Read-write intelligence.
Push them on any given topic or stray from the most obvious fields, and they will lie thoughtlessly
To get a better understanding of these AI language models, I’ve been playing with a variety for the past few weeks; from tools sold to copywriters to flexible, multipurpose systems like GPT-3. The experience has been dizzying. Often, I’m amazed by the fluency, insight, and creativity of these systems.
But other times, I’m stunned by how limited these programs are. Something that’s often overlooked is just how much human curation is needed to shape their output. The text above was not the first response I received from GPT-3, and I had to go through several iterations to generate a response that was both cogent and funny. It helped, of course, that the task I’d set GPT-3 was an imaginative and open-ended one: it played into the program’s strengths .
The Verge . I played around with different models, inputting opening paragraphs into these systems and asking for story ideas. Here is some more from GPT-3 on large language models: As neuroscientist Mark Changizi has shown, the process of learning a language is a massive project. “Language is one of the most complex things that brains can do,” he writes, “and it emerges from the brain’s more basic skills, like vision, hearing, and motor control.”All these points make sense if you’re not concentrating too hard, but they don’t flow from sentence to sentence. They never follow an argument or build to a conclusion. And again, fabrication is a problem.
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