I'm happy to answer questions or accept feedback about the new functionality.

We're very excited about the potential of Wolfram technology + LLMs, and we've got a number of interesting projects underway in this area. Stephen's other recent blog posts linked at the top of the Chat Notebooks post provide a nice tour.

The Wolfram/Chatbook[1] package mentioned in the post is freely available for any Wolfram 13.2 users. It's also open source and available on GitHub[2].

[1]: https://paclets.com/Wolfram/Chatbook

[2]: https://github.com/WolframResearch/Chatbook

1. How long can a single chat input be? 2. If I am using a chat notebook with several previous inputs, when entering some new input below all of those, is it just appending all previous inputs together and sending the entire thing to the LLM? If so, how long can this be? Will it summarize if it exceeds that length? 3. What model is this using? GPT-3.5 or GPT-4 or something else? 4. What plans do you have next to integrate AI into Wolfram products?

When you have a sequence of alternating 'ChatInput' and 'ChatOutput' cells, those are sent in-order as individual, respectively, "role" -> "user" and "role" -> "assistant" messages to the chat API. It doesn't, for example, concatenate them into a single message.

The one exception is Chatbook's implementation of Personas, which are defined (roughly speaking) as a "system" message that gets prepended as the first message in the chat conversation on each ChatInput evaluation, and which in some cases is concatenated with other ChatInput cells that have been set to "role" -> "system".

Chatbook's don't currently do automatic behind-the-scenes summarization, but we've experimented with some possible approaches. There are UI and and UX considerations around how we present that possibility of automatic summarization that I think require greater consideration.

3. Currently we support the OpenAI Chat API, and all models supported by that API. However, Chatbook currently only implements quick UI access to the GPT-3.5 and GPT-4 models. If you want to use a different OpenAI model in a particular notebook, you can set the relevant setting by evaluating:

    CurrentValue[
        EvaluationNotebook[],
        {TaggingRules, "ChatNotebookSettings", "Model"}
    ] = "gpt-3.5-turbo-0301"

Our focus has been on flexible and easily user-extensible support for Personas (for example, developing the Prompt Repository[1]), but we're also planning to add built-in support for additional foundation models, and greater customizability so that new models are easy to add and use from the Chat Notebooks interface.

[1]: https://resources.wolframcloud.com/PromptRepository/

4. We've got a wide range of exciting projects coming down our R&D pipeline, and things are changing rapidly.

In some sense more fundamental that Chat functionality in notebooks is our work integrating LLM-related functionality more broadly into the Wolfram language: e.g. LLMFunction[..], LLMSynthesize[..], LLMPrompt[..], LLMTool[..] (all part of the Wolfram/LLMFunctions paclet[2]), where we're putting the power of LLMs directly in the hands of Wolfram users.

Making LLMs "just" a feature area added to the many existing powerful capabilities of Wolfram is making it easy for ourselves and our users to maximally leverage the new capabilities LLMs provide. So stay tuned, more is definitely coming :)

[2]: https://paclets.com/Wolfram/LLMFunctions

And do you think current AI models will be able to solve as yet unsolved problems in math and physics, perhaps with increasingly large datasets or augments like Trees of Thought or process supervision? Or do you think it will require a different fundamental model?

A surprise win was as much luck I had talking through an issue and getting correct, functional one-shot time grid and 3D plots including extra complications for the calculation of the axes.

A recent lose was trying to get it to generate a script that could optimize the gravity coefficient in the Reddit algorithm based on target timeframes and historic data. It offered salvageable output, if you pieced together several attempts and reframes, but never pulled it over the finish line.

Genuinely curious.

So it's possible, in theory, that an AI-in-a-box could do the hardest parts of science aside from experiments. Whether modern LLMs are any good at it is a different question.

I've always found this kinda confusing, and the "evaluate everything above" approach used by the LLM cells much more intuitive, but having the two types of cells behave so differently brings it back to confusing and asking for trouble. Do you think it would make sense to have an option to force all cells to work that way?

Why do I, who would have been the one to implement such a feature, not even remember whether it exists or not? Because if it does, it's horribly useless. It's just rarely what you actually want, and if you do want to do that, you can simply select the cell group, or command-A select the whole notebook, and use shift-return to evaluate everything. That's something I do regularly, but only when I need to, and it would be horrible if it tried to do that every time. I think it was Maple that tried such an interface. (Except their's was 2-dimensional: you could move equations/definitions around on the screen, and anything below or to the right would use definitions from above or to the left of it. This interface was one of the things that convinced me that this was a bad idea.)

The situation with GPTs is different: they do not have the equivalent of kernel state. It took me a while to believe it's actually true, but they have infinite amnesia: every time you send an API call, or submit a new prompt in their web interface, the model is starting completely from scratch with no state or memory whatsoever of anything that's happened before.

