November 2023
Nov. 20, 2023
And the investors wailed and gnashed their teeth but it’s true, that is what they agreed to, and they had no legal recourse. And OpenAI’s new CEO, and its nonprofit board, cut them a check for their capped return and said “bye” and went back to running OpenAI for the benefit of humanity. It turned out that a benign, carefully governed artificial superintelligence is really good for humanity, and OpenAI quickly solved all of humanity’s problems and ushered in an age of peace and abundance in which nobody wanted for anything or needed any Microsoft products. And capitalism came to an end.
— Matt Levine, in a hypothetical
Nov. 21, 2023
An Interactive Guide to CSS Grid (via) Josh Comeau’s extremely clear guide to CSS grid, with interactive examples for all of the core properties.
The way I think about the AI of the future is not as someone as smart as you or as smart as me, but as an automated organization that does science and engineering and development and manufacturing.
Nov. 22, 2023
Before Altman’s Ouster, OpenAI’s Board Was Divided and Feuding. This is the first piece of reporting I’ve seen on the OpenAI situation which has offered a glimmer of an explanation as to what happened.
It sounds like the board had been fighting about things for over a year—notably including who should replace departed members, which is how they’d shrunk down to just six people.
There’s also an interesting detail in here about the formation of Anthropic:
“Mr. Sutskever’s frustration with Mr. Altman echoed what had happened in 2021 when another senior A.I. scientist left OpenAI to form the company Anthropic. That scientist and other researchers went to the board to try to push Mr. Altman out. After they failed, they gave up and departed, according to three people familiar with the attempt to push Mr. Altman out.”
Deciphering clues in a news article to understand how it was reported
Written journalism is full of conventions that hint at the underlying reporting process, many of which are not entirely obvious. Learning how to read and interpret these can help you get a lot more out of the news.
[... 1,456 words]Sam Altman expelling Toner with the pretext of an inoffensive page in a paper no one read would have given him a temporary majority with which to appoint a replacement director, and then further replacement directors. These directors would, naturally, agree with Sam Altman, and he would have a full, perpetual board majority - the board, which is the only oversight on the OA CEO. Obviously, as an extremely experienced VC and CEO, he knew all this and how many votes he (thought he) had on the board, and the board members knew this as well - which is why they had been unable to agree on replacement board members all this time.
— Gwern
Weeknotes: DevDay, GitHub Universe, OpenAI chaos
Three weeks of conferences and Datasette Cloud work, four days of chaos for OpenAI.
[... 766 words]Introducing Claude 2.1. Anthropic’s Claude used to have the longest token context of any of the major models: 100,000 tokens, which is about 300 pages. Then GPT-4 Turbo came out with 128,000 tokens and Claude lost one of its key differentiators.
Claude is back! Version 2.1, announced today, bumps the token limit up to 200,000—and also adds support for OpenAI-style system prompts, a feature I’ve been really missing.
They also announced tool use, but that’s only available for a very limited set of partners to preview at the moment.
Claude: How to use system prompts. Documentation for the new system prompt support added in Claude 2.1. The design surprises me a little: the system prompt is just the text that comes before the first instance of the text “Human: ...”—but Anthropic promise that instructions in that section of the prompt will be treated differently and followed more closely than any instructions that follow.
This whole page of documentation is giving me some pretty serious prompt injection red flags to be honest. Anthropic’s recommended way of using their models is entirely based around concatenating together strings of text using special delimiter phrases.
I’ll give it points for honesty though. OpenAI use JSON to field different parts of the prompt, but under the hood they’re all concatenated together with special tokens into a single token stream.
I remember that they [Ev and Biz at Twitter in 2008] very firmly believed spam was a concern, but, “we don’t think it's ever going to be a real problem because you can choose who you follow.” And this was one of my first moments thinking, “Oh, you sweet summer child.” Because once you have a big enough user base, once you have enough people on a platform, once the likelihood of profit becomes high enough, you’re going to have spammers.
We have reached an agreement in principle for Sam Altman to return to OpenAI as CEO with a new initial board of Bret Taylor (Chair), Larry Summers, and Adam D'Angelo.
— @OpenAI
Nov. 23, 2023
YouTube: Intro to Large Language Models. Andrej Karpathy is an outstanding educator, and this one hour video offers an excellent technical introduction to LLMs.
At 42m Andrej expands on his idea of LLMs as the center of a new style of operating system, tying together tools and and a filesystem and multimodal I/O.
There’s a comprehensive section on LLM security—jailbreaking, prompt injection, data poisoning—at the 45m mark.
