773 items tagged “generative-ai”
2024
Anthropic cookbook: multimodal. I'm currently on the lookout for high quality sources of information about vision LLMs, including prompting tricks for getting the most out of them.
This set of Jupyter notebooks from Anthropic (published four months ago to accompany the original Claude 3 models) is the best I've found so far. Best practices for using vision with Claude includes advice on multi-shot prompting with example, plus this interesting think step-by-step style prompt for improving Claude's ability to count the dogs in an image:
You have perfect vision and pay great attention to detail which makes you an expert at counting objects in images. How many dogs are in this picture? Before providing the answer in
<answer>tags, think step by step in<thinking>tags and analyze every part of the image.
Vision language models are blind (via) A new paper exploring vision LLMs, comparing GPT-4o, Gemini 1.5 Pro, Claude 3 Sonnet and Claude 3.5 Sonnet (I'm surprised they didn't include Claude 3 Opus and Haiku, which are more interesting than Claude 3 Sonnet in my opinion).
I don't like the title and framing of this paper. They describe seven tasks that vision models have trouble with - mainly geometric analysis like identifying intersecting shapes or counting things - and use those to support the following statement:
The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like of a person with myopia seeing fine details as blurry, and at worst, like an intelligent person that is blind making educated guesses.
While the failures they describe are certainly interesting, I don't think they justify that conclusion.
I've felt starved for information about the strengths and weaknesses of these vision LLMs since the good ones started becoming available last November (GPT-4 Vision at OpenAI DevDay) so identifying tasks like this that they fail at is useful. But just like pointing out an LLM can't count letters doesn't mean that LLMs are useless, these limitations of vision models shouldn't be used to declare them "blind" as a sweeping statement.
Claude: You can now publish, share, and remix artifacts. Artifacts is the feature Anthropic released a few weeks ago to accompany Claude 3.5 Sonnet, allowing Claude to create interactive HTML+JavaScript tools in response to prompts.
This morning they added the ability to make those artifacts public and share links to them, which makes them even more useful!
Here's my box shadow playground from the other day, and an example page I requested demonstrating the Milligram CSS framework - Artifacts can load most code that is available via cdnjs so they're great for quickly trying out new libraries.
Jevons paradox (via) I've been thinking recently about how the demand for professional software engineers might be affected by the fact that LLMs are getting so good at producing working code, when prompted in the right way.
One possibility is that the price for writing code will fall, in a way that massively increases the demand for custom solutions - resulting in a greater demand for software engineers since the increased value they can provide makes it much easier to justify the expense of hiring them in the first place.
TIL about the related idea of the Jevons paradox, currently explained by Wikipedia like so:
[...] when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the falling cost of use induces increases in demand enough that resource use is increased, rather than reduced.
Box shadow CSS generator (via) Another example of a tiny personal tool I built using Claude 3.5 Sonnet and artifacts. In this case my prompt was:
CSS for a slight box shadow, build me a tool that helps me twiddle settings and preview them and copy and paste out the CSS
I changed my mind half way through typing the prompt and asked it for a custom tool, and it built me this!
Here's the full transcript - in a follow-up prompt I asked for help deploying it and it rewrote the tool to use <script type="text/babel"> and the babel-standalone library to add React JSX support directly in the browser - a bit of a hefty dependency (387KB compressed / 2.79MB total) but I think acceptable for this kind of one-off tool.
Being able to knock out tiny custom tools like this on a whim is a really interesting new capability. It's also a lot of fun!
Voters in the Clapham and Brixton Hill constituency can rest easy - despite appearances, their Reform candidate Mark Matlock really does exist. [...] Matlock - based in the South Cotswolds, some 100 miles from the constituency in which he is standing - confirmed: "I am a real person." Although his campaign image is Al-generated, he said this was for lack of a real photo of him wearing a tie in Reform's trademark turquoise.
Chrome Prompt Playground.
Google Chrome Canary is currently shipping an experimental on-device LLM, in the form of Gemini Nano. You can access it via the new window.ai API, after first enabling the "Prompt API for Gemini Nano" experiment in chrome://flags (and then waiting an indeterminate amount of time for the ~1.7GB model file to download - I eventually spotted it in ~/Library/Application Support/Google/Chrome Canary/OptGuideOnDeviceModel).
