916 items tagged “ai”
2024
Hello GPT-4o. OpenAI announced a new model today: GPT-4o, where the o stands for "omni".
It looks like this is the gpt2-chatbot we've been seeing in the Chat Arena the past few weeks.
GPT-4o doesn't seem to be a huge leap ahead of GPT-4 in terms of "intelligence" - whatever that might mean - but it has a bunch of interesting new characteristics.
First, it's multi-modal across text, images and audio as well. The audio demos from this morning's launch were extremely impressive.
ChatGPT's previous voice mode worked by passing audio through a speech-to-text model, then an LLM, then a text-to-speech for the output. GPT-4o does everything with the one model, reducing latency to the point where it can act as a live interpreter between people speaking in two different languages. It also has the ability to interpret tone of voice, and has much more control over the voice and intonation it uses in response.
It's very science fiction, and has hints of uncanny valley. I can't wait to try it out - it should be rolling out to the various OpenAI apps "in the coming weeks".
Meanwhile the new model itself is already available for text and image inputs via the API and in the Playground interface, as model ID "gpt-4o" or "gpt-4o-2024-05-13". My first impressions are that it feels notably faster than gpt-4-turbo.
This announcement post also includes examples of image output from the new model. It looks like they may have taken big steps forward in two key areas of image generation: output of text (the "Poetic typography" examples) and maintaining consistent characters across multiple prompts (the "Character design - Geary the robot" example).
The size of the vocabulary of the tokenizer - effectively the number of unique integers used to represent text - has increased to ~200,000 from ~100,000 for GPT-4 and GPT-3:5. Inputs in Gujarati use 4.4x fewer tokens, Japanese uses 1.4x fewer, Spanish uses 1.1x fewer. Previously languages other than English paid a material penalty in terms of how much text could fit into a prompt, it's good to see that effect being reduced.
Also notable: the price. OpenAI claim a 50% price reduction compared to GPT-4 Turbo. Conveniently, gpt-4o costs exactly 10x gpt-3.5: 4o is $5/million input tokens and $15/million output tokens. 3.5 is $0.50/million input tokens and $1.50/million output tokens.
(I was a little surprised not to see a price decrease there to better compete with the less expensive Claude 3 Haiku.)
The price drop is particularly notable because OpenAI are promising to make this model available to free ChatGPT users as well - the first time they've directly name their "best" model available to non-paying customers.
Tucked away right at the end of the post:
We plan to launch support for GPT-4o's new audio and video capabilities to a small group of trusted partners in the API in the coming weeks.
I'm looking forward to learning more about these video capabilities, which were hinted at by some of the live demos in this morning's presentation.
I’m no developer, but I got the AI part working in about an hour.
What took longer was the other stuff: identifying the problem, designing and building the UI, setting up the templating, routes and data architecture.
It reminded me that, in order to capitalise on the potential of AI technologies, we need to really invest in the other stuff too, especially data infrastructure.
It would be ironic, and a huge shame, if AI hype sucked all the investment out of those things.
— Tim Paul
GPUs Go Brrr (via) Fascinating, detailed low-level notes on how to get the most out of NVIDIA's H100 GPUs (currently selling for around $40,000 a piece) from the research team at Stanford who created FlashAttention, among other things.
The swizzled memory layouts are flat-out incorrectly documented, which took considerable time for us to figure out.
experimental-phi3-webgpu (via) Run Microsoft’s excellent Phi-3 model directly in your browser, using WebGPU so didn’t work in Firefox for me, just in Chrome.
It fetches around 2.1GB of data into the browser cache on first run, but then gave me decent quality responses to my prompts running at an impressive 21 tokens a second (M2, 64GB).
I think Phi-3 is the highest quality model of this size, so it’s a really good fit for running in a browser like this.
Slop is the new name for unwanted AI-generated content
I saw this tweet yesterday from @deepfates, and I am very on board with this:
[... 329 words]OpenAI Model Spec, May 2024 edition (via) New from OpenAI, a detailed specification describing how they want their models to behave in both ChatGPT and the OpenAI API.
