An AI for Image Recognition Spontaneously Gained a ‘Number Sense’
Many of us struggle with mathematical concepts, yet we’re all equipped with an innate “number sense,” or numerosity. Thanks to a strange group of “number neurons” buried in the visual cortex, human newborns, monkeys, cows, and other animals have the curious superpower to glance at a scene and intuitively gauge how much stuff is in it—long before knowing how to count.
Now AI has learned to do the same on its own.
In a study published last week in Science Advances, a team led by Dr. Andreas Nieder at the University of Tübingen in Germany found that a biologically-inspired deep neural network spontaneously gained a number sense—when it was only trained to recognize objects in an image.
Similar to the human brain, the artificial visual cortex gradually developed units with a specific sense of abstract quantity. Without the team ever explicitly teaching the AI what a number is, the network could discriminate between large and small amounts within an image, with each of its number units precisely “tuned” to a particular number. When challenged on cognitive tests for numerosity—derived from those for humans and monkeys—the AI even made mistakes reminiscent of biological brains.
It seems that “the spontaneous emergence of the number sense is based on mechanisms inherent to the visual system,” the authors concluded.
The study is the latest example of using machine intelligence to probe human intelligence. “We can now have hypotheses about how things happen in the brain and can go back and forth from artificial networks to real networks,” said Nieder.
Although we often associate numbers with counting and math, most of us can eyeball two boxes of donuts and intuitively gauge which box has more—especially when the numbers are relatively small.
By four months old, human infants can already discriminate two items from three. With age and experience, our sense of numerosity grows stronger, although even adults still struggle with larger numbers—imagine trying to distinguish a crowd of 100,000 from 125,000, for example.
Yet despite its critical role in numerical information processing, scientists have long scratched their heads at how the abstract sense emerges from number neurons. The classic cognitive psychology approach is to recruit toddlers and see how they respond to different quantities in colorful pictures; the neuroscience solution is to directly measure the electrical chattering of number neurons in animals.
Nieder and team decided on a drastically different method: cognitive psychology is increasingly relevant for engineering smarter AI. So why not turn the tables and study cognition using AI?
Deep learning networks have vastly different architectures. Here, the team went for a hierarchical convolutional neural network (HCNN), which has deep biological roots. These models, popular and successful in computer vision applications, were originally inspired by the discovery of cells in the early visual cortex back in the 1950s.
Similar to how we process vision, HCNN has multiple layers that extract increasingly abstract information from an image. The data is then pooled and fed to a classification layer, which spits out the final conclusion: that’s a miniature schnauzer—as opposed to the standard or giant schnauzer breeds.
Further resembling its human visual counterpart, the HCNN naturally “evolves” neurons tailored to particular aspects of an image after training to maximize the network’s performance for a given task. That is, some artificial neurons will fire when it “sees” a line, a face, or some weirdly dream-like shapes that can’t be easily classified.
The team first trained their HCNN using ImageNet, a dataset of around 1.2 million natural photos classified into 1,000 categories.
