to-findearly signs of problems in production operations from time-series data. It says its technology helps factories avoid even minor deviations in norms, which can have a significant impacton product quality and machine utilization.
WHAT IT DOES : An artificial intelligence and control framework that transforms human-operated mobile machines into autonomous intelligent robots that can be used in industry. Its software and hardware framework work together to make mobile machines autonomous.
WHAT IT DOES: Encodes the world’s industrial expertise and data and
translates it into digital knowledge. Maana’s knowledge graph, coupled
with advanced AI algorithms, semantic search, and deep learning, helps
industrial and oil and gascompanies make faster and more relevant data-driven decisions.
WHAT IT DOES: Aims to improve industrial processes by deploying AI
where the data is produced and where decisions need to be made: at the edge of the network and in the objects themselves. The platform automates
predictive models, for faster and more accurate predictions in customer and asset intelligence.