DeepMind boffins brain-damage AI to find out what makes it tick

Researchers trying to understand how neural networks work shouldn't just focus on interpretable neurons, according to new research from DeepMind researchers. AI systems are often described as black boxes. It's difficult to understand how they work and reach particular outcomes, making people nervous about using them to make important decisions in areas such as healthcare or recruitment. Making neural networks more interpretable is hot topic in research. It's possible to look at the connections between different groups of neurons and visualise which ones correspond to a specific class. If an image classification model is fed different types of pictures, say an image of a cat or dog, researchers can find the'cat neurons' or a'dog neurons'.