The power of Unity in AI

Since 2018, Cross Compass has integrated Unity into the pipeline of several of its consulting services for the manufacturing field to train and validate AI algorithms safely before deployment. Read on to learn how this AI company came to use gaming technology to add value to such a mature industry.

Cross Compass is a leading AI company providing state-of-the-art solutions to global industry leaders in manufacturing, robotics, gaming, healthcare, design, and marketing. Established in Tokyo in 2015, Cross Compass develops cutting-edge techniques in Deep Learning, Machine Learning, and Artificial Life, to increase safety, quality, and productivity for the benefit of society.

We invited them to share in their own words why they embraced Unity and how it helps them deliver the following benefits:

Offers a platform for discussing specifications and progress with clients and partners Avoids many safety checks required to set up the data collection environment Provides an unlimited amount of data for AI training and testing Allows for faster iterative cycles of AI training and testing in simulation Leads to the delivery of AI solutions with higher performance and quality to end users Increases the value of human intervention while AIs handle the repetitive tasks reliably

Learn more in this guest post from Cross Compass by Romain Angénieux, AI Simulation Group Leader; Steven Weigh, Global Brand Identity Designer; and Antoine Pasquali, Chief Technology Officer.

Challenges in introducing AI for manufacturing environments

Designing and deploying cutting-edge AI solutions for manufacturing environments is a complicated process. Manufacturing production lines have been meticulously optimized and perfected for decades. Experts have designed, tweaked, and iterated upon every detail end-to-end, to ensure the highest efficiency, safety, and quality standards that meet strict industry requirements and tight delivery schedules. This results in zero room for experimentation, disruption, risk, or unproven methods.

AI by comparison, is evolving at lightspeed. Every other day brings new research on the latest methods, expanded possibilities, and new frontiers. However, most of this research, only exists in the lab, built upon carefully curated data that bears little resemblance to the noisy, unstructured, unlabeled, or as is often the case, the complete absence of data that exists in the real world. In stark contrast to manufacturing, AI rarely takes the time to validate itself under real-world conditions. The two industries couldn’t be further apart in their approaches.

In a lab, reaching 99% accuracy is a laudable achievement. In the manufacturing environment, a remaining 1% error rate is an unacceptably high failure, defect, or safety risk that can have severe real-world consequences. Given this dichotomy, how might we introduce the latest AI solutions into such a precise, constrained environment? And how might we experiment with, validate and deploy AI solutions in a way that doesn’t introduce risk, cost, downtime, or some combination of all three? These are the questions we were asking ourselves when tasked with training and deploying AI on our clients’ factory floors.

Using simulation to develop AI solutions

The most obvious solution was to bring the manufacturing environment into the lab. That is, to recreate the factory floor in a simulated environment where we can develop our AI solutions without fear of downtime or damaging state-of-the-art equipment.

A simulated environment gives us total control over factory conditions, allowing us to change parameters, experiment with, disrupt, and validate

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