I also appreciated his note on how parameter size maps to file size: Llama 70B is 140GB, because each of those 70 billion parameters is a 2 byte 16bit floating point number on disk.
The 6 Types of Conversations with Generative AI. I’ve hoping to see more user research on how users interact with LLMs for a while. Here’s a study from Nielsen Norman Group, who conducted a 2-week diary study involving 18 participants, then interviewed 14 of them.
They identified six categories of conversation, and made some resulting design recommendations.
A key observation is that “search style” queries (just a few keywords) often indicate users who are new to LLMs, and should be identified as a sign that the user needs more inline education on how to best harness the tool.
Suggested follow-up prompts are valuable for most of the types of conversation identified.
To some degree, the whole point of the tech industry’s embrace of “ethics” and “safety” is about reassurance. Companies realize that the technologies they are selling can be disconcerting and disruptive; they want to reassure the public that they’re doing their best to protect consumers and society. At the end of the day, though, we now know there’s no reason to believe that those efforts will ever make a difference if the company’s “ethics” end up conflicting with its money. And when have those two things ever not conflicted?
Nov. 25, 2023
I’m on the Newsroom Robots podcast, with thoughts on the OpenAI board
Newsroom Robots is a weekly podcast exploring the intersection of AI and journalism, hosted by Nikita Roy.
[... 1,032 words]Nov. 26, 2023
This is nonsensical. There is no way to understand the LLaMA models themselves as a recasting or adaptation of any of the plaintiffs’ books.
Nov. 27, 2023
Prompt injection explained, November 2023 edition
A neat thing about podcast appearances is that, thanks to Whisper transcriptions, I can often repurpose parts of them as written content for my blog.
[... 1,357 words]MonadGPT (via) “What would have happened if ChatGPT was invented in the 17th century? MonadGPT is a possible answer.
MonadGPT is a finetune of Mistral-Hermes 2 on 11,000 early modern texts in English, French and Latin, mostly coming from EEBO and Gallica.
Like the original Mistral-Hermes, MonadGPT can be used in conversation mode. It will not only answer in an historical language and style but will use historical and dated references.”
Nov. 29, 2023
Announcing Deno Cron. Scheduling tasks in deployed applications is surprisingly difficult. Deno clearly understand this, and they’ve added a new Deno.cron(name, cron_definition, callback) mechanism for running a JavaScript function every X minutes/hours/etc.
As with several other recent Deno features, there are two versions of the implementation. The first is an in-memory implementation in the Deno open source binary, while the second is a much more robust closed-source implementation that runs in Deno Deploy:
“When a new production deployment of your project is created, an ephemeral V8 isolate is used to evaluate your project’s top-level scope and to discover any Deno.cron definitions. A global cron scheduler is then updated with your project’s latest cron definitions, which includes updates to your existing crons, new crons, and deleted crons.”
Two interesting features: unlike regular cron the Deno version prevents cron tasks that take too long from ever overlapping each other, and a backoffSchedule: [1000, 5000, 10000] option can be used to schedule attempts to re-run functions if they raise an exception.
llamafile is the new best way to run a LLM on your own computer
Mozilla’s innovation group and Justine Tunney just released llamafile, and I think it’s now the single best way to get started running Large Language Models (think your own local copy of ChatGPT) on your own computer.
[... 650 words]Nov. 30, 2023
ChatGPT is one year old. Here’s how it changed the world. I’m quoted in this piece by Benj Edwards about ChatGPT’s one year birthday:
“Imagine if every human being could automate the tedious, repetitive information tasks in their lives, without needing to first get a computer science degree,” AI researcher Simon Willison told Ars in an interview about ChatGPT’s impact. “I’m seeing glimpses that LLMs might help make a huge step in that direction.”
This is what I constantly tell my students: The hard part about doing a tech product for the most part isn't the what beginners think makes tech hard — the hard part is wrangling systemic complexity in a good, sustainable and reliable way.
Many non-tech people e.g. look at programmers and think the hard part is knowing what this garble of weird text means. But this is the easy part. And if you are a person who would think it is hard, you probably don't know about all the demons out there that will come to haunt you if you don't build a foundation that helps you actively keeping them away.
— atoav
Annotate and explore your data with datasette-comments. New plugin for Datasette and Datasette Cloud: datasette-comments, providing tools for collaborating on data exploration with a team through posting comments on individual rows of data.
Alex Garcia built this for Datasette Cloud but as with almost all of our work there it’s also available as an open source Python package.