Learning higher level representations and invariant transformations

学习更高层次的表示和不变变换

• 批准号: 341366-2007
• 负责人: Vincent, Pascal
• 金额: $ 1.46万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=361539
• 关键词: Learning; higher; level; representations; invariant

Can we design an artificial intelligence system that, once plunged in an unknown world, like a newborn baby opening his eyes for the first time, would be able to form high level concepts exclusively from the enormous quantity of raw sensory information that it receives? Such a system should be able to discover deep regularities and stable structures in this ever changing information stream, so that it may eventually form its own subjective notions of space, time, of things (more or less stable entities) moving around. This research program aims at exploring new fundamental principles to guide the building of next generation autonomous learning systems with this kind of abilities. Our approach will be based on computer implementations capable of growing "layers of understanding", stacked one upon the other. Each such layer shall be building a slightly higher level "understanding" of the representation extracted by the previous one, by exploiting regularities that the previous layer failed to capture. We intend to focus on a particular class of regularities called "invariant transformations", that we want to automatically discover and learn. The layer by layer extraction of ever higher level, ever more meaning carrying representations, is expected to be a key element for opening the door to near human-level performance in artificial learning systems (thus advancing the state-of-the-art in the fields of machine learning, pattern recognition and data mining). It is also expected to produce computerized systems capable of far more accurate predictions from past observed data. This would have direct technological applications in a wide range of fields where superior prediction systems are immensely useful: from engineering to finance, from business decision making to medicine.

我们能否设计一个人工智能系统，一旦陷入一个未知的世界，就像一个新生儿第一次睁开眼睛一样，能够完全从它接收到的大量原始感官信息中形成高级概念？这样一个系统应该能够在不断变化的信息流中发现深层的稳定结构，这样它就可以最终形成自己对空间、时间和事物（或多或少稳定的实体）的主观概念。本研究计划旨在探索新的基本原则，以指导下一代自主学习系统的建设与这种能力。我们的方法将基于计算机实现，能够增长“理解层”，一层一层堆叠。每个这样的层都应该通过利用前一层未能捕获的错误来构建对前一层提取的表示的稍微更高级别的“理解”。我们打算专注于一个特殊的类别的转换称为“不变的转换”，我们希望自动发现和学习。逐层提取越来越高的层次，越来越多的含义承载表示，预计将成为打开人工学习系统中接近人类水平性能的大门的关键要素（从而推进机器学习，模式识别和数据挖掘领域的最新技术）。它还有望产生计算机化的系统，能够根据过去的观测数据进行更准确的预测。这将在上级预测系统非常有用的广泛领域中具有直接的技术应用：从工程到金融，从商业决策到医学。