Accurate traffic forecasting is crucial for effective urban planning and transportation management, enabling efficient resource allocation and enhanced travel experiences. However, existing models often face limitations in generalization, struggling with zero-shot prediction on unseen regions and cities, as well as diminished long-term accuracy. This is primarily due to the inherent challenges in handling the spatial and temporal heterogeneity of traffic data, coupled with the significant distribution shift across time and space. In this work, we aim to unlock new possibilities for building versatile, resilient and adaptive spatio-temporal foundation models for traffic prediction. To achieve this goal, we introduce a novel foundation model, named OpenCity, that can effectively capture and normalize the underlying spatio-temporal patterns from diverse data characteristics, facilitating zero-shot generalization across diverse urban environments. OpenCity integrates the Transformer architecture with graph neural networks to model the complex spatio-temporal dependencies in traffic data. By pre-training OpenCity on large-scale, heterogeneous traffic datasets, we enable the model to learn rich, generalizable representations that can be seamlessly applied to a wide range of traffic forecasting scenarios. Experimental results demonstrate that OpenCity exhibits exceptional zero-shot predictive performance. Moreover, OpenCity showcases promising scaling laws, suggesting the potential for developing a truly one-for-all traffic prediction solution that can adapt to new urban contexts with minimal overhead. We made our proposed OpenCity model open-source and it is available at the following link: https://github.com/HKUDS/OpenCity.

While it upends the art world, AI-powered technology is sweeping through another industry: Fast food. Technology powered by AI has been used in restaurants for some time to improve customer experience and monitor internal expenses. Now, AI-powered voice bots will be putting in your orders. Taco Bell's parent company Yum Brands recently shared that it's testing an AI-powered conversational bot that takes orders in the drive-thru lane. The AI voice bot could help the chain "potentially automate ordering," according to Business Insider.

Starting Monday, drive-thru customers at two Panera Bread locations in upstate New York will have their orders taken by a computer in a test of artificial intelligence technology's accuracy and ability to decrease service times. The sandwich chain is the latest restaurant company to invest in potential improvements to the drive-thru experience. A surge in drive-thru ordering during the Covid pandemic led to long lines of cars wrapped around restaurants, pushing chains to focus on speed of service and order accuracy. For example, McDonald's has also been working to automate its drive-thru lane, announcing a partnership last year with IBM to work toward that goal. Yum Brands' Taco Bell and Restaurant Brands International's Burger King have been building double drive-thru lanes at some locations to allow customers to pick up their digital orders more quickly.