Artificial intelligence has made great strides recently on the problem of visual object recognition. The latest generation of algorithms reaches performance levels similar to humans on some recognition tasks. This raises the question whether artificial intelligence can help us understand how biological brains perform visual recognition. In a new paper in PLoS Computational Biology, Seyed Khaligh-Razavi and Niko Kriegeskorte compared the internal representations of a state-of-the-art deep-learning computer-vision system to the representations in biological brains. They found that the deep computer-vision model’s representations are very similar to biological brain representations, more so than those of a wide range of earlier computer-vision systems.
These findings suggest that biological visual object recognition, which used to be impossible to mimic in artificial systems, is moving into the realm of processes that can be approximately replicated with computers. Deep learning models are likely to generate a new generation of specific computational simulations of the human brain. Our findings further suggest the intriguing possibility that modelling biological brains more closely than current engineering approaches do promises further advances in computer vision and artificial intelligence.
The paper can be found here.