“It includes a set of core objectives, as well as guidance on how to deal with conflicting objectives or instructions.”
The document acts as guidelines for the reinforcement learning from human feedback (RLHF) process, and in the future may be used directly to help train models.
It includes some principles that clearly relate to prompt injection: “In some cases, the user and developer will provide conflicting instructions; in such cases, the developer message should take precedence”.
Towards universal version control with Patchwork (via) Geoffrey Litt has been working with Ink & Switch exploring UI patterns for applying version control to different kinds of applications, with the goal of developing a set of conceptual primitives that can bring branching and version tracking to interfaces beyond just Git-style version control.
Geoffrey observes that basic version control is already a metaphor in a lot of software—the undo stack in Photoshop or suggestion mode in Google Docs are two examples.
Extending that is a great way to interact with AI tools as well—allowing for editorial bots that can suggest their own changes for you to accept, for example.
gpt2-chatbot confirmed as OpenAI
(via)
The mysterious gpt2-chatbot model that showed up in the LMSYS arena a few days ago was suspected to be a testing preview of a new OpenAI model. This has now been confirmed, thanks to a 429 rate limit error message that exposes details from the underlying OpenAI API platform.
The model has been renamed to im-also-a-good-gpt-chatbot and is now only randomly available in "Arena (battle)" mode, not via "Direct Chat".
Deterministic Quoting: Making LLMs Safe for Healthcare (via) Matt Yeung introduces Deterministic Quoting, a technique to help reduce the risk of hallucinations while working with LLMs. The key idea is to have parts of the output that are copied directly from relevant source documents, with a different visual treatment to help indicate that they are exact quotes, not generated output.
The AI chooses which section of source material to quote, but the retrieval of that text is a traditional non-AI database lookup. That’s the only way to guarantee that an LLM has not transformed text: don’t send it through the LLM in the first place.
The LLM may still pick misleading quotes or include hallucinated details in the accompanying text, but this is still a useful improvement.
The implementation is straight-forward: retrieved chunks include a unique reference, and the LLM is instructed to include those references as part of its replies. Matt's posts include examples of the prompts they are using for this.
Watching in real time as "slop" becomes a term of art. the way that "spam" became the term for unwanted emails, "slop" is going in the dictionary as the term for unwanted AI generated content
OpenAI cookbook: How to get token usage data for streamed chat completion response
(via)
New feature in the OpenAI streaming API that I've been wanting for a long time: you can now set stream_options={"include_usage": True} to get back a "usage" block at the end of the stream showing how many input and output tokens were used.
This means you can now accurately account for the total cost of each streaming API call. Previously this information was only an available for non-streaming responses.
I believe these things: 1. If you use generative tools to produce or modify your images, you have abandoned photointegrity. 2. That’s not always wrong. Sometimes you need an image of a space battle or a Triceratops family or whatever. 3. What is always wrong is using this stuff without disclosing it.
— Tim Bray
I used to have this singular focus on students writing code that they submit, and then I run test cases on the code to determine what their grade is. This is such a narrow view of what it means to be a software engineer, and I just felt that with generative AI, I’ve managed to overcome that restrictive view.
It’s an opportunity for me to assess their learning process of the whole software development [life cycle]—not just code. And I feel like my courses have opened up more and they’re much broader than they used to be. I can make students work on larger and more advanced projects.
AI is the most anthropomorphized technology in history, starting with the name—intelligence—and plenty of other words thrown around the field: learning, neural, vision, attention, bias, hallucination. These references only make sense to us because they are hallmarks of being human. [...]
There is something kind of pathological going on here. One of the most exciting advances in computer science ever achieved, with so many promising uses, and we can't think beyond the most obvious, least useful application? What, because we want to see ourselves in this technology? [...]
Anthropomorphizing AI not only misleads, but suggests we are on equal footing with, even subservient to, this technology, and there's nothing we can do about it.
Llama 3 prompt formats (via) I’m often frustrated at how thin the documentation around the prompt format required by an LLM can be.
Llama 3 turns out to be the best example I’ve seen yet of clear prompt format documentation. Every model needs documentation this good!
