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In a study published in Nature in October 2016, we introduced a form of memory-augmented neural network called a differentiable neural computer (DNC), and demonstrated that it can learn to use its memory to answer questions about complex, structured data, including artificially generated stories, family trees, and even a map of the London Underground. The DNC code is available on DeepMind's GitHub repository here.dUK快充网络
Neural networks excel at pattern recognition and quick, reactive decision-making, but we are only just beginning to build neural networks that can think slowly – that is, deliberate or reason using knowledge. We show how neural networks and memory systems can be combined to make learning machines that can store knowledge quickly and reason about it flexibly. These models, which we call differentiable neural computers (DNCs), can learn from examples like neural networks, but they can also store complex data like computers.dUK快充网络
When we designed DNCs, we wanted machines that could learn to form and navigate complex data structures on their own. At the heart of a DNC is a neural network called a controller, which is analogous to the processor in a computer. A controller is responsible for taking input in, reading from and writing to memory, and producing output that can be interpreted as an answer.dUK快充网络
Differentiable neural computers learn how to use memory and how to produce answers completely from scratch. They learn to do so using the magic of optimisation: when a DNC produces an answer, we compare the answer to a desired correct answer. Over time, the controller learns to produce answers that are closer and closer to the correct answer. In the process, it figures out how to use its memory.dUK快充网络
We hope that DNCs provide both a new tool for computer science and a new metaphor for cognitive science and neuroscience: here is a learning machine that, without prior programming, can organise information into connected facts and use those facts to solve problems.dUK快充网络 |
