Items tagged ai in Apr, 2023
Filters: Year: 2023 × Month: Apr × ai × Sorted by date
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.
[... 1219 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. # 29th April 2023, 5:43 pm
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. # 28th April 2023, 7:34 pm
GPT-3 token encoder and decoder. I built an Observable notebook with an interface to encode, decode and search through GPT-3 tokens, building on top of a notebook by EJ Fox and Ian Johnson. # 27th April 2023, 11:48 pm
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. # 26th April 2023, 6:04 pm
The Consumer Financial Protection Bureau (CFPB) supervises, sets rules for, and enforces numerous federal consumer financial laws and guards consumers in the financial marketplace from unfair, deceptive, or abusive acts or practices and from discrimination [...] the fact that the technology used to make a credit decision is too complex, opaque, or new is not a defense for violating these laws.
— The Consumer Financial Protection Bureau (PDF) # 26th April 2023, 12:36 am
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.
[... 2547 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.
— Mitchell Hashimoto # 23rd April 2023, 4:08 am
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.
— Farhad Manjoo # 21st April 2023, 8:41 pm
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.
— Dan Sheehan # 21st April 2023, 4:38 pm
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. # 21st April 2023, 3:32 pm
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.” # 19th April 2023, 3:47 pm
Inside the secret list of websites that make AI chatbots sound smart. Washington Post story digging into the C4 dataset—Colossal Clean Crawled Corpus, a filtered version of Common Crawl that’s often used for training large language models. They include a neat interactive tool for searching a domain to see if it’s included—TIL that simonwillison.net is the 106,649th ranked site in C4 by number of tokens, 189,767 total—0.0001% of the total token volume in C4. # 19th April 2023, 1:35 pm
LLaVA: Large Language and Vision Assistant (via) Yet another multi-modal model combining a vision model (pre-trained CLIP ViT-L/14) and a LLaMA derivative model (Vicuna). The results I get from their demo are even more impressive than MiniGPT-4. Also includes a new training dataset, LLaVA-Instruct-150K, derived from GPT-4 and subject to the same warnings about the OpenAI terms of service. # 19th April 2023, 1:14 am
What’s in the RedPajama-Data-1T LLM training set
RedPajama is “a project to create leading open-source models, starts by reproducing LLaMA training dataset of over 1.2 trillion tokens”. It’s a collaboration between Together, Ontocord.ai, ETH DS3Lab, Stanford CRFM, Hazy Research, and MILA Québec AI Institute.
[... 1077 words]RedPajama, a project to create leading open-source models, starts by reproducing LLaMA training dataset of over 1.2 trillion tokens. With the amount of projects that have used LLaMA as a foundation model since its release two months ago—despite its non-commercial license—it’s clear that there is a strong desire for a fully openly licensed alternative.
RedPajama is a collaboration between Together, Ontocord.ai, ETH DS3Lab, Stanford CRFM, Hazy Research, and MILA Québec AI Institute aiming to build exactly that.
Step one is gathering the training data: the LLaMA paper described a 1.2 trillion token training set gathered from sources that included Wikipedia, Common Crawl, GitHub, arXiv, Stack Exchange and more.
RedPajama-Data-1T is an attempt at recreating that training set. It’s now available to download, as 2,084 separate multi-GB jsonl files—2.67TB total.
Even without a trained model, this is a hugely influential contribution to the world of open source LLMs. Any team looking to build their own LLaMA from scratch can now jump straight to the next stage, training the model. # 17th April 2023, 5:13 pm
Latest Twitter search results for “as an AI language model” (via) Searching for “as an AI language model” on Twitter reveals hundreds of bot accounts which are clearly being driven by GPT models and have been asked to generate content which occasionally trips the ethical guidelines trained into the OpenAI models.
If Twitter still had an affordable search API someone could do some incredible disinformation research on top of this, looking at which accounts are implicated, what kinds of things they are tweeting about, who they follow and retweet and so-on. # 17th April 2023, 2:28 pm
MiniGPT-4 (via) An incredible project with a poorly chosen name. A team from King Abdullah University of Science and Technology in Saudi Arabia combined Vicuna-13B (a model fine-tuned on top of Facebook’s LLaMA) with the BLIP-2 vision-language model to create a model that can conduct ChatGPT-style conversations around an uploaded image. The demo is very impressive, and the weights are available to download—45MB for MiniGPT-4, but you’ll need the much larger Vicuna and LLaMA weights as well. # 17th April 2023, 2:21 pm
How I Used Stable Diffusion and Dreambooth to Create A Painted Portrait of My Dog (via) I like posts like this that go into detail in terms of how much work it takes to deliberately get the kind of result you really want using generative AI tools. Jake Dahn trained a Dreambooth model from 40 photos of Queso—his photogenic Golden Retriever—using Replicate, then gathered the prompts from ten images he liked on Lexica and generated over 1,000 different candidate images, picked his favourite, used Draw Things img2img resizing to expand the image beyond the initial crop, then Automatic1111 inpainting to tweak the ears, then Real-ESRGAN 4x+ to upscale for the final print. # 16th April 2023, 7:57 pm
Web LLM runs the vicuna-7b Large Language Model entirely in your browser, and it’s very impressive
A month ago I asked Could you train a ChatGPT-beating model for $85,000 and run it in a browser?. $85,000 was a hypothetical training cost for LLaMA 7B plus Stanford Alpaca. “Run it in a browser” was based on the fact that Web Stable Diffusion runs a 1.9GB Stable Diffusion model in a browser, so maybe it’s not such a big leap to run a small Large Language Model there as well.
