Finding the blind spot of your copilot
The recent announcement of GitHub Copilot raised a few eyebrows in the Rafinex office. It also got us thinking: Is this really the best that we can hope for from AI? Copilot is clearly a very effective tool to speed up many boilerplate coding tasks. But what about when you need to solve a new sui generis problem? Is it sufficient to just regurgitate past software solutions if you aim for something innovative and unknown? Will AI warrant that previous coding mistakes be identified, sorted, and remedied? We have doubts.
Rafinex’ stochastic optimization approach turns this on its head. Imagine a new modular approach for body-in-white work for a next-gen automotive EV’s battery skateboard platform. Instead of saying, “here’s what’s [presumably…!] worked before”, our algorithms start from a clean plate. It empowers the designer to push the limits and anticipate a full spectrum of variation and uncertainties in loads and material properties to develop one new and robust solution. So, instead of betting on AI to deliver a miraculous copilot … You’d instead use your head and our optimization algorithms.
After all, most innovations start with a clever idea!
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