'Try thinking of chatbots as 'improv machines.'' 📝 Helen Toner (hlntnr)
Language models have revealed a striking fact: for some tasks, simply predicting the next word accurately enough—doing improv well enough—can be remarkably valuable. The improv machine metaphor helps us think through how we can use these systems in practice. Sometimes, there’s nothing wrong with getting your information from an improv scene. Poems, jokes,scripts: this kind of output stands on its own, regardless of how it was created.
By contrast, using an improv machine when you need correct answers but can’t verify them yourself is more perilous. People using ChatGPT and similar tools to do open-ended research are starting to discover this. In one case, a law professor was made aware of a sexual assault accusation against him that.
For all the enthusiasm these new improv machines have sparked, there’s still a lot we don’t know about them. We understand very little about the inscrutable processes under the hood by which they determine what text to output. And there is even more uncertainty ahead—researchers haveby the capabilities that emerge when language models are trained using more data and more computing resources, and it’s not clear where exactly the limits of their abilities will fall.
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