Deep Learning and AI Alignment

深度学习和人工智能的结合

• 批准号: CRC-2021-00500
• 负责人: Grosse, Roger
• 金额: $ 3.64万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749675
• 关键词: Deep; Learning; AI; Alignment

Neural networks have become the core machine learning technology across many areas of Artificial Intelligence, due to their ability to automatically learn high-level feature representations. While networks have achieved impressive performance in terms of the accuracy of their predictions, there remain several obstacles: neural networks are often overconfident about their predictions, sensitive to shifts in the data distribution, or prone to exploit spurious correlations. Furthermore, neural net architectures are largely based on pattern recognition, whereas extending them to more difficult problems will require more deliberative reasoning.Much of Dr. Grosse's research has focused on understanding neural net training dynamics - theways in which what happens during training affects the final outcome. His program is unique in that while most such work has focused on the pattern recognition setting, he will extend such analyses to cases where the network performs sophisticated reasoning or optimization at test time by meeting the following objectives: 1) extending prior investigations of neural net training dynamics to settings where the architectures perform more sophisticated planning, reasoning, or optimization, and where the training regime may involve multiple neural nets trained to different objectives2) understanding how and why these settings can lead to different patterns of generalization compared with more traditional neural nets3) understanding the reasons for a neural net's predictions by determining how the predictions would have changed if the network were trained on slightly different data; and 4) developing algorithms for training a neural net to produce not only an answer, but also an independently checkable justification for that answer. Ultimately, this program will lead to greater interpretability of the network's predictions, as well as reducing the reliance on spurious correlations in the data.

神经网络已经成为人工智能许多领域的核心机器学习技术，因为它们能够自动学习高级特征表示。虽然神经网络在预测的准确性方面取得了令人印象深刻的表现，但仍然存在一些障碍：神经网络通常对其预测过于自信，对数据分布的变化敏感，或者倾向于利用虚假的相关性。此外，神经网络架构在很大程度上是基于模式识别的，而将其扩展到更困难的问题将需要更多的深思熟虑的推理。格罗斯博士的大部分研究都集中在理解神经网络训练动力学--训练过程中发生的事情影响最终结果的方式。他的计划是独一无二的，因为虽然大多数此类工作都集中在模式识别设置上，但他将这些分析扩展到网络通过满足以下目标在测试时执行复杂推理或优化的情况：1）将神经网络训练动态的现有研究扩展到架构执行更复杂的规划、推理或优化的设置，以及训练机制可能涉及针对不同目标训练的多个神经网络2）理解这些设置如何以及为什么会导致与更传统的神经网络相比不同的泛化模式3）了解神经网络预测的原因，通过确定如果网络被稍微训练，预测将如何改变不同的数据;以及4）开发用于训练神经网络的算法，以不仅产生答案，而且产生该答案的可独立检查的理由。最终，该计划将提高网络预测的可解释性，并减少对数据中虚假相关性的依赖。