The Accuracy Challenge for ChatGPT

The challenge with ChatGPT is that it has a tendency to pretty confidently produce inaccurate responses. While ChatGPT shows us a potential future where our digital agents become so much smarter, we’re not quite there yet.

I’ve read and listened to two really interesting discussions about this accuracy challenge. The first is an article by Stephen Wolfram titled “Wolfram|Alpha as the Way to Bring Computational Knowledge Superpowers to ChatGPT” where he presents Wolfram|Alpha with its natural language processing as a possible service to enable ChatGPT to deliver more accurate responses by drawing on Wolfram|Alpha’s vast store of knowledge.

Wolfram|Alpha does something very different from ChatGPT, in a very different way. But they have a common interface: natural language. And this means that ChatGPT can “talk to” Wolfram|Alpha just like humans do—with Wolfram|Alpha turning the natural language it gets from ChatGPT into precise, symbolic computational language on which it can apply its computational knowledge power.

On the other side is a discussion in a recent podcast episode of Ezra Klein’s podcast titled “A Skeptical Take on the AI Revolution” where he and “AI expert” Gary Marcus discuss these accuracy and reliability challenges in more detail. I’m still listening to the podcast episode so I haven’t heard the whole discussion just yet.

You can find the episode on Pocket Casts here:

You can also find the podcast behind the New York Times paywall here:


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