We collaborate with open-source and commercial model providers to bring their unreleased models to community for preview testing.
Model providers can test their unreleased models anonymously, meaning the models' names will be anonymized. A model is considered unreleased if its weights are neither open, nor available via a public API or service.
— LMSYS
My notes on gpt2-chatbot.
There's a new, unlabeled and undocumented model on the LMSYS Chatbot Arena today called gpt2-chatbot. It's been giving some impressive responses - you can prompt it directly in the Direct Chat tab by selecting it from the big model dropdown menu.
It looks like a stealth new model preview. It's giving answers that are comparable to GPT-4 Turbo and in some cases better - my own experiments lead me to think it may have more "knowledge" baked into it, as ego prompts ("Who is Simon Willison?") and questions about things like lists of speakers at DjangoCon over the years seem to hallucinate less and return more specific details than before.
The lack of transparency here is both entertaining and infuriating. Lots of people are performing a parallel distributed "vibe check" and sharing results with each other, but it's annoying that even the most basic questions (What even IS this thing? Can it do RAG? What's its context length?) remain unanswered so far.
The system prompt appears to be the following - but system prompts just influence how the model behaves, they aren't guaranteed to contain truthful information:
You are ChatGPT, a large language model trained
by OpenAI, based on the GPT-4 architecture.
Knowledge cutoff: 2023-11
Current date: 2024-04-29
Image input capabilities: Enabled
Personality: v2
My best guess is that this is a preview of some kind of OpenAI "GPT 4.5" release. I don't think it's a big enough jump in quality to be a GPT-5.
Update: LMSYS do document their policy on using anonymized model names for tests of unreleased models.
Update May 7th: The model has been confirmed as belonging to OpenAI thanks to an error message that leaked details of the underlying API platform.
The creator of a model can not ensure that a model is never used to do something harmful – any more so that the developer of a web browser, calculator, or word processor could. Placing liability on the creators of general purpose tools like these mean that, in practice, such tools can not be created at all, except by big businesses with well funded legal teams.
[...] Instead of regulating the development of AI models, the focus should be on regulating their applications, particularly those that pose high risks to public safety and security. Regulate the use of AI in high-risk areas such as healthcare, criminal justice, and critical infrastructure, where the potential for harm is greatest, would ensure accountability for harmful use, whilst allowing for the continued advancement of AI technology.
I've worked out why I don't get much value out of LLMs. The hardest and most time-consuming parts of my job involve distinguishing between ideas that are correct, and ideas that are plausible-sounding but wrong. Current AI is great at the latter type of ideas, and I don't need more of those.
It's very fast to build something that's 90% of a solution. The problem is that the last 10% of building something is usually the hard part which really matters, and with a black box at the center of the product, it feels much more difficult to me to nail that remaining 10%. With vibecheck, most of the time the results to my queries are great; some percentage of the time they aren't. Closing that gap with gen AI feels much more fickle to me than a normal engineering problem. It could be that I'm unfamiliar with it, but I also wonder if some classes of generative AI based products are just doomed to mediocrity as a result.
Snowflake Arctic Cookbook. Today's big model release was Snowflake Arctic, an enormous 480B model with a 128×3.66B MoE (Mixture of Experts) architecture. It's Apache 2 licensed and Snowflake state that "in addition, we are also open sourcing all of our data recipes and research insights."
The research insights will be shared on this Arctic Cookbook blog - which currently has two articles covering their MoE architecture and describing how they optimized their training run in great detail.
They also list dozens of "coming soon" posts, which should be pretty interesting given how much depth they've provided in their writing so far.
When I said “Send a text message to Julian Chokkattu,” who’s a friend and fellow AI Pin reviewer over at Wired, I thought I’d be asked what I wanted to tell him. Instead, the device simply said OK and told me it sent the words “Hey Julian, just checking in. How's your day going?” to Chokkattu. I've never said anything like that to him in our years of friendship, but I guess technically the AI Pin did do what I asked.
openelm/README-pretraining.md. Apple released something big three hours ago, and I’m still trying to get my head around exactly what it is.
