Скачать или смотреть AGI-16 Cosmo Harrigan - Deep Learning for AGI: Survey of Recent Developments

Cosmo Harrigan presents his talk "Deep Learning for AGI: Survey of Recent Developments" (slides:http://machineintelligence.org/deep-l...) at the Ninth Conference on Artificial General Intelligence (AGI-16) in New York (http://agi-conf.org/2016/) as part of the workshop asking "Can Deep Neural Networks solve the problems of Artificial General Intelligence?" (http://agi-conf.org/2016/workshops/).

Abstract:
The relevance of deep learning to the field of Artificial General Intelligence research is described, in terms of the expanding scope of deep learning model designs and the increasing combination of deep learning with other methods to form hybrid architectures. Deep learning is a rapidly expanding research area, and various groups have recently proposed novel extensions to earlier deep learning models, including: generative models; the ability to interface with external memory and other external resources; Neural Turing Machines which learn programs; deep reinforcement learning; neuroevolution; intrinsic motivation and unsupervised learning; and more complex network models.

The presentation is organized with a view towards the integration of additional abilities into deep learning architectures, including: planning; reasoning and logic; data efficient learning and one-shot learning; program induction; additional learning algorithms other than backpropagation; more sophisticated techniques for unsupervised learning and reinforcement learning; and structured prediction. We can view deep learning research as making significant contributions relevant to AGI, but also note that future progress in the field will likely depend on integrating threads of research from cognitive science, machine learning, universal artificial intelligence and symbolic artificial intelligence, resulting in systems that significantly extend the boundaries of what might be considered "deep learning" today.