I got Claude 3.5 Sonnet to build me this playground interface for experimenting with the model. You can execute prompts, stream the responses and all previous prompts and responses are stored in localStorage.
Here's the full Sonnet transcript, and the final source code for the app.
The best documentation I've found for the new API is is explainers-by-googlers/prompt-api on GitHub.
gemma-2-27b-it-llamafile (via) Justine Tunney shipped llamafile packages of Google's new openly licensed (though definitely not open source) Gemma 2 27b model this morning.
I downloaded the gemma-2-27b-it.Q5_1.llamafile version (20.5GB) to my Mac, ran chmod 755 gemma-2-27b-it.Q5_1.llamafile and then ./gemma-2-27b-it.Q5_1.llamafile and now I'm trying it out through the llama.cpp default web UI in my browser. It works great.
It's a very capable model - currently sitting at position 12 on the LMSYS Arena making it the highest ranked open weights model - one position ahead of Llama-3-70b-Instruct and within striking distance of the GPT-4 class models.
We argued that ChatGPT is not designed to produce true utterances; rather, it is designed to produce text which is indistinguishable from the text produced by humans. It is aimed at being convincing rather than accurate. The basic architecture of these models reveals this: they are designed to come up with a likely continuation of a string of text. It’s reasonable to assume that one way of being a likely continuation of a text is by being true; if humans are roughly more accurate than chance, true sentences will be more likely than false ones. This might make the chatbot more accurate than chance, but it does not give the chatbot any intention to convey truths. This is similar to standard cases of human bullshitters, who don’t care whether their utterances are true; good bullshit often contains some degree of truth, that’s part of what makes it convincing.
Accidental GPT-4o voice preview (via) Reddit user RozziTheCreator was one of a small group who were accidentally granted access to the new multimodal GPT-4o audio voice feature. They captured this video of it telling them a spooky story, complete with thunder sound effects added to the background and in a very realistic voice that clearly wasn't the one from the 4o demo that sounded similar to Scarlet Johansson.
OpenAI provided a comment for this Tom's Guide story confirming the accidental rollout so I don't think this is a faked video.
Open challenges for AI engineering
I gave the opening keynote at the AI Engineer World’s Fair yesterday. I was a late addition to the schedule: OpenAI pulled out of their slot at the last minute, and I was invited to put together a 20 minute talk with just under 24 hours notice!
[... 5,640 words]picopilot (via) Kyle Carberry's "GitHub Copilot in 70 lines of JavaScript". The title is a little hyperbolic, but the code itself really does implement an OpenAI powered Visual Studio Code text completion extension in 71 lines of code. This is an excellent example for learning what a minimal VS Code extension looks like.
Here's the system prompt it uses:
You provide code completion results given a prefix and suffix. Respond with a JSON object with the key 'completion' containing a suggestion to place between the prefix and suffix. Follow existing code styles. Listen to comments at the end of the prefix. The language is "{language}".
Then it passes the prefix and suffix as two user messages, and uses the "response_format": {"type": "json_object"} option to enforce JSON output from the GPT-4o API.
The feature this is missing is the thing that makes GitHub Copilot so impressive: Copilot does a whole bunch of clever tricks to find snippets of relevant code from the current and other nearby files and includes them with the prompt, resulting in much higher quality completions.
Listen to the AI-generated ripoff songs that got Udio and Suno sued. Jason Koebler reports on the lawsuit filed today by the RIAA against Udio and Suno, the two leading generative music startups.
The lawsuit includes examples of prompts that the record labels used to recreate famous songs that were almost certainly included in the (undisclosed) training data. Jason collected some of these together into a three minute video, and the result in pretty damning. Arguing "fair use" isn't going to be easy here.
What Apple unveiled last week with Apple Intelligence wasn't so much new products, but new features—a slew of them—for existing products, powered by generative AI.
[...] These aren't new apps or new products. They're the most used, most important apps Apple makes, the core apps that define the Apple platforms ecosystem, and Apple is using generative AI to make them better and more useful—without, in any way, rendering them unfamiliar.
llama.ttf (via) llama.ttf is "a font file which is also a large language model and an inference engine for that model".
