Deep Learning and AI Alignment

由于神经网络具有自动学习高级特征表示的能力，因此已经成为人工智能许多领域的核心机器学习技术。虽然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：神经网络通常对他们的预测过于自信，对数据分布的变化敏感，或者容易利用虚假的相关性。此外，神经网络架构很大程度上基于模式识别，而将它们扩展到更困难的问题将需要更多的审慎推理。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上，即训练过程中发生的事情如何影响最终结果。他的项目的独特之处在于，虽然大多数此类工作都集中在模式识别设置上，但他将把这种分析扩展到网络在测试时执行复杂推理或优化的情况下，通过满足以下目标：1)将神经网络训练动力学的先前研究扩展到架构执行更复杂的规划、推理或优化的设置中；当训练机制可能涉及多个针对不同目标进行训练的神经网络时，2)理解这些设置与更传统的神经网络相比如何以及为什么会导致不同的泛化模式；3)通过确定如果网络在稍微不同的数据上进行训练，预测会如何变化，来理解神经网络预测的原因；4)开发训练神经网络的算法，使其不仅能生成答案，还能生成可独立验证的答案。最终，该程序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。

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.