How Large language Models (LLMS) can support automotive OEMs when applied with the right use case?
Like with most technologies, LLMs are not one-sizefits-all. So, despite their growing popularity, large language models can only work when leveraged in the right avenues.
Small-scale OEMs with limited financial resources, often focus on establishing a place in the market over investing in expensive language models. On the other hand, medium to large-scale OEMs, can leverage LLMs to enhance existing services.
An illustration of an LLM application is a virtual assistant integrated into vehicles, where users can inquire about their car’s features, receive personalised navigation guidance, and engage in enjoyable conversations. Thereby, adding a desirable aspect to the driving experience. On the other hand, crucial functionalities like automatic repair detection, software updates, and fast battery charging are essential for vehicle performance.
LLMs also significantly impact customer support, by effectively handling inquiries and troubleshooting. They aid supply chain management by predicting demand and optimising inventories.
What is the scope of LLMs in the automotive industry and why are they necessary for the auto industry. Do you think the industry should weigh on its benefits before jumping onto the next big tech trend? Kindly comment.
LLMs offer innovative solutions to age-old challenges.
They can analyse vast amounts of data, such as, preferences and driving habits, to offer personalised recommendations for services, entertainment, and more.
Now, should the industry weigh the benefits before hopping onto the next big tech trend? Absolutely. Despite its tremendous potential, there are challenges regarding safety.
Bu hikaye The Machinist dergisinin August 2023 sayısından alınmıştır.
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Bu hikaye The Machinist dergisinin August 2023 sayısından alınmıştır.
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