You can see it kick into action at 8m28s in this video, where creator Søren Fuglede Jørgensen types "Once upon a time" followed by dozens of exclamation marks, and those exclamation marks then switch out to render a continuation of the story. But... when they paste the code out of the editor again it shows as the original exclamation marks were preserved - the LLM output was presented only in the way they were rendered.
The key trick here is that the font renderer library HarfBuzz (used by Firefox, Chrome, Android, GNOME and more) added a new WebAssembly extension in version 8.0 last year, which is powerful enough to run a full LLM based on the tinyllama-15M model - which fits in a 60MB font file.
(Here's a related demo from Valdemar Erk showing Tetris running in a WASM font, at 22m56s in this video.)
The source code for llama.ttf is available on GitHub.
The people who are most confident AI can replace writers are the ones who think writing is typing.
In our “who validates the validators” user studies, we found that people expected—and also desired—for the LLM to learn from any human interaction. That too, “as efficiently as possible” (ie after 1-2 demonstrations, the LLM should “get it”)
Building search-based RAG using Claude, Datasette and Val Town
Retrieval Augmented Generation (RAG) is a technique for adding extra “knowledge” to systems built on LLMs, allowing them to answer questions against custom information not included in their training data. A common way to implement this is to take a question from a user, translate that into a set of search queries, run those against a search engine and then feed the results back into the LLM to generate an answer.
[... 3,372 words]It is in the public good to have AI produce quality and credible (if ‘hallucinations’ can be overcome) output. It is in the public good that there be the creation of original quality, credible, and artistic content. It is not in the public good if quality, credible content is excluded from AI training and output OR if quality, credible content is not created.
llm-claude-3 0.4. LLM plugin release adding support for the new Claude 3.5 Sonnet model:
pipx install llm
llm install -U llm-claude-3
llm keys set claude
# paste AP| key here
llm -m claude-3.5-sonnet \
'a joke about a pelican and a walrus having lunch'
Claude 3.5 Sonnet. Anthropic released a new model this morning, and I think it's likely now the single best available LLM. Claude 3 Opus was already mostly on-par with GPT-4o, and the new 3.5 Sonnet scores higher than Opus on almost all of Anthropic's internal evals.
It's also twice the speed and one fifth of the price of Opus (it's the same price as the previous Claude 3 Sonnet). To compare:
- gpt-4o: $5/million input tokens and $15/million output
- Claude 3.5 Sonnet: $3/million input, $15/million output
- Claude 3 Opus: $15/million input, $75/million output
Similar to Claude 3 Haiku then, which both under-cuts and out-performs OpenAI's GPT-3.5 model.
In addition to the new model, Anthropic also added a "artifacts" feature to their Claude web interface. The most exciting part of this is that any of the Claude models can now build and then render web pages and SPAs, directly in the Claude interface.
This means you can prompt them to e.g. "Build me a web app that teaches me about mandelbrot fractals, with interactive widgets" and they'll do exactly that - I tried that prompt on Claude 3.5 Sonnet earlier and the results were spectacular (video demo).
An unsurprising note at the end of the post:
To complete the Claude 3.5 model family, we’ll be releasing Claude 3.5 Haiku and Claude 3.5 Opus later this year.
If the pricing stays consistent with Claude 3, Claude 3.5 Haiku is going to be a very exciting model indeed.
[...] And then some absolute son of a bitch created ChatGPT, and now look at us. Look at us, resplendent in our pauper's robes, stitched from corpulent greed and breathless credulity, spending half of the planet's engineering efforts to add chatbot support to every application under the sun when half of the industry hasn't worked out how to test database backups regularly.
Tags with descriptions. Tiny new feature on my blog: I can now add optional descriptions to my tag pages, for example on datasette and sqlite-utils and prompt-injection.
I built this feature on a live call this morning as an unplanned demonstration of GitHub's new Copilot Workspace feature, where you can run a prompt against a repository and have it plan, implement and file a pull request implementing a change to the code.
My prompt was:
Add a feature that lets me add a description to my tag pages, stored in the database table for tags and visible on the /tags/x/ page at the top
It wasn't as compelling a demo as I expected: Copilot Workspace currently has to stream an entire copy of each file it modifies, which can take a long time if your codebase includes several large files that need to be changed.
It did create a working implementation on its first try, though I had given it an extra tip not to forget the database migration. I ended up making a bunch of changes myself before I shipped it, listed in the pull request.
