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.
[A] 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.
He and his team challenged their relation network with several tasks. The first was to answer questions about relationships between objects in a single image, such as cubes, balls, and cylinders. For example: "There is an object in front of the blue thing; does it have the same shape as the tiny cyan thing that is to the right of the gray metal ball?" For this task, the relation network was combined with two other types of neural nets: one for recognizing objects in the image, and one for interpreting the question. Over many images and questions, other machine-learning algorithms were right 42% to 77% of the time. Humans scored a respectable 92%. The new relation network combo was correct 96% of the time, a superhuman score [arxiv.org], the researchers report in a paper posted last week on the preprint server arXiv.