Samsung
OEM : Computer and Electronic
Samsung Electronics constantly reinvents the future. We explore the unknown to discover technologies to help people all over the world lead happier, healthier lives.
Assembly Line
Behind the Foldable Phones in Our Pockets
Why we invested in ZEDEDA: visibility, control, and security for the distributed edge
Traditionally, the deployment and remote management of Industrial Internet of Things (IIoT) at the edge has been difficult and expensive. One of the most significant hurdles faced by enterprise users today is the cost and lead time needed to update what are often proprietary legacy solutions. In the current environment, the majority of IIoT solution providers offer highly customized, siloed solutions for customers in specific verticals. As a result, these verticalized solutions tend to be fragmented and often are expensive to update and maintain.
We invested in ZEDEDA because the company has proven it can remain a horizontal play across multiple industrial verticals. ZEDEDA was founded in 2016 as a vertical solution provider. But in 2019, the company made the decision to open source its flagship product, EVE-OS.
Landing AI Secures Funding to Unlock Power of Small Datasets, Unleashing Next Era of AI
Landing AI, led by artificial intelligence visionary, Andrew Ng, developed LandingLens™, a fast, easy to use enterprise MLOps platform. It applies AI and deep learning to help manufacturers solve visual inspection problems, find product defects more reliably, and generate business value.
“You don’t always need big data to win with AI. You need good data that teaches the AI what you want it to learn,” said Ng, Founder & CEO of Landing AI. “AI built for 50 million data points doesn’t work when you only have 50 data points. By bringing machine learning to everyone regardless of the size of their data set, the next era of AI will have a real-world impact on all industries.”
The data-centric approach of Landing AI is also key to making LandingLens fast and easy-to-use. The process of engineering the data, instead of the AI software, gives an efficient way for manufacturers to teach an AI model what to do. Domain experts, not just AI experts, can now build an AI system, and take it to production. For example, rather than needing to write pages of code to train a neural network, a domain expert can do it with a few mouse clicks. This no code/low code data-centric platform enables new users to build advanced AI models in less than a day. Vision inspection projects that used to take over a year can be executed in just weeks using LandingLens.