Christian Noske joined NGP Capital in October 2021 as a Partner. He focuses on investments in the Intelligent Enterprise and Smart Mobility areas. Prior to NGP Capital, he was Partner at Target Global, the founding Managing Director of Alliance Ventures (Renault-Nissan-Mitsubishi) and founding Partner at BMWi Ventures, where he focused on Enterprise Software, Industrial Tech and Automotive Tech. NGP Capital is a global venture capital firm with over $1.6 billion under management, investing in growth-stage technology companies within the Edge Cloud, Cyber Security, Digital Industry, and Digital Transformation. NGP Capital backs entrepreneurs building a responsible and inclusive world where the confluence of sensors, mobility, software, and cloud solutions will connect people and industries in new ways, transforming how we live and work.

Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make predictions by associating labels with specific data samples, like the caption "bear" with a photo of a black bear. A system trained on many labeled examples of different kinds of contracts, for example, would eventually learn to distinguish between those contracts and even extrapolate to contracts that it hasn't seen before. The trouble is, annotation is a manual and labor-intensive process that's historically been assigned to gig workers on platforms like Amazon Mechanical Turk. But with the soaring interest in AI -- and in the data used to train that AI -- an entire industry has sprung up around tools for annotation and labeling.

From autonomous drones to driver-assist cars and shopping carts that can check you out of a supermarket without standing in line for a cashier, artificial intelligence is set to change our lives. But it doesn't just happen. In order to function, artificial intelligence needs to recognize millions of traffic hazards, or airborne objects, or items in a supermarket by comparing them with labeled images stored in its memory. Dataloop is the first company to figure out how to combine and automate much of the process behind the "computer vision" that allows these applications to work – saving time and resources. The four-year-old Israeli startup estimates its software is already used by most of the labeling providers.

Data is the fuel for machine learning, but the data needs to be accurately labeled for the machines to learn. To that end, data training startup Dataloop yesterday unveiled that it's received $11 million in Series A funding to build SaaS data pipelines that combine human supervision of the data annotation process, along with data management capabilities. Today's computer vision models are extremely powerful, and the ones based on deep learning approaches can exceed human capabilities. From self-driving cars navigating in the world to programs that can accurate diagnose diseases in MRI images, the potential uses for Ais built upon convolutional neural networks are astonishingly wide. However, there's a catch (there always is).

Data is the fuel for machine learning, but the data needs to be accurately labeled for the machines to learn. To that end, data training startup Dataloop yesterday unveiled that it's received $11 million in Series A funding to build SaaS data pipelines that combine human supervision of the data annotation process, along with data management capabilities. Today's computer vision models are extremely powerful, and the ones based on deep learning approaches can exceed human capabilities. From self-driving cars navigating in the world to programs that can accurate diagnose diseases in MRI images, the potential uses for Ais built upon convolutional neural networks are astonishingly wide. However, there's a catch (there always is).

AI data management and annotation startup Dataloop today announced that it raised $16 million in funding, a combination of an $11 million series A round and a previously undisclosed $5 million seed round. A spokesperson says the funds will enable Dataloop to increase its recruitment efforts and grow its presence in the U.S. and Europe. Training AI and machine learning algorithms requires plenty of annotated data. But data rarely comes with annotations. The bulk of the work often falls to human labelers, whose efforts tend to be expensive, imperfect, and slow. Dataloop claims to solve the annotation challenge with a platform for automating data prep and data operations.