Programmer of the Universe

“OK, so let me tell you…” And so it would begin. A long and colorful story. An elaborate description of a wild idea. In the forty years I knew Ed Fredkin I heard countless wild ideas and colorful stories from him. He always radiated a certain adventurous joy—together with supreme, almost-childlike confidence. Ed was someone who wanted to independently figure things out for himself, and delighted in presenting his often somewhat-outlandish conclusions—whether about technology, science, business or the world—with dramatic showman-like panache.

In all the years I knew Ed, I’m not sure he ever really listened to anything I said (though he did use tools I built). He used to like to tell people I’d learned a lot from him. And indeed we had intellectual interests that should have overlapped. But in actuality our ways of thinking about them mostly didn’t connect much at all. But at a personal and social level it was still always a lot of fun being around Ed and being exposed to his unique intense opportunistic energy—with its repeating themes but ever-changing directions. Continue reading

AIs and Alien Minds

How do alien minds perceive the world? It’s an old and oft-debated question in philosophy. And it now turns out to also be a question that rises to prominence in connection with the concept of the ruliad that’s emerged from our Wolfram Physics Project.

I’ve wondered about alien minds for a long time—and tried all sorts of ways to imagine what it might be like to see things from their point of view. But in the past I’ve never really had a way to build my intuition about it. That is, until now. So, what’s changed? It’s AI. Because in AI we finally have an accessible form of alien mind. Continue reading

The Leading Edge of 2023 Technology … and Beyond

Today we’re launching Version 13.3 of Wolfram Language and Mathematica—both available immediately on desktop and cloud. It’s only been 196 days since we released Version 13.2, but there’s a lot that’s new, not least a whole subsystem around LLMs.

Last Friday (June 23) we celebrated 35 years since Version 1.0 of Mathematica (and what’s now Wolfram Language). And to me it’s incredible how far we’ve come in these 35 years—yet how consistent we’ve been in our mission and goals, and how well we’ve been able to just keep building on the foundations we created all those years ago. Continue reading

This is part of an ongoing series about our LLM-related technology:ChatGPT Gets Its “Wolfram Superpowers”!Instant Plugins for ChatGPT: Introducing the Wolfram ChatGPT Plugin KitThe New World of LLM Functions: Integrating LLM Technology into the Wolfram LanguagePrompts for Work & Play: Launching the Wolfram Prompt RepositoryIntroducing Chat Notebooks: Integrating LLMs into the Notebook Paradigm

A New Kind of Notebook

We originally invented the concept of “Notebooks” back in 1987, for Version 1.0 of Mathematica. And over the past 36 years, Notebooks have proved to be an incredibly convenient medium in which to do—and publish—work (and indeed, I, for example, have created hundreds of thousands of them). And, yes, eventually the basic concepts of Notebooks were widely copied—though still not even with everything we had back in 1987!

Well, now there’s a new challenge and opportunity for Notebooks: integrating LLM functionality into them. It’s an interesting design problem, and I’m pretty pleased with what we’ve come up with. And today we’re introducing Chat Notebooks as a new kind of Notebook that supports LLM-based chat functionality. Continue reading

This is part of an ongoing series about our LLM-related technology:ChatGPT Gets Its “Wolfram Superpowers”!Instant Plugins for ChatGPT: Introducing the Wolfram ChatGPT Plugin KitThe New World of LLM Functions: Integrating LLM Technology into the Wolfram LanguagePrompts for Work & Play: Launching the Wolfram Prompt RepositoryIntroducing Chat Notebooks: Integrating LLMs into the Notebook Paradigm

Building Blocks of “LLM Programming”

Prompts are how one channels an LLM to do something. LLMs in a sense always have lots of “latent capability” (e.g. from their training on billions of webpages). But prompts—in a way that’s still scientifically mysterious—are what let one “engineer” what part of that capability to bring out. Continue reading

This is part of an ongoing series about our LLM-related technology:ChatGPT Gets Its “Wolfram Superpowers”!Instant Plugins for ChatGPT: Introducing the Wolfram ChatGPT Plugin KitThe New World of LLM Functions: Integrating LLM Technology into the Wolfram LanguagePrompts for Work & Play: Launching the Wolfram Prompt RepositoryIntroducing Chat Notebooks: Integrating LLMs into the Notebook Paradigm