That fact that it looks like you're having an interactive conversation in which it knowns what you said before is an illusion created by the fact that the entire conversation all the way back to the start is re-submitted with every new prompt.

Given that reality, and the way people seem to want to interact with these things, the use of a physical context rather than a temporal one seemed most natural. We did talk about implementing our own temporal context mechanism to artificially make the LLM appear to have time-based internal state, like a kernel, but I personally hated the idea, and we never implemented it.

Now, it's possible that at some point these systems will in fact start having internal state (i.e. you have your own personal LLM running on a wad of gum stuck behind your ear, and as you interact with it it constantly fine tunes its weights, or something). At that point we might regret the decision, but I feel like this whole world is so unstable and rapidly evolving that it made sense for us to do what's right now, and worry about the future, if there is one, during that future.

Theodore

One of the nice advantages of a notebook interface to LLMs is that you can easily go back and edit and rearrange the chat history in the normal ways that any cell in a notebook can be edited, moved, deleted, etc.

So e.g. if you want to go back to an earlier point, you can simply move your cursor between cells at an earlier position in the "physical" chat history, insert a new chat cell, and you're done.

[1]: https://writings.stephenwolfram.com/2023/06/introducing-chat...

[2]: https://github.com/WolframResearch/Chatbook#generate-immedia...

The repo README doesn’t make it obvious how it works, but if you go to the Chat Interface portion of the docs it’s illustrated: https://jupyter-ai.readthedocs.io/en/latest/users/index.html...

Is this a record that should be set straight?

I've used Jupyter since it was IPython notebook, but I don't think that community claims to be the first coming of notebooks. The accessibility of python along with the breadth and depth of the scipy community makes it a quite a tour de force. So perhaps these articles are aimed at people who only use open source tools.

It's Wolfram. If they didn't claim to invent something fundamental in the first paragraph, I'd immediately assume the site was hacked or it's a poorly timed April fools joke written by an intern.

My favorite example is when Caltech put out a press release about a young woman got her PhD and nearly beat Wolfram's record for youngest PhD from Caltech. He felt immediately compelled to write a long winded several-thousand word essay about how glad he is to still have the record for being the youngest PhD, by a full two weeks! And also, he wanted to remind everyone that he knew Richard Feynman. Cringe...

The Wikipedia article suggests a contemporary in 1987 (MathCAD) but I'm not seeing a Mathematica/Jupyter-style notebook interface before that year. That would seem to have been the year of the notebook interface, and Mathematica 1.0 did come out that year with one. As far as Wolfram claims go, this one doesn't seem too offensive.

Jupyter notebooks were inspired by Mathematica notebooks: that's widely known and has been explicitly said in blog posts and elsewhere by the creators of Jupyter. It's no secret… And as the designer and developer of Mathematica notebooks I am flattered and honored that my ideas are now so widely used, even if it is in a product other than the one I wrote myself.

Time for a trip down memory lane. There were several notebook-adjacent products around at that time (1987) but none of them implemented notebooks as we know them today.

At the time I was using Xcode, which had a terminal-like interface that was a plain text document in which you could put all the shell commands you needed to build your app. This could include calls to the compiler, linker, or just any generic shell commands. To evaluate a given command (which could be one or more lines long) you had to manually select the exact range of text you wanted to run, which was kind of irritating. Any output would be placed directly below the input line. Because the document was pure plain text, it had no way of knowing what was input and what was output, or distinguish new output from old. So all the outputs accumulated, and you had to manually select and delete them periodically. It was an improvement over a glass teletype terminal interface, but I thought I could do better for Mathematica (code name Omega at the time).

The solution I came up with was to create a text document that was annotated with “zones” delimited by vertical bars along the right side of the screen (where cell brackets are now). Initially I used different black and white texture patterns on 4 or 5 pixel wide vertical bars. One texture for input and another texture for output.

This immediately gave two huge advantages: first, you didn’t have to manually select the range of a multi-line input. Just putting the cursor anywhere within an input zone and evaluating would evaluate the whole input. Second, because the system was keeping track of what was output, old output could be deleted automatically and replaced with new output.

Initially I called these things zones, but Steve Jobs suggested I use large close-square-bracket symbols instead of patterned bars, and think of them more like cells in a spreadsheet than like zones in a text document. Once you have brackets instead of bars, it’s immediately obvious that you could have larger brackets that encompass two or more cells. So I created cell groups, initially just to group input/output pairs, but of course it’s not a big leap to also have sections and subsections, then different cell types, styles, graphics cells, etc.

