This paper introduces and explores a new programming paradigm, Model-based Programming, designed to address the challenges inherent in applying deep learning models to real-world applications. Despite recent significant successes of deep learning models across a range of tasks, their deployment in real business scenarios remains fraught with difficulties, such as complex model training, large computational resource requirements, and integration issues with existing programming languages. To ameliorate these challenges, we propose the concept of 'Model-based Programming' and present a novel programming language - M Language, tailored to a prospective model-centered programming paradigm. M Language treats models as basic computational units, enabling developers to concentrate more on crucial tasks such as model loading, fine-tuning, evaluation, and deployment, thereby enhancing the efficiency of creating deep learning applications. We posit that this innovative programming paradigm will stimulate the extensive application and advancement of deep learning technology and provide a robust foundation for a model-driven future.

A wide range of sensor-rich, networked embedded systems are being created that must operate robustly for years in the face of novel failures by managing complex autonomic processes. These systems are being composed, for example, into vast networks of space, air, ground, and underwater vehicles. Our objective is to revolutionize the way in which we control these new artifacts by creating reactive model-based programming languages that enable everyday systems to reason intelligently and enable machines to explore other worlds. A model-based program is state and fault aware; it elevates the programming task to specifying intended state evolutions of a system. The program's executive automatically coordinates system interactions to achieve these states, entertaining known and potential failures, using models of its constituents and environment.

A wide range of sensor-rich, networked embedded systems are being created that must operate robustly for years in the face of novel failures by managing complex autonomic processes. Our objective is to revolutionize the way in which we control these new artifacts by creating reactive model-based programming languages that enable everyday systems to reason intelligently and enable machines to explore other worlds. The program's executive automatically coordinates system interactions to achieve these states, entertaining known and potential failures, using models of its constituents and environment. Model-based programming is being generalized to hybrid discrete-continuous systems and the coordination of networks of robotic vehicles.

A wide range of sensor-rich, networked embedded systems are being created that must operate robustly for years in the face of novel failures by managing complex autonomic processes. These systems are being composed, for example, into vast networks of space, air, ground, and underwater vehicles. Our objective is to revolutionize the way in which we control these new artifacts by creating reactive model-based programming languages that enable everyday systems to reason intelligently and enable machines to explore other worlds. A model-based program is state and fault aware; it elevates the programming task to specifying intended state evolutions of a system. The program's executive automatically coordinates system interactions to achieve these states, entertaining known and potential failures, using models of its constituents and environment. At the executive's core is a method, called CONFLICT-DIRECTED A*, which quickly prunes promising but infeasible solutions, using a form of one-shot learning. This approach has been demonstrated on a range of systems, including the National Aeronautics and Space Administration's Deep Space One probe. Model-based programming is being generalized to hybrid discrete-continuous systems and the coordination of networks of robotic vehicles.