Game on: How rock, paper, scissors became the future of factory quality control

In this episode, we explore how accessible AI vision technology has become - and the surprising challenges that come with it.

Luke Walsh from Brainboxes built an AI rock, paper, scissors game using open-source software and hardware costing under £1,000. The playful demo masks serious industrial applications: from catching defects in car seat stitching with 22 cameras to monitoring hazardous environments without putting maintenance engineers at risk.

The conversation covers the technical realities of sub-200-millisecond latency, the stubborn resistance of factory maintenance teams, and why Overall Equipment Effectiveness (OEE) has been the missing piece in factory automation. Luke explains how vision systems can now replace invasive sensors, monitor quality without human inconsistency, and prove their worth to sceptical teams - one tote bin at a time.

Tune in to learn why the best way to deploy AI in factories is to start small, prove value fast, and never assume your training data covers every possible hand gesture.

#AI #computervision #manufacturing #opensource #bias #wetalkiot

Summary of this week's episode:

What Brainboxes does

How AI recognises hand gestures in under 200 milliseconds

Three reasons to choose open source: Latest models, trained engineers, community support

Real-world applications: Quality control and OEE

The car seat stitching use case: 22 cameras, one seat, zero tolerance

Beyond quality: Monitoring hazardous environments and non-invasive throughput measurement

Winning over sceptical maintenance teams

The tote bin story: When data settles a night shift dispute

The bias challenge: When "scissors" becomes a swearing gesture

The future of industrial AI vision systems and Jevons Paradox