916 items tagged “ai”
2023
GPT and other large language models are aesthetic instruments rather than epistemological ones. Imagine a weird, unholy synthesizer whose buttons sample textual information, style, and semantics. Such a thing is compelling not because it offers answers in the form of text, but because it makes it possible to play text—all the text, almost—like an instrument.
A calculator has a well-defined, well-scoped set of use cases, a well-defined, well-scoped user interface, and a set of well-understood and expected behaviors that occur in response to manipulations of that interface.
Large language models, when used to drive chatbots or similar interactive text-generation systems, have none of those qualities. They have an open-ended set of unspecified use cases.
LLM Visualization. Brendan Bycroft’s beautifully crafted interactive explanation of the transformers architecture—that universal but confusing model diagram, only here you can step through and see a representation of the flurry of matrix algebra that occurs every time you get a Large Language Model to generate the next token.
Seamless Communication (via) A new “family of AI research models” from Meta AI for speech and text translation. The live demo is particularly worth trying—you can record a short webcam video of yourself speaking and get back the same video with your speech translated into another language.
The key to it is the new SeamlessM4T v2 model, which supports 101 languages for speech input, 96 Languages for text input/output and 35 languages for speech output. SeamlessM4T-Large v2 is a 9GB file, available on Hugging Face.
Also in this release: SeamlessExpressive, which “captures certain underexplored aspects of prosody such as speech rate and pauses”—effectively maintaining things like expressed enthusiasm across languages.
Plus SeamlessStreaming, “a model that can deliver speech and text translations with around two seconds of latency”.
So something everybody I think pretty much agrees on, including Sam Altman, including Yann LeCun, is LLMs aren't going to make it. The current LLMs are not a path to ASI. They're getting more and more expensive, they're getting more and more slow, and the more we use them, the more we realize their limitations.
We're also getting better at taking advantage of them, and they're super cool and helpful, but they appear to be behaving as extremely flexible, fuzzy, compressed search engines, which when you have enough data that's kind of compressed into the weights, turns out to be an amazingly powerful operation to have at your disposal.
[...] And the thing you can really see missing here is this planning piece, right? So if you try to get an LLM to solve fairly simple graph coloring problems or fairly simple stacking problems, things that require backtracking and trying things and stuff, unless it's something pretty similar in its training, they just fail terribly.
[...] So that's the theory about what something like Q* might be, or just in general, how do we get past this current constraint that we have?
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.”
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]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.”
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]This is nonsensical. There is no way to understand the LLaMA models themselves as a recasting or adaptation of any of the plaintiffs’ books.
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]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?
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.
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.
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
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.
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.
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
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]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.”
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.
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
The company pressed forward and launched ChatGPT on November 30. It was such a low-key event that many employees who weren’t directly involved, including those in safety functions, didn’t even realize it had happened. Some of those who were aware, according to one employee, had started a betting pool, wagering how many people might use the tool during its first week. The highest guess was 100,000 users. OpenAI’s president tweeted that the tool hit 1 million within the first five days. The phrase low-key research preview became an instant meme within OpenAI; employees turned it into laptop stickers.
Inside the Chaos at OpenAI (via) Outstanding reporting on the current situation at OpenAI from Karen Hao and Charlie Warzel, informed by Karen’s research for a book she is currently writing. There are all sorts of fascinating details in here that I haven’t seen reported anywhere, and it strongly supports the theory that this entire situation (Sam Altman being fired by the board of the OpenAI non-profit) resulted from deep disagreements within OpenAI concerning speed to market and commercialization of their technology v.s. safety research and cautious progress towards AGI.
Details emerge of surprise board coup that ousted CEO Sam Altman at OpenAI. The board of the non-profit in control of OpenAI fired CEO Sam Altman yesterday, which is sending seismic waves around the AI technology industry. This overview by Benj Edwards is the best condensed summary I’ve seen yet of everything that’s known so far.
The EU AI Act now proposes to regulate “foundational models”, i.e. the engine behind some AI applications. We cannot regulate an engine devoid of usage. We don’t regulate the C language because one can use it to develop malware. Instead, we ban malware and strengthen network systems (we regulate usage). Foundational language models provide a higher level of abstraction than the C language for programming computer systems; nothing in their behaviour justifies a change in the regulatory framework.
— Arthur Mensch, Mistral AI
“Learn from your chats” ChatGPT feature preview (via) 7 days ago a Reddit user posted a screenshot of what’s presumably a trial feature of ChatGPT: a “Learn from your chats” toggle in the settings.
The UI says: “Your primary GPT will continually improve as you chat, picking up on details and preferences to tailor its responses to you.”
It provides the following examples: “I move to SF in two weeks”, “Always code in Python”, “Forget everything about my last project”—plus an option to reset it.
No official announcement yet.
Fleet Context. This project took the source code and documentation for 1221 popular Python libraries and ran them through the OpenAI text-embedding-ada-002 embedding model, then made those pre-calculated embedding vectors available as Parquet files for download from S3 or via a custom Python CLI tool.
I haven’t seen many projects release pre-calculated embeddings like this, it’s an interesting initiative.
I’ve resigned from my role leading the Audio team at Stability AI, because I don’t agree with the company’s opinion that training generative AI models on copyrighted works is ‘fair use’.
[...] I disagree because one of the factors affecting whether the act of copying is fair use, according to Congress, is “the effect of the use upon the potential market for or value of the copyrighted work”. Today’s generative AI models can clearly be used to create works that compete with the copyrighted works they are trained on. So I don’t see how using copyrighted works to train generative AI models of this nature can be considered fair use.
But setting aside the fair use argument for a moment — since ‘fair use’ wasn’t designed with generative AI in mind — training generative AI models in this way is, to me, wrong. Companies worth billions of dollars are, without permission, training generative AI models on creators’ works, which are then being used to create new content that in many cases can compete with the original works.
Exploring GPTs: ChatGPT in a trench coat?
The biggest announcement from last week’s OpenAI DevDay (and there were a LOT of announcements) was GPTs. Users of ChatGPT Plus can now create their own, custom GPT chat bots that other Plus subscribers can then talk to.
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