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Understanding is a loaded term. But large transformers seem pretty good at learning from datasets (of more or less any modality) to they extent that they can cr
by solidasparagus 2y ago
Understanding is a loaded term. But large transformers seem pretty good at learning from datasets (of more or less any modality) to they extent that they can create useful new datapoints and allow you to work with existing datapoints in useful, structured ways.
- Jensson 2y agoTransformers main usefulness is translation, including translations from technical lingo to natural languages and vice versa, or code to natural language etc. That was their targeted usecase when originally made and that is what most people use them for. This is what you use them for that doesn't require hallucinating. They don't understand so they can't replace technical experts, they can just translate a bit back and forth but you still need an expert who understands the domain since translations isn't enough for a layperson to replace an expert. The other use case is to make them hallucinate tropey stories and concepts for brainstorming, this isn't nearly as useful though due to it adding so much low quality stuff here when it hallucinates.