Wednesday, July 24, 2024

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Unlocking Automotive Innovation and Potential of Large Engineering Models

Artificial intelligence has been making great strides across industries, and the automobile sector is no exception. Presently, it is focused on self-driving cars and improving battery efficiency, both of which have picked up at a faster rate than expected, but AI is going to change the future of the automobile industry in ways beyond these current limitations.

black car
Photo by Sarmad Mughal on Pexels.com

Recent advances in AI at picture generation Dalle-2 and text generation ChatGPT have shown how AI systems learn quickly. Of late, this has been realized through the fine, readily available high-quality data used in training Large Language Models. However, to effectively apply AI in the automotive sector, there is an explicit necessity to go beyond LLMs into LEMs.

This makes a huge possibility with LEMs the achievement of optimality in design within stipulated parameters, whether they be physical laws or manufacturing constraints. Just like Henry Ford did to the automotive industry with the introduction of the production line, the LEM could do the same for the stalling electric vehicle market by coming up with solutions to bring them closer to the people and into stiff competition. LEMs development requires large volumes of engineering data, which can be created through high-fidelity, scalable engineering simulators, augmented with real-world data.

For instance, the application of LEMs in the sphere of electric motor design may result in disruptive innovation that will allow their unintuitive free-form motor geometry shapes and harmonization of all powertrain components. This goes well beyond human engineers’ capabilities and has the potential to unlock significant advancements in this field.

Besides the development of LEMs, improving battery technology is receiving attention in the automotive industry. Toyota and others are developing solid-state batteries for improved range and faster charging. Microsoft applies AI technology to screen a candidate material class of more than 32 million, shaving the material discovery process down from years to just days.

While the media is touting generative AI tools like ChatGPT, it will be far more exciting to see AI being applied to achieve results way beyond human ability. While the automotive industry may not be that glamorous place some people foresaw with notably autonomous vehicles and million-mile batteries still in the future, it will garner much attention. However, large engineering models that the automotive industry will embrace will bring us closer to these targets, potentially revolutionizing this sector beyond what is currently known.

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