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This feature hierarchy and the filters that model significance in the data, make it possible for the layers to learn from experience. Thus, deep nets can crunch unstructureddata that was previously not available for unsupervised analysis. But hyperautomation is an innovation in its infancy, and it’s expected to explode in 2020.
A general LLM won’t be calibrated for that, but you can recalibrate it—a process known as fine-tuning—to your own data. Fine-tuning applies to both hosted cloud LLMs and open source LLM models you run yourself, so this level of ‘shaping’ doesn’t commit you to one approach. You really have to take what’s already there.
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