Neuroinformatics, Neural Networks and Neurocomputers for Brain-inspired Computational Intelligence

N. Kasabov
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Abstract

The talk discusses briefly current challenges in artificial intelligence (AI), including: efficient learning of data (interactive, adaptive, life-long; transfer); interpretability and explainability; personalised predictive modelling and profiling; multiple modality of data (e.g. genetic, clinical, behaviour, cognitive, static, temporal, longitudinal); computational complexity; energy consumption; human-machine interaction.
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脑启发计算智能的神经信息学、神经网络和神经计算机
讲座简要讨论了人工智能(AI)当前面临的挑战,包括:有效的数据学习(交互式,自适应,终身;转移);可解释性和可解释性;个性化预测建模和分析;多模态数据(如遗传、临床、行为、认知、静态、时间、纵向);计算复杂度;能源消耗;人机交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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