These key defining features of notebooks did not exist in any other system at the time, and to the best of my knowledge were not independently developed by other people. All the current notebook systems that share these broad features, definitely including Jupyter notebooks, derive from the original Mathematica notebooks I developed in 1987-8. (There are other styles of user interface, including some used by Maple and Matlab around the same time, that are called notebooks, but they did not share the key features I’ve described. They were and are fine products that I’m sure have both advantages and disadvantages compared to our Notebooks, but unlike our Notebooks, they are not the precursors of Jupyter.)

So can Stephen Wolfram personally claim to have invented notebooks? No, and he doesn’t: he claims that Wolfram Research did, which is true, since I was one of the co-founders of that company, and I invented that form of notebook.

Theodore

Item #8 at https://www.wolfram.com/mathematica/scrapbook/

""""

1987: The Mathematica front end begins to take shape…

[screenshots]

(Theo Gray invents cells and groups… and other things still seen today…)

"""

It's unfortunate as clearly he has a huge amount to contribute. Maybe with an LLM someone can build a de-Stepheniser that takes in his pompous, smug text and outputs something more mellow and reasonable whilst keeping all the facts, which is what we are there for after all.

I'm personally a fan of many ideas Wolfram pushes, just not a fan of the style they're pushed. I'd say I'm not in the minority, to the point it's a tired cliche.

¹ https://en.wikipedia.org/wiki/Literate_programming

> I wrote the Notebook user interface for Mathematica and led the user interface group for over 20 years.

https://home.theodoregray.com/

But also, even if it's true, it's kind of an offputting way to start the article. I know that's his thing and I know it's discussed every time one of these is posted here so apologies for the repetitive observation.

> According to Stephen Wolfram: "The idea of a notebook is to have an interactive document that freely mixes code, results, graphics, text and everything else.

By that description Smalltalk with its workspace goes back to the 70s. (I don't know if there's anything closer to the Mathematica notebooks from that era.)

So, yeah, seeing that Jupyter is being used more broadly than Mathematica, Wolfram should feel uncomfortable. (Also note that Jupyter is officially inspired by Mathematica.)

My recollection (which could be wrong, it's been a while) is that SageMath’s notebook preceeded iPython notebooks. IIRC iPython started as a CLI repl, which may have come before the notebook.

(Mathematica came before all of them, and likely inspired SageMath’s interface)

Which, I suppose I can cede that they are among the first "web applications" that do this. But I have a hard time really getting behind a ton of the discussion on it. Even talking about the "breadth and depth" of part of the python community is to ignore how much had been done for years in the mathematica community.

I was soured as early advocates were pitching it as way more impressive than it is. With some being proud of themselves for realizing you can put a "pip install" at the top of the notebook, thinking that somehow made it repeatable. I recall senior engineers wanting notebooks to have prod access so that you could deploy from a notebook. And thinking they were making a good practice statement. Ugh.

Jupyter notebooks are massively used in data science and AI research which eventually led to ChatGPT.

Now ChatGPT is integrated in Mathematica notebooks.

Nice circular situation.

> Thus, for example, if you evaluate x = 5 in an input cell, then subsequently ask for the value of x, the result will be 5 wherever in the notebook you ask—even if it’s above the “x = 5” cell

And

> successive chat cells are “aware” of what’s in cells above them. But even if we add it later, a cell placed at the top won’t “know about” anything in cells below it.

Surely this is because the OpenAI api is chat based and they need to evaluate that chat in-order. it does seem like an inconvenient interface for programmatic LLM generators if you don’t want a chat UX.

But to answer the "why?" - likely because this type of AI is a tool that is going to become universal very rapidly, and the overwhelming majority of the population has little to no understanding of the finer details of the technology. So the default persona has been fine-tuned to include the annoying "as an AI language model [...]" disclaimer very frequently, amongst other disambiguations, couched remarks, and clarifications that a power user would not necessarily require.

https://www.makerlisp.com

Is that even possible?

The audience - people in general - are very resistant to having knowledge/ideas shoved into their brains, and I don't blame them, 90% of it is crap.

Authors have to work very hard to overcome this. "Tell them what you're going to tell them, tell them, tell them what you told them", best advice I got on presentations.

If there was anything better the Powerpoint, short of "killing the unbelievers", it would probably have made a mint by now.

What’s nice with it is both the input and output are modifiable text areas, so you can tune the angent’s example responses to be exactly what you intend. And the on disk format is just the message and parameter JSON, so you can tune your prompt in a live notebook then consume it directly from anything that speaks JSON (most bundlers, for example).

The community will come up with things that will make the content of this blog post look like hello world todo apps.

Can't a wealthy bazillionaire like Elon Musk just drop a few billions and buy the rights to GPT4?