But they are also concerned that researchers are unable to fully understand how these unsupervised algorithms perform tasks like creating new content, be it audio, code, images, text, simulations or videos. Further, these models can plagiarize, be biased, potentially replace thousands of routine jobs, and also pose security and privacy risks. Self-supervised at most, deep neural networks train on humongous amounts of data, but they learn on their own with no human programming or oversight. Speaking at the recent SoftBank World corporate conference, Son underscored that AI is now “self-learning, self-training, and self-inferencing, just like human beings." However, while researchers understand the mathematical operations of each neuron, it is a challenge to comprehend the connection between the maths and the wide range of observed behaviours. This makes it difficult for them to diagnose failure patterns, provide remedies, or even certify a model’s safety.
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