Research talk 9 - The Next Frontier in AI Unsupervised Learning
Yann Lecun is one of the most famous scientist in Deep Learning domain. He is the creator of Convolutional Neural Networks and the director of AI Research at Facebook.
Deep learning has made great processes in several domains such as autonomous transportation and medical image understanding. But so far the most influential achievements have been made in Deep Learning are by supervised learning, in which the machine is trained with inputs labeled by humans. However the labeling work is time-consuming and expensive, and supervised is not the primary way of real human intelligence. In contrast, unsupervised learning is the more promising method of building real artificial intelligence.
AI systems today do not possess “common sense”, which humans and animals acquire by observing the world, acting in it, and understanding the physical constraints of it. So unsupervised learning is believed to be the key towards machines with common sense, which allows machines to learn from raw, unlabeled data, such as video or text.
The image above is very interesting. Yann likened the artificial intelligence problem to a cake. In this image he pointed out which part can be solved by different method, and he referred the main body to unsupervised learning. Both reinforcement learning and supervised learning can solve very small part of the whole problem, as these two methods are limited to certain problems where a direct guidance (e.g. labeled data or judgment) is available.
In this talk, two major research outputs he showed are Memory Networks and Generative Adversarial Networks. The former one provide a new architecture which implements a key-value function, enabling the neural networks to call very long term memories. The latter one is described as the most promising idea in Deep Learning so far by Yann. In this work, there are a image generater and a discriminator. The generater tries to fake a new image to confuse the discriminator. And the discriminator aims to distinguish the faked ones from real images. By training in a adversarial way, both two models will improve their performance. There’s no labeled data needed for GAN, but it can generate very impressive results.