Here's What You Need to Know: AI technology is fast evolving. The national security establishment is racing to adopt artificial intelligence in nearly every aspect of operations, from processing payroll to processing disparate battlefield information into a cohesive whole, such as in the Pentagon's Joint All Domain Command and Control effort to network otherwise separated operational "nodes" to one another in warfare to optimize and streamline attack. However, training AI systems to recognize the things they are meant to recognize requires vast, even seemingly limitless volumes of annotated data. As promising AI is, an AI system is only as effective as its training data. At the moment, there seem to be few barriers to AI and its promise for the future, yet an actual AI-system is only as effective as its database.

SAN FRANCISCO, Feb. 04, 2020 (GLOBE NEWSWIRE) -- Labelbox, the leading training data platform for enterprise machine learning applications, today announced the close of a $25 million Series B funding round led by Andreessen Horowitz with General Partner Peter Levine joining the Labelbox board of directors. Previous investors First Round Capital, Gradient Ventures (Google's AI-focused venture fund) and Kleiner Perkins also participated. To date, Labelbox has raised $39 million in venture funding. Labelbox offers a training data platform for machine learning teams to build real-world artificial intelligence. The platform consists of label editor tools, batch & real-time labeling workflows, collaboration, quality review, analytics, and an optional, fully managed and dedicated labeling workforce.