DeepMind uses machine learning to mimic “slow” thinking
DeepMind, a Google subsidiary, explained in a new paper published in Nature, a new approach to machine learning that uses something called a differentiable neural computer. The new computer isn’t a physical piece of hardware, it’s more of a technique for organizing information and then applying that prior knowledge to unique problems. DeepMind’s technique merges notions of memory with more traditional neural networks using a “controller.” The controller saves information by either storing it in a new location or overwriting a previously occupied location. Throughout this process, an association between the information is formed via the timeline of when new data was written in.
The controller uses that same chronology along with the actual content of what has been saved to retrieve information. The framework created is navigable and proves itself effective for drawing insights from graph data structures. These graph structures are complex representations of data that are commonly used to represent things like customer purchasing preferences and GPS navigation information. DeepMind tested its differentiable neural computer on the London Underground and was successful at generating routes from the structured data. According to the company, the next step in development will be trying the new algos on larger data sets.
