Cognitive computing journey

PPAA '14 Pub Date : 2014-02-16 DOI:10.1145/2567634.2567646
D. Nahamoo
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引用次数: 7

Abstract

Building intelligent machines has been a long dream of humanity. While the journey has been difficult and slow, the progress in Machine Learning, Optimization Techniques and advancement in Deep Belief Networks offers promising ways to engineer cognitive systems. The science behind cognitive computing seeks to develop systems that emulate human brain functions such as perception, knowledge accumulation, goal planning, and logical inference. Cognitive systems will operate at a speed and an informational capacity that far exceeds human capability. They will serve to act as an advisor, partner, helpmate, and co-creator to the humans, collaborating on human terms. Cognitive computing is a fundamentally new computing paradigm for tackling real world problems, exploiting enormous amounts of information using massively parallel machines that interact with humans and other cognitive systems. Cognitive systems will bring human-like reasoning to the problems of Big Data, and will also permit us to expand into the white space of domains that require human-like cognition but that either exceed human capacity or are impossible for a live human presence. In this talk, I will review the past progress and discuss the future challenges. I will address the architectural challenges of building a general purpose system of systems that can learn, can reason, and can interact in a human natural way.
认知计算之旅
制造智能机器一直是人类长久以来的梦想。虽然这一过程艰难而缓慢,但机器学习、优化技术和深度信念网络的进步为设计认知系统提供了有希望的方法。认知计算背后的科学旨在开发模拟人类大脑功能的系统,如感知、知识积累、目标规划和逻辑推理。认知系统将以远远超过人类能力的速度和信息容量运行。它们将充当人类的顾问、伙伴、助手和共同创造者,以人类的方式进行协作。认知计算是一种全新的计算范式,用于解决现实世界的问题,利用与人类和其他认知系统交互的大规模并行机器来开发大量信息。认知系统将为大数据问题带来类似人类的推理,也将允许我们扩展到需要类似人类认知的空白领域,但这些领域要么超出了人类的能力,要么不可能有活生生的人类存在。在这次演讲中,我将回顾过去的进展并讨论未来的挑战。我将讨论构建一个通用系统的架构挑战,这个系统可以学习、推理,并以人类自然的方式进行交互。
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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