Turning LLM Capabilities into Functions

So far, we mostly think of LLMs as things we interact directly with, say through chat interfaces. But what if we could take LLM functionality and “package it up” so that we can routinely use it as a component inside anything we’re doing? Well, that’s what our new LLMFunction is about. Continue reading

This is part of an ongoing series about our LLM-related technology:ChatGPT Gets Its “Wolfram Superpowers”!Instant Plugins for ChatGPT: Introducing the Wolfram ChatGPT Plugin KitThe New World of LLM Functions: Integrating LLM Technology into the Wolfram LanguagePrompts for Work & Play: Launching the Wolfram Prompt RepositoryIntroducing Chat Notebooks: Integrating LLMs into the Notebook Paradigm

Build a New Plugin in under a Minute…

A few weeks ago, in collaboration with OpenAI, we released the Wolfram plugin for ChatGPT, which lets ChatGPT use Wolfram Language and Wolfram|Alpha as tools, automatically called from within ChatGPT. One can think of this as adding broad “computational superpowers” to ChatGPT, giving access to all the general computational capabilities and computational knowledge in Wolfram Language and Wolfram|Alpha.

But what if you want to make your own special plugin, that does specific computations, or has access to data or services that are for example available only on your own computer or computer system? Well, today we’re releasing a first version of a kit for doing that. And building on our whole Wolfram Language tech stack, we’ve managed to make the whole process extremely easy—to the point where it’s now realistic to deploy at least a basic custom ChatGPT plugin in under a minute. Continue reading

This is part of an ongoing series about our LLM-related technology:ChatGPT Gets Its “Wolfram Superpowers”!Instant Plugins for ChatGPT: Introducing the Wolfram ChatGPT Plugin KitThe New World of LLM Functions: Integrating LLM Technology into the Wolfram LanguagePrompts for Work & Play: Launching the Wolfram Prompt RepositoryIntroducing Chat Notebooks: Integrating LLMs into the Notebook Paradigm

In Just Two and a Half Months…

Early in January I wrote about the possibility of connecting ChatGPT to Wolfram|Alpha. And today—just two and a half months later—I’m excited to announce that it’s happened! Thanks to some heroic software engineering by our team and by OpenAI, ChatGPT can now call on Wolfram|Alpha—and Wolfram Language as well—to give it what we might think of as “computational superpowers”. It’s still very early days for all of this, but it’s already very impressive—and one can begin to see how amazingly powerful (and perhaps even revolutionary) what we can call “ChatGPT + Wolfram” can be.

Back in January, I made the point that, as an LLM neural net, ChatGPT—for all its remarkable prowess in textually generating material “like” what it’s read from the web, etc.—can’t itself be expected to do actual nontrivial computations, or to systematically produce correct (rather than just “looks roughly right”) data, etc. But when it’s connected to the Wolfram plugin it can do these things. So here’s my (very simple) first example from January, but now done by ChatGPT with “Wolfram superpowers” installed: Continue reading

The Shock of ChatGPT

Just a few months ago writing an original essay seemed like something only a human could do. But then ChatGPT burst onto the scene. And suddenly we realized that an AI could write a passable human-like essay. So now it’s natural to wonder: How far will this go? What will AIs be able to do? And how will we humans fit in?

My goal here is to explore some of the science, technology—and philosophy—of what we can expect from AIs. I should say at the outset that this is a subject fraught with both intellectual and practical difficulty. And all I’ll be able to do here is give a snapshot of my current thinking—which will inevitably be incomplete—not least because, as I’ll discuss, trying to predict how history in an area like this will unfold is something that runs straight into an issue of basic science: the phenomenon of computational irreducibility. Continue reading

It’s Just Adding One Word at a Time

That ChatGPT can automatically generate something that reads even superficially like human-written text is remarkable, and unexpected. But how does it do it? And why does it work? My purpose here is to give a rough outline of what’s going on inside ChatGPT—and then to explore why it is that it can do so well in producing what we might consider to be meaningful text. I should say at the outset that I’m going to focus on the big picture of what’s going on—and while I’ll mention some engineering details, I won’t get deeply into them. (And the essence of what I’ll say applies just as well to other current “large language models” [LLMs] as to ChatGPT.)

The first thing to explain is that what ChatGPT is always fundamentally trying to do is to produce a “reasonable continuation” of whatever text it’s got so far, where by “reasonable” we mean “what one might expect someone to write after seeing what people have written on billions of webpages, etc.” Continue reading