[... 2276 words]Although fine-tuning can feel like the more natural option—training on data is how GPT learned all of its other knowledge, after all—we generally do not recommend it as a way to teach the model knowledge. Fine-tuning is better suited to teaching specialized tasks or styles, and is less reliable for factual recall. [...] In contrast, message inputs are like short-term memory. When you insert knowledge into a message, it’s like taking an exam with open notes. With notes in hand, the model is more likely to arrive at correct answers.
— Ted Sanders, OpenAI # 15th April 2023, 1:44 pm
New prompt injection attack on ChatGPT web version. Markdown images can steal your chat data. An ingenious new prompt injection / data exfiltration vector from Roman Samoilenko, based on the observation that ChatGPT can render markdown images in a way that can exfiltrate data to the image hosting server by embedding it in the image URL. Roman uses a single pixel image for that, and combines it with a trick where copy events on a website are intercepted and prompt injection instructions are appended to the copied text, in order to trick the user into pasting the injection attack directly into ChatGPT. # 14th April 2023, 6:33 pm
One way to avoid unspotted prediction errors is for the technology in its current state to have early and frequent contact with reality as it is iteratively developed, tested, deployed, and all the while improved. And there are creative ideas people don’t often discuss which can improve the safety landscape in surprising ways — for example, it’s easy to create a continuum of incrementally-better AIs (such as by deploying subsequent checkpoints of a given training run), which presents a safety opportunity very unlike our historical approach of infrequent major model upgrades.
— Greg Brockman # 14th April 2023, 6:08 pm
Prompt injection: What’s the worst that can happen?
Activity around building sophisticated applications on top of LLMs (Large Language Models) such as GPT-3/4/ChatGPT/etc is growing like wildfire right now.
[... 2302 words]Building LLM applications for production. Chip Huyen provides a useful, in-depth review of the challenges involved in taking an app built on top of a LLM from prototype to production, including issues such as prompt ambiguity and unpredictability, cost and latency concerns, challenges in testing and updating to new models. She also lists some promising use-cases she’s seeing for categories of application built on these tools. # 14th April 2023, 3:35 pm
The Great Flowering: Why OpenAI is the new AWS and the New Kingmakers still matter (via) James Governor discusses the potential impact of AI-assisted productivity on the wider software engineering industry, and calls me “a bellwether”! # 13th April 2023, 7:20 pm
Before we scramble to deeply integrate LLMs everywhere in the economy, can we pause and think whether it is wise to do so?
This is quite immature technology and we don’t understand how it works.
If we’re not careful we’re setting ourselves up for a lot of correlated failures.
— Jan Leike, Alignment Team lead, OpenAI # 13th April 2023, 7:08 pm
Free Dolly: Introducing the World’s First Truly Open Instruction-Tuned LLM (via) Databricks released a large language model called Dolly a few weeks ago. They just released Dolly 2.0 and it is MUCH more interesting—it’s an instruction tuned 12B parameter upgrade of EleutherAI’s Pythia model. Unlike other recent instruction tuned models Databricks didn’t use a training set derived from GPT-3—instead, they recruited 5,000 employees to help put together 15,000 human-generated request/response pairs, which they have released under a Creative Commons Attribution-ShareAlike license. The model itself is a 24GB download from Hugging Face—I’ve run it slowly on a small GPU-enabled Paperspace instance, but hopefully optimized ways to run it will emerge in short order. # 13th April 2023, 2:19 am
Graphic designers had a similar sea change ~20-25 years ago.
Flyers, restaurant menus, wedding invitations, price lists... That sort of thing was bread and butter work for most designers. Then desktop publishing happened and a large fraction of designers lost their main source of income as the work shifted to computer assisted unskilled labor.
The field still thrives today, but that simple work is gone forever.
— Janne Moren # 12th April 2023, 3:28 am
Running Python micro-benchmarks using the ChatGPT Code Interpreter alpha
Today I wanted to understand the performance difference between two Python implementations of a mechanism to detect changes to a SQLite database schema. I rendered the difference between the two as this chart:
[... 2939 words]