What it produces is always “statistically plausible”, at least at a linguistic level. It has coherent things to say, that pull in concepts it’s learned, quite often in interesting and unexpected ways. And what’s remarkable is how human-like the output is, not just at a small scale, but across whole essays. A Basic ExampleĪt its core, ChatGPT is a system for generating linguistic output that “follows the pattern” of what’s out there on the web and in books and other materials that have been used in its training. But now-thanks to the success of ChatGPT-as well as all the work we’ve done in making Wolfram|Alpha understand natural language-there’s finally the opportunity to combine these to make something much stronger than either could ever achieve on their own. 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.įor decades there’s been a dichotomy in thinking about AI between “statistical approaches” of the kind ChatGPT uses, and “symbolic approaches” that are in effect the starting point for Wolfram|Alpha. But they have a common interface: natural language. Wolfram|Alpha does something very different from ChatGPT, in a very different way. But it’s pretty clear that wonderful things are possible. We’ve just started exploring what this means for ChatGPT. It’s equally, if not more, important for human-like AIs as well-immediately giving them what we can think of as computational knowledge superpowers, that leverage the non-human-like power of structured computation and structured knowledge. And the point is that it’s not just important for us humans. It’s a tremendously powerful way of working.

But I can also immediately call on Wolfram Language and Wolfram|Alpha to tap into a kind of unique “computational superpower” that lets me do all sorts of beyond-human things. And in doing this my goal has been to build a system that can “computationally assist”-and augment-what I and others want to do. I myself have been deeply involved with the computational paradigm for many decades, in the singular pursuit of building a computational language to represent as many things in the world as possible in formal symbolic ways. And indeed one of the great achievements of our civilization over the past several centuries has been to build up the paradigms of mathematics, the exact sciences-and, most importantly, now computation-and to create a tower of capabilities quite different from what pure human-like thinking can achieve. Some of it is instead more formal and structured. And, as I discuss elsewhere, I think its success is probably telling us some very fundamental things about the nature of human thinking.īut while ChatGPT is a remarkable achievement in automating the doing of major human-like things, not everything that’s useful to do is quite so “human like”. Finally, and suddenly, here’s a system that can successfully generate text about almost anything-that’s very comparable to what humans might write. And even having watched developments in the past few years I find the performance of ChatGPT thoroughly remarkable.

I’ve been tracking neural net technology for a long time (about 43 years, actually).

And it’s happening now with OpenAI’s ChatGPT. It happened with our Physics Project in 2020. It happened to us with Wolfram|Alpha back in 2009. It’s always amazing when things suddenly “just work”. “What Is ChatGPT Doing … and Why Does It Work?” »

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