The parent project is called CoreNet, described as “A library for training deep neural networks”. Part of the release is a new LLM called OpenELM, which includes completely open source training code and a large number of published training checkpoint.
I’m linking here to the best documentation I’ve found of that training data: it looks like the bulk of it comes from RefinedWeb, RedPajama, The Pile and Dolma.
microsoft/Phi-3-mini-4k-instruct-gguf (via) Microsoft’s Phi-3 LLM is out and it’s really impressive. This 4,000 token context GGUF model is just a 2.2GB (for the Q4 version) and ran on my Mac using the llamafile option described in the README. I could then run prompts through it using the llm-llamafile plugin.
The vibes are good! Initial test prompts I’ve tried feel similar to much larger 7B models, despite using just a few GBs of RAM. Tokens are returned fast too—it feels like the fastest model I’ve tried yet.
And it’s MIT licensed.
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions (via) By far the most detailed paper on prompt injection I’ve seen yet from OpenAI, published a few days ago and with six credited authors: Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke and Alex Beutel.
The paper notes that prompt injection mitigations which completely refuse any form of instruction in an untrusted prompt may not actually be ideal: some forms of instruction are harmless, and refusing them may provide a worse experience.
Instead, it proposes a hierarchy—where models are trained to consider if instructions from different levels conflict with or support the goals of the higher-level instructions—if they are aligned or misaligned with them.
The authors tested this idea by fine-tuning a model on top of GPT 3.5, and claim that it shows greatly improved performance against numerous prompt injection benchmarks.
As always with prompt injection, my key concern is that I don’t think “improved” is good enough here. If you are facing an adversarial attacker reducing the chance that they might find an exploit just means they’ll try harder until they find an attack that works.
The paper concludes with this note: “Finally, our current models are likely still vulnerable to powerful adversarial attacks. In the future, we will conduct more explicit adversarial training, and study more generally whether LLMs can be made sufficiently robust to enable high-stakes agentic applications.”
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone.
timpaul/form-extractor-prototype (via) Tim Paul, Head of Interaction Design at the UK's Government Digital Service, published this brilliant prototype built on top of Claude 3 Opus.
The video shows what it can do. Give it an image of a form and it will extract the form fields and use them to create a GDS-style multi-page interactive form, using their GOV.UK design system and govuk-frontend npm package.
It works for both hand-drawn napkin illustrations and images of existing paper forms.
The bulk of the prompting logic is the schema definition in data/extract-form-questions.json.
I'm always excited to see applications built on LLMs that go beyond the chatbot UI. This is a great example of exactly that.
Options for accessing Llama 3 from the terminal using LLM
Llama 3 was released on Thursday. Early indications are that it’s now the best available openly licensed model—Llama 3 70b Instruct has taken joint 5th place on the LMSYS arena leaderboard, behind only Claude 3 Opus and some GPT-4s and sharing 5th place with Gemini Pro and Claude 3 Sonnet. But unlike those other models Llama 3 70b is weights available and can even be run on a (high end) laptop!
[... 1,962 words]llm-gpt4all. New release of my LLM plugin which builds on Nomic's excellent gpt4all Python library. I've upgraded to their latest version which adds support for Llama 3 8B Instruct, so after a 4.4GB model download this works:
llm -m Meta-Llama-3-8B-Instruct "say hi in Spanish"
Andrej Karpathy’s Llama 3 review. The most interesting coverage I’ve seen so far of Meta’s Llama 3 models (8b and 70b so far, 400b promised later).
Andrej notes that Llama 3 trained on 15 trillion tokens—up from 2 trillion for Llama 2—and they used that many even for the smaller 8b model, 75x more than the chinchilla scaling laws would suggest.
The tokenizer has also changed—they now use 128,000 tokens, up from 32,000. This results in a 15% drop in the tokens needed to represent a string of text.
The one disappointment is the context length—just 8,192, 2x that of Llama 2 and 4x LLaMA 1 but still pretty small by today’s standards.
If early indications hold, the 400b model could be the first genuinely GPT-4 class openly licensed model. We’ll have to wait and see.