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What does the neural network see?

05.03.2019

Open AI and Google have created a new tool called Activation Atlases to visualize interactions between neurons in AI systems. It allows you to penetrate into the "consciousness" of the machine in order to better understand the internal decision-making processes of artificial intelligence, as well as to identify their errors and shortcomings. Activation Atlases is built around the capability visualization method. It provides an insight into what the hidden layers of neural networks are, which in turn make machine learning more accessible and interpretable. In other words, it allows researchers to analyze how individual algorithms work, understanding how they combine various abstract elements to identify specific objects.

Activation Atlases will finally help answer the question of what the neural network actually sees. Similar studies have been done before, but then it was like identifying individual letters in the visual alphabet of algorithms. The new study is closer to the whole dictionary, it explains how these letters are combined into words. One of the authors of the work compares Activation Atlases with a microscope for neural networks.

Open AI believes that this technology can detect unforeseen problems in neural networks, for example, in places where the network relies on spurious correlations in image classification (where algorithms could confuse dark-skinned people with gorillas), or when reusing a function leads to strange mistakes.

Activation Atlases uses a well-known neural network called GoogLeNet or InceptionV1, which is trained on images from the ImageNet database. It processes data through about ten layers of hundreds of neurons. In turn, each neuron fires to a different extent on different types of images. The researchers traced this process and found out exactly which elements of the images evoke a response from the system.

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