By Matthew Hutson
How many parks are near the new home you’re thinking of buying? What’s the best dinner-wine pairing at a restaurant? These everyday questions require relational reasoning, an important component of higher thought that has been difficult for artificial intelligence (AI) to master. Now, researchers at Google’s DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test.
Humans are generally pretty good at relational reasoning, a kind of thinking that uses logic to connect and compare places, sequences, and other entities. But the two main types of AI—statistical and symbolic—have been slow to develop similar capacities. Statistical AI, or machine learning, is great at pattern recognition, but not at using logic. And symbolic AI can reason about relationships using predetermined rules, but it’s not great at learning on the fly.
The new study proposes a way to bridge the gap: an artificial neural network for relational reasoning. Similar to the way neurons are connected in the brain, neural nets stitch together tiny programs that collaboratively find patterns in data. They can have specialized architectures for processing images, parsing language, or even learning games. In this case, the new “relation network” is wired to compare every pair of objects in a scenario individually. “We’re explicitly forcing the network to discover the relationships that exist between the objects,” says Timothy Lillicrap, a computer scientist at DeepMind in London who co-authored the paper.
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