Deep Learning and AI Alignment

点击翻译按钮获取中文摘要

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.