Computer Vision and How it could affect the Future of Drive-Thru
A New AI Power Center
White Castle is partnering with SoundHound to roll out voice AI at select drive-thrus. The technology will be added to over 100 White Castle drive-thrus by the end of 2024. Many of these drive-thru lanes will operate 24/7.
White Castle initially tested drive-thru AI in 2020 through a partnership with Mastercard. SoundHound provided voice recognition for the trial, while Rekor Systems offered vehicle recognition. This trial served as an “incubation phase” for the companies, and SoundHound now works directly with White Castle.
The voice AI technology will allow customers to place their orders through a voice interface. This will free employees to focus on other tasks, such as food preparation and customer service. The technology is also expected to improve the accuracy of orders and reduce wait times.
Computer Vision
Computer vision has the potential to significantly improve restaurant drive-thru operations by providing real-time data and alerts to staff, enabling them to quickly and effectively manage customer orders. Here are three examples of how computer vision could be used to improve restaurant drive-thru operations:
License Plate Recognition: Computer vision can be used to recognize the license plates of vehicles as they enter the drive-thru lane. This would allow the restaurant to quickly identify returning customers, providing an opportunity to offer personalized menu recommendations and promotions. Moreover, this could also enable the restaurant to keep track of wait times and optimize staffing levels accordingly.
Vehicle Detection: Computer vision can detect vehicles in the drive-thru lane, enabling the restaurant to monitor traffic and optimize staffing levels. For example, if multiple vehicles are in the drive-thru lane, the restaurant can adjust staffing levels to ensure that orders are taken and fulfilled quickly.
Order Accuracy: Computer vision can be used to monitor the preparation of customer orders, ensuring that all items are included and prepared to the customer’s specifications. This would reduce errors and increase order accuracy, resulting in a better customer experience. Additionally, computer vision can monitor food safety and quality, ensuring that all food items are cooked to the appropriate temperature and quality standards.
Computer vision has the potential to significantly improve restaurant drive-thru operations by providing real-time data and alerts to staff, enabling them to optimize staffing levels, monitor order accuracy, and improve the overall customer experience. By leveraging the power of computer vision, restaurants can increase efficiency, reduce wait times, and provide customers a more personalized and accurate drive-thru experience.
CHAT GPT VS GOOGLE BARD
Google BARD (Bidirectional Encoder Representations from Transformers-based Auto Regressive Decoder) and Chat GPT (Generative Pre-trained Transformer) are both natural language processing (NLP) models developed by Google and OpenAI respectively, but they have some key differences.
Training data: BARD was trained on a large amount of text data from the web, while GPT was trained on a more diverse range of data, including books, articles, and websites. This gives GPT a broader understanding of language and allows it to generate more creative and diverse responses.
Architecture: Both models use transformer architecture, but BARD has a separate encoder and decoder, while GPT only has a decoder. This means that BARD is better suited for tasks that require a bidirectional understanding of text, such as translation, while GPT is more suited for tasks that involve generating text, such as chatbots or text completion.
Focus: BARD was developed primarily for language translation and other text-to-text tasks, while GPT was developed for generating natural language text in response to a given prompt or input.
In summary, while both BARD and GPT are powerful NLP models developed by Google and OpenAI respectively, they have some key differences in their training data, architecture, focus, and output format, which make them better suited for different types of language processing tasks.
The above is an excerpt from the Restaurant Technology 2023 eBook.
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