I've been using Copilot Workspace quite a bit recently as a code explanation tool - I'll prompt it to e.g. "add architecture documentation to the README" on a random repository not owned by me, then read its initial plan to see what it's figured out without going all the way through to the implementation and PR phases. Example in this tweet where I figured out the rough design of the Jina AI Reader API for this post.
Claude: Building evals and test cases. More documentation updates from Anthropic: this section on writing evals for Claude is new today and includes Python code examples for a number of different evaluation techniques.
Included are several examples of the LLM-as-judge pattern, plus an example using cosine similarity and another that uses the new-to-me Rouge Python library that implements the ROUGE metric for evaluating the quality of summarized text.
Anthropic release notes (via) Anthropic have started publishing release notes! Currently available for their API and their apps (mobile and web).
What I'd really like to see are release notes for the models themselves, though as far as I can tell there haven't been any updates to those since the Claude 3 models were first released (the Haiku model name in the API is still claude-3-haiku-20240307 and Anthropic say they'll change that identifier after any updates to the model).
Language models on the command-line
I gave a talk about accessing Large Language Models from the command-line last week as part of the Mastering LLMs: A Conference For Developers & Data Scientists six week long online conference. The talk focused on my LLM Python command-line utility and ways you can use it (and its plugins) to explore LLMs and use them for useful tasks.
[... 4,992 words]We're adding the human touch, but that often requires a deep, developmental edit on a piece of writing. The grammar and word choice just sound weird. You're always cutting out flowery words like 'therefore' and 'nevertheless' that don't fit in casual writing. Plus, you have to fact-check the whole thing because AI just makes things up, which takes forever because it's not just big ideas. AI hallucinates these flippant little things in throwaway lines that you'd never notice. [...]
It's tedious, horrible work, and they pay you next to nothing for it.
GitHub Copilot Chat: From Prompt Injection to Data Exfiltration (via) Yet another example of the same vulnerability we see time and time again.
If you build an LLM-based chat interface that gets exposed to both private and untrusted data (in this case the code in VS Code that Copilot Chat can see) and your chat interface supports Markdown images, you have a data exfiltration prompt injection vulnerability.
The fix, applied by GitHub here, is to disable Markdown image references to untrusted domains. That way an attack can't trick your chatbot into embedding an image that leaks private data in the URL.
Previous examples: ChatGPT itself, Google Bard, Writer.com, Amazon Q, Google NotebookLM. I'm tracking them here using my new markdown-exfiltration tag.
I understand people are upset about AI art making it to the final cut, but please try to also google artist names and compare to their portfolio before accusing them of using AI. I'm genuinely pretty upset to be accused of this. It's no fun to work on your craft for decades and then be told by some 'detection site' that your work is machine generated and people are spreading this around as a fact.
PDF to Podcast (via) At first glance this project by Stephan Fitzpatrick is a cute demo of a terrible sounding idea... but then I tried it out and the results are weirdly effective. You can listen to a fake podcast version of the transformers paper, or upload your own PDF (with your own OpenAI API key) to make your own.
It's open source (Apache 2) so I had a poke around in the code. It gets a lot done with a single 180 line Python script.
When I'm exploring code like this I always jump straight to the prompt - it's quite long, and starts like this:
Your task is to take the input text provided and turn it into an engaging, informative podcast dialogue. The input text may be messy or unstructured, as it could come from a variety of sources like PDFs or web pages. Don't worry about the formatting issues or any irrelevant information; your goal is to extract the key points and interesting facts that could be discussed in a podcast. [...]
So I grabbed a copy of it and pasted in my blog entry about WWDC, which produced this result when I ran it through Gemini Flash using llm-gemini:
cat prompt.txt | llm -m gemini-1.5-flash-latest
Then I piped the result through my ospeak CLI tool for running text-to-speech with the OpenAI TTS models (after truncating to 690 tokens with ttok because it turned out to be slightly too long for the API to handle):
llm logs --response | ttok -t 690 | ospeak -s -o wwdc-auto-podcast.mp3
And here's the result (3.9MB 3m14s MP3).
It's not as good as the PDF-to-Podcast version because Stephan has some really clever code that uses different TTS voices for each of the characters in the transcript, but it's still a surprisingly fun way of repurposing text from my blog. I enjoyed listening to it while I was cooking dinner.