In this talk, we first share about CUDA-X AI, a collection of software-acceleration libraries built on top of CUDA, Nvidia’s parallel programming model, that provides essential optimizations for deep learning, machine learning and high-performance computing (HPC).
In the second part, we share about our research in machine reasoning. There is growing interest in advancing AI in the reasoning field, as reasoning is one of the main abilities associated with intelligence. Deep learning performs exceptionally well at pattern recognition and in recent times, it is advancing into the reasoning field, from relational networks for question answering and transparency-by-design networks for visual reasoning. In this talk, we will share an alternate and complementary paradigm for performing reasoning with a type theoretic approach.
Zhangsheng is a solutions architect in deep learning at NVIDIA, engaging customers from higher education and research institutions on AI projects. He also works on machine reasoning, researching how to successfully merge neural and symbolic AI techniques to advance knowledge representation and reasoning.