Deep learning for cognitive computing

认知计算的深度学习

• 批准号: 490785-2015
• 负责人: Bengio, Yoshua
• 金额: $ 14.93万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618881
• 关键词: Deep; learning; cognitive; computing

The quest for artificial intelligence has advanced substantially in recent years, thanks in good part to breakthroughs in our ability to provide actionable knowledge to computers through what is called machine learning: provided with enough examples, the computer can learn to perform a task, and generalize correctly to new cases. These breakthroughs arose from a machine learning approach called deep learning, inspired from brains and based on older work on artificial neural networks. Initial progress has been in pattern recognition, especially for speech recognition and computer vision. These areas have been revolutionized by deep learning, both in academia and in the information technology industry. This project aims at contributing to the expansion of neural networks outside of their traditional pattern recognition domain into what is called cognitive computing, a strategic area for IBM, one that encompasses artificial intelligence research domains such as natural language understanding, reasoning, machine translation and dialogue, but also takes advantage of progress in computer vision and speech recognition.

近年来，对人工智能的探索取得了很大进展，这在很大程度上要归功于我们通过所谓的机器学习向计算机提供可操作知识的能力的突破：只要有足够的例子，计算机就可以学习执行任务，并正确地对新情况进行概括。这些突破源于一种名为深度学习的机器学习方法，该方法受到大脑的启发，并基于人工神经网络方面的早期工作。在模式识别方面取得了初步进展，特别是语音识别和计算机视觉。无论是在学术界还是在信息技术行业，深度学习都给这些领域带来了革命性的变化。该项目旨在将神经网络从传统的模式识别领域扩展到所谓的认知计算，这是IBM的一个战略领域，涵盖自然语言理解、推理、机器翻译和对话等人工智能研究领域，但也利用了计算机视觉和语音识别方面的进展。