Artificial Neural Networks using Multimodal Neurons


C Language Integrated Production System (CLIP) neurons respond to the same concept whether it is presented literally, symbolically, or conceptually, according to our findings. This could explain CLIP's accuracy in recognizing startling visual renderings of concepts, and it's also a big step toward understanding how CLIP and other models learn associations and biases.

The discovered multimodal neurons exist in the human brain. Rather than any specific visual aspect, these neurons respond to clusters of abstract thoughts based around a similar high-level topic. The "Halle Berry" neuron, which has been published in both Scientific American and The New York Times, responds to images, doodles, and the text "Halle Berry" (but not other names).

CLIP is a general-purpose vision system developed by OpenAI that matches the performance of a ResNet-502 but exceeds existing vision systems on some of the most difficult datasets. Each of these challenge datasets, ObjectNet, ImageNet Rendition, and ImageNet Sketch, puts the model through its paces by requiring it to recognize not only simple distortions or changes in lighting or pose, but also complete abstraction and reconstruction—sketches, cartoons, and even statues of the objects.

CLIP Multimodal Neurons

The study is the result of nearly a decade of research on reading convolutional networks, which began with the discovery that many of these traditional techniques are directly relevant to CLIP. To further comprehend the model's activations, we use two tools: feature visualization, which uses gradient-based optimization to maximize the neuron's firing, and dataset examples, which examines the distribution of maximal activating images for a neuron in a dataset.

Bias and Overgeneralization

Despite being trained on a restricted fraction of the internet, the model nevertheless inherits many biases and associations that are unchecked. Although many of the associations we found appear to be innocuous, we found numerous instances where CLIP holds links that potentially lead to representational injury, such as denigration of specific individuals or groups.

These connections pose clear obstacles to the use of such sophisticated visual systems. These biases and associations are likely to remain in the system, with their consequences emerging in both apparent and practically undetectable ways during deployment, whether fine-tuned or employed zero-shot. Because many biased actions are difficult to predict a priori, measuring and correcting them can be difficult. We believe that by identifying some of these linkages and ambiguities ahead of time, our interpretability tools can help practitioners anticipate future problems.

The understanding of CLIP is constantly developing, and we're still deciding if and how large versions of CLIP should be released. We believe that continued community study of the available versions, as well as the tools we're presenting today, will contribute to a better understanding of multimodal systems and help us make better decisions.

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