The future of deep learning, according to its pioneers

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Deep neural networks will move past their shortcomings without help from symbolic artificial intelligence, three pioneers of deep learning argue in a paper published in the July issue of the Communications of the ACM journal.

In their paper, Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, recipients of the 2018 Turing Award, explain the current challenges of deep learningand how it differs from learning in humans and animals. They also explore recent advances in the field that might provide blueprints for the future directions for research in deep learning.

Titled “Deep Learning for AI,” the paper envisions a future in which deep learning models can learn with little or no help from humans, are flexible to changes in their environment, and can solve a wide range of reflexive and cognitive problems.

The challenges of deep learning

Deep learning is often compared to the brains of humans and animals. However, the past years have proven that artificial neural networks, the main component used in deep learning models, lack the efficiency, flexibility, and versatility of their biological counterparts.

In their paper, Bengio, Hinton, and LeCun acknowledge these shortcomings. “Supervised learning, while successful in a wide variety of tasks, typically requires a large amount of human-labeled data. Similarly, when reinforcement learning is based only on rewards, it requires a very large number of interactions,” they write.

Supervised learning is a popular subset of machine learning algorithms, in which a model is presented with labeled examples, such as a list of images and their corresponding content. The model is trained to find recurring patterns in examples that have similar labels. It then uses the learned patterns to associate new examples with the right labels. Supervised learning is especially useful for problems where labeled examples are abundantly available.

Reinforcement learning is another branch of machine learning, in which an “agent” learns to maximize “rewards” in an environment. An environment can be as simple as a tic-tac-toe board in which an AI player is rewarded for lining up three Xs or Os, or as complex as an urban setting in which a self-driving car is rewarded for avoiding collisions, obeying traffic rules, and reaching its destination. The agent starts by taking random actions. As it receives feedback from its environment, it finds sequences of actions that provide better rewards.

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