Autonomous Deep Learning for AI

在过去的几年里，人工智能（AI）取得了令人印象深刻的进展，这要归功于基于深度学习的系统的开发，深度学习是人工神经网络的现代实例。这些进展主要是在感知任务上，使用监督学习（通常来自人类标记的数据），以及认知任务-称为系统1任务-以无意识的方式快速执行，并与直觉（即，不容易表达）。在行动方面还有很多工作要做，还有认知任务--被称为系统2任务--以有意识的方式缓慢而顺序地执行，与推理有关，并以口头表达。行动很重要，因为它们允许学习者干预其环境，从而更容易发现因果关系。发现因果解释对于从训练数据所涵盖的典型情况中进行推广非常重要（目前，人类在这方面比机器好得多）。行动也很重要，因为它们允许学习者积极寻求知识，有目的地探索其环境以更好地理解它。最后，人工智能的实际应用当然通常需要机器以复杂的方式行动，例如，学习策略，而不仅仅是执行分类。在此背景下，拟议的研究结合了以下五个相互关联的研究目标：（1）探索基于共同学习语言和它所指的世界的扎根语言学习方法（2）探索获得知识的代理学习方法（包括因果关系）关于我们希望学习者用自然语言指代的环境（3）开发新的学习理论，该理论不依赖于相同的训练和测试分布的假设，而只需要保留因果机制（4）设计利用这种理论的深度学习架构和训练框架，以便学习独立的因果机制，当组合时，可用于构建因果解释和规划，以及将深度学习从系统1提升到联合系统1和系统2的能力，以及（5）以快速的研究周期在虚拟环境中训练这些系统进行基础语言学习和世界建模任务。

There has been impressive progress in artificial intelligence (AI) in the last few years thanks to the development of systems based on deep learning, the modern instantiation of artificial neural networks. These advances have been mostly on perception tasks, using supervised learning (often from human-labeled data), and for cognitive tasks - called system 1 tasks - which are performed fast, in an unconscious way, and associated with intuition (i.e., not easy to verbalize). Much remains to be done on the side of actions, as well as cognitive tasks - called system 2 tasks - which are performed slowly and sequentially, in a conscious way, and associated with reasoning and expressed verbally. Actions are important because they allow the learner to intervene in its environment, thus to more easily uncover causal relationships. Uncovering causal explanations is important to generalize far from the typical situations covered by the training data (and humans are much better at this than machines, currently). Actions are also important because they allow the learner to actively seek knowledge, to purposely explore its environment to better understand it. Finally, actual applications of AI of course often require machines to act in complicated ways, e.g., learn a policy, and not just perform a classification. In this context, the proposed research combines the following five interconnected research objectives: (1) explore grounded language learning methods based on jointly learning language and about the world to which it refers (2) explore agent-learning methods to acquire knowledge (including about cause and effect) about the environment that we wish the learner to refer to with natural language (3) develop new learning theory that does not rely on the assumption of a same training and test distribution to one where only the causal mechanisms need to be preserved (4) design deep learning architectures and training frameworks that exploit such a theory in order to learn independent causal mechanisms which, when combined, can be used for building causal explanations and for planning, as well as to lift deep learning from system 1 to joint system 1 and system 2 abilities, and (5) train such systems on grounded language learning and world modeling tasks in virtual environments with a fast research cycle.