Over recent years artificial intelligence has been developing rapidly, and if anything, the pace is now accelerating. In generative AI (Gen AI) in particular, we’re seeing ever-larger models and richer modes, encompassing text, image, video, speech, and code. We’re seeing the evolution of multiple application areas, and an absolute proliferation of potential use cases. In most areas of business, the excitement is almost palpable.
Engineering is one such area – but it is a sector in which special rules and constraints apply. While creativity is essential, engineers face unique requirements: precision, regulation, industrial standards, and their enterprise's low risk tolerance – after all, this is a discipline that includes autonomous vehicles. Engineering applications often function ‘in the real world’ – where operating environments can be harsh (or disconnected from ecosystems) and computation requirements can be more limited than in cloud or on-premise implementations.
