Learning higher level representations and invariant transformations

我们能否设计一个人工智能系统，一旦投入未知世界，就像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？这样的系统应该能够在不断变化的信息流中发现深刻的规律和稳定的结构，从而最终形成自己对空间、时间和周围移动的事物（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立。我们的方法将基于能够不断增长的“理解层”的计算机实现，一个堆叠在另一个上。每个这样的层都应该通过利用前一层未能捕获的规律，对前一层提取的表示建立一个稍微更高层次的“理解”。我们打算专注于一类特殊的规律，称为“不变变换”，我们想要自动发现和学习。对更高层次、更有意义的表示的逐层提取，有望成为打开人工学习系统接近人类水平性能之门的关键因素（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系统，能够根据过去观测到的数据做出更准确的预测。这将在从工程到金融，从商业决策到医学等广泛领域产生直接的技术应用，在这些领域，卓越的预测系统非常有用。

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.