The researchers at DeepMind, which created the champion Go-playing robot AlphaGo, are working on an approach that could prove significant in the quest to make machines as intelligent as we are.
In two papers published this week and reported by New Scientist, researchers at the Alphabet subsidiary describe efforts to teach computers about relational reasoning, a cognitive capability that is foundational to human intelligence.
Simply put, relational reasoning is the ability to consider relationships between different mental representations, such as objects, words, or ideas. This kind of reasoning is both crucial to human cognitive development and vital to solving just about any problem.
Most existing machine-learning systems don’t try to understand the relationship between concepts. A vision system can identify a dog or a cat in a picture, for example, but it doesn’t know that the dog is chasing the cat.
The two systems developed at DeepMind solve that by modifying existing machine-learning methods to make them capable of learning about physical relationships between static objects, as well as the behavior of moving objects over time.
They demonstrate the first capability using CLEVR, a data set of simple objects. After training, they can ask the system whether one object is in front of another, or which object is closest. Their results are dramatically better than anything achieved before, even exceeding human performance in some cases.