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Over the course of this blog post, I will first contrast transfer learning with machine learning's most pervasive and successful paradigm, supervised learning. According to Andrew Ng, transfer learning will become a key driver of Machine Learning success in industry. Fuelled by advances in Deep Learning, more capable computing utilities, and large labeled datasets, supervised learning has been largely responsible for the wave of renewed interest in AI, funding rounds and acquisitions, and in particular the applications of machine learning that we have seen in recent years and that have become part of our daily lives. Even more so as transfer learning currently receives relatively little visibility compared to other areas of machine learning such as unsupervised learning and reinforcement learning, which have come to enjoy increasing popularity: Unsupervised learning -- the key ingredient on the quest to General AI according to Yann LeCun as can be seen in Figure 5 -- has seen a resurgence of interest, driven in particular by Generative Adversarial Networks.
Included below is a version of the talk in blog post form.1 This talk is about a new research agenda aimed at using machine learning to make AI systems safe even at very high capability levels. A task-directed AI system is a system that pursues a semi-concrete objective in the world, like "build a million houses" or "cure cancer." We'll model more advanced AI systems by just supposing that systems will continue to achieve higher scores in ML tasks. Suppose an AI system composes a story, and a human gives the system a reward based on how good the story is.4 This is similar to some RL tasks: the agent wants to do something that will cause it to receive a high reward in the future.
Sports analytics is routinely used to assign values to such things as shots taken or to compare player performance, but a new automated method based on deep learning techniques - developed by researchers at Disney Research, California Institute of Technology and STATS, a supplier of sports data - will provide coaches and teams with a quicker tool to help assess defensive athletic performance in any game situation. The innovative method analyzes detailed game data on player and ball positions to create models, or "ghosts," of how a typical player in a league or on another team would behave when an opponent is on the attack. "With the innovation of data-driven ghosting, we can now, for the first time, scalably quantify, analyze and compare detailed defensive behavior," said Peter Carr, research scientist at Disney Research. The researchers used a type of machine learning called deep learning, which uses brain-inspired programs called neural networks.