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Loihi: a neuromorphic manycore processor with on-chip learning. A million spiking-neuron integrated circuit with a scalable communication network and interface. DNA methylation-based classification of central nervous system tumours. Google’s neural machine translation system: bridging the gap between human and machine translation. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms.