774 items tagged “generative-ai”
2023
Delimiters won’t save you from prompt injection
Prompt injection remains an unsolved problem. The best we can do at the moment, disappointingly, is to raise awareness of the issue. As I pointed out last week, “if you don’t understand it, you are doomed to implement it.”
[... 1,010 words]Hugging Face Transformers Agent. Fascinating new Python API in Hugging Face Transformers version v4.29.0: you can now provide a text description of a task—e.g. “Draw me a picture of the sea then transform the picture to add an island”—and a LLM will turn that into calls to Hugging Face models which will then be installed and used to carry out the instructions. The Colab notebook is worth playing with—you paste in an OpenAI API key and a Hugging Face token and it can then run through all sorts of examples, which tap into tools that include image generation, image modification, summarization, audio generation and more.
The largest model in the PaLM 2 family, PaLM 2-L, is significantly smaller than the largest PaLM model but uses more training compute. Our evaluation results show that PaLM 2 models significantly outperform PaLM on a variety of tasks, including natural language generation, translation, and reasoning. These results suggest that model scaling is not the only way to improve performance. Instead, performance can be unlocked by meticulous data selection and efficient architecture/objectives. Moreover, a smaller but higher quality model significantly improves inference efficiency, reduces serving cost, and enables the model’s downstream application for more applications and users.
— PaLM 2 Technical Report, PDF
ImageBind. New model release from Facebook/Meta AI research: “An approach to learn a joint embedding across six different modalities—images, text, audio, depth, thermal, and IMU (inertial measurement units) data”. The non-interactive demo shows searching audio starting with an image, searching images starting with audio, using text to retrieve images and audio, using image and audio to retrieve images (e.g. a barking sound and a photo of a beach to get dogs on a beach) and using audio as input to an image generator.
Language models can explain neurons in language models (via) Fascinating interactive paper by OpenAI, describing how they used GPT-4 to analyze the concepts tracked by individual neurons in their much older GPT-2 model. “We generated cluster labels by embedding each neuron explanation using the OpenAI Embeddings API, then clustering them and asking GPT-4 to label each cluster.”
When trying to get your head around a new technology, it helps to focus on how it challenges existing categorizations, conventions, and rule sets. Internally, I’ve always called this exercise, “dealing with the platypus in the room.” Named after the category-defying animal; the duck-billed, venomous, semi-aquatic, egg-laying mammal. [...] AI is the biggest platypus I’ve ever seen. Nearly every notable quality of AI and LLMs challenges our conventions, categories, and rulesets.
Jsonformer: A Bulletproof Way to Generate Structured JSON from Language Models. This is such an interesting trick. A common challenge with LLMs is getting them to output a specific JSON shape of data reliably, without occasionally messing up and generating invalid JSON or outputting other text.
Jsonformer addresses this in a truly ingenious way: it implements code that interacts with the logic that decides which token to output next, influenced by a JSON schema. If that code knows that the next token after a double quote should be a comma it can force the issue for that specific token.
This means you can get reliable, robust JSON output even for much smaller, less capable language models.
It’s built against Hugging Face transformers, but there’s no reason the same idea couldn’t be applied in other contexts as well.
What Tesla is contending is deeply troubling to the Court. Their position is that because Mr. Musk is famous and might be more of a target for deep fakes, his public statements are immune. In other words, Mr. Musk, and others in his position, can simply say whatever they like in the public domain, then hide behind the potential for their recorded statements being a deep fake to avoid taking ownership of what they did actually say and do. The Court is unwilling to set such a precedent by condoning Tesla's approach here.
Because we do not live in the Star Trek-inspired rational, humanist world that Altman seems to be hallucinating. We live under capitalism, and under that system, the effects of flooding the market with technologies that can plausibly perform the economic tasks of countless working people is not that those people are suddenly free to become philosophers and artists. It means that those people will find themselves staring into the abyss – with actual artists among the first to fall.
Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs (via) There’s a lot to absorb about this one. Mosaic trained this model from scratch on 1 trillion tokens, at a cost of $200,000 taking 9.5 days. It’s Apache-2.0 licensed and the model weights are available today.
They’re accompanying the base model with an instruction-tuned model called MPT-7B-Instruct (licensed for commercial use) and a non-commercially licensed MPT-7B-Chat trained using OpenAI data. They also announced MPT-7B-StoryWriter-65k+—“a model designed to read and write stories with super long context lengths”—with a previously unheard of 65,000 token context length.
They’re releasing these models mainly to demonstrate how inexpensive and powerful their custom model training service is. It’s a very convincing demo!
No Moat: Closed AI gets its Open Source wakeup call — ft. Simon Willison (via) I joined the Latent Space podcast yesterday (on short notice, so I was out and about on my phone) to talk about the leaked Google memo about open source LLMs. This was a Twitter Space, but swyx did an excellent job of cleaning up the audio and turning it into a podcast.
Leaked Google document: “We Have No Moat, And Neither Does OpenAI”
SemiAnalysis published something of a bombshell leaked document this morning: Google “We Have No Moat, And Neither Does OpenAI”.
[... 1,073 words]Midjourney 5.1
Midjourney released version 5.1 of their image generation model on Tuesday. Here’s their announcement on Twitter—if you have a Discord account there’s a more detailed Discord announcement here.
[... 396 words]At this point the lawsuits seem a bit far-fetched: “You should have warned us months ago that artificial intelligence would hurt your business” is unfair given how quickly ChatGPT has exploded from nowhere to become a cultural and business phenomenon. But now everyone is on notice! If you are not warning your shareholders now about how AI could hurt your business, and then it does hurt your business, you’re gonna get sued.
OpenLLaMA. The first openly licensed model I’ve seen trained on the RedPajama dataset. This initial release is a 7B model trained on 200 billion tokens, but the team behind it are promising a full 1 trillion token model in the near future. I haven’t found a live demo of this one running anywhere yet.
replit-code-v1-3b (via) As promised last week, Replit have released their 2.7b “Causal Language Model”, a foundation model trained from scratch in partnership with MosaicML with a focus on code completion. It’s licensed CC BY-SA-4.0 and is available for commercial use. They repo includes a live demo and initial experiments with it look good—you could absolutely run a local GitHub Copilot style editor on top of this model.
We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. [...] We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity: remarkably, more than 100 billion weights from these models can be ignored at inference time.
— SparseGPT, by Elias Frantar and Dan Alistarh
Prompt injection explained, with video, slides, and a transcript
I participated in a webinar this morning about prompt injection, organized by LangChain and hosted by Harrison Chase, with Willem Pienaar, Kojin Oshiba (Robust Intelligence), and Jonathan Cohen and Christopher Parisien (Nvidia Research).
[... 3,120 words]Amnesty Uses Warped, AI-Generated Images to Portray Police Brutality in Colombia. I saw massive backlash against Amnesty Norway for this on Twitter, where people argued that using AI-generated images to portray human rights violations like this undermines Amnesty’s credibility. I agree: I think this is a very risky move. An Amnesty spokesperson told VICE Motherboard that they did this to provide coverage “without endangering anyone who was present”, since many protestors who participated in the national strike covered their faces to avoid being identified.
Let’s be bear or bunny
The Machine Learning Compilation group (MLC) are my favourite team of AI researchers at the moment.
[... 599 words]Enriching data with GPT3.5 and SQLite SQL functions
I shipped openai-to-sqlite 0.3 yesterday with a fun new feature: you can now use the command-line tool to enrich data in a SQLite database by running values through an OpenAI model and saving the results, all in a single SQL query.
[... 1,219 words]MLC LLM (via) From MLC, the team that gave us Web LLM and Web Stable Diffusion. “MLC LLM is a universal solution that allows any language model to be deployed natively on a diverse set of hardware backends and native applications”. I installed their iPhone demo from TestFlight this morning and it does indeed provide an offline LLM that runs on my phone. It’s reasonably capable—the underlying model for the app is vicuna-v1-7b, a LLaMA derivative.
IF by DeepFloyd Lab (via) New image generation AI model, financially backed by StabilityAI but based on the Google Imagen paper. Claims to be much better at following complex prompts, including being able to generate text! I tried the Colab notebook with “a photograph of raccoon in the woods holding a sign that says ’I will eat your trash’” and it didn’t quite get the text right, see via link for the result.
How prompt injection attacks hijack today’s top-end AI – and it’s really tough to fix. Thomas Claburn interviewed me about prompt injection for the Register. Lots of direct quotes from our phone call in here—we went pretty deep into why it’s such a difficult problem to address.
The Dual LLM pattern for building AI assistants that can resist prompt injection
I really want an AI assistant: a Large Language Model powered chatbot that can answer questions and perform actions for me based on access to my private data and tools.
[... 2,547 words]A lot of people who claim to be doing prompt engineering today are actually just blind prompting. "Blind Prompting" is a term I am using to describe the method of creating prompts with a crude trial-and-error approach paired with minimal or no testing and a very surface level knowedge of prompting. Blind prompting is not prompt engineering. [...] In this blog post, I will make the argument that prompt engineering is a real skill that can be developed based on real experimental methodologies.
Other tech-friendly journalists I know have been going through something similar: Suddenly, we’ve got something like a jetpack to strap to our work. Sure, the jetpack is kinda buggy. Yes, sometimes it crashes and burns. And the rules for its use aren’t clear, so you’ve got to be super careful with it. But sometimes it soars, shrinking tasks that would have taken hours down to mere minutes, sometimes minutes to seconds.
The AI Writing thing is just pivot to video all over again, a bunch of dead-eyed corporate types willing to listen to any snake oil salesman who offers them higher potential profits. It'll crash in a year but scuttle hundreds of livelihoods before it does.
Bard now helps you code (via) Google have enabled Bard’s code generation abilities—these were previously only available through jailbreaking. It’s pretty good—I got it to write me code to download a CSV file and insert it into a SQLite database—though when I challenged it to protect against SQL injection it hallucinated a non-existent “cursor.prepare()” method. Generated code can be exported to a Colab notebook with a click.
Stability AI Launches the First of its StableLM Suite of Language Models (via) 3B and 7B base models, with 15B and 30B are on the way. CC BY-SA-4.0. “StableLM is trained on a new experimental dataset built on The Pile, but three times larger with 1.5 trillion tokens of content. We will release details on the dataset in due course.”