915 items tagged “ai”
2023
Every wave of technological innovation has been unleashed by something costly becoming cheap enough to waste. Software production has been too complex and expensive for too long, which has caused us to underproduce software for decades, resulting in immense, society-wide technical debt. This technical debt is about to contract in a dramatic, economy-wide fashion as the cost and complexity of software production collapses, releasing a wave of innovation.
AI-enhanced development makes me more ambitious with my projects
The thing I’m most excited about in our weird new AI-enhanced reality is the way it allows me to be more ambitious with my projects.
[... 3,334 words]I think it’s likely that soon all computer users will have the ability to develop small software tools from scratch, and to describe modifications they’d like made to software they’re already using.
I lost everything that made me love my job through Midjourney over night. A poster on r/blender describes how their job creating graphics for mobile games has switched from creating 3D models for rendering 2D art to prompting Midjourney v5 and cleaning up the results in Photoshop. “I am now able to create, rig and animate a character thats spit out from MJ in 2-3 days. Before, it took us several weeks in 3D. [...] I always was very sure I wouldn’t lose my job, because I produce slightly better quality. This advantage is gone, and so is my hope for using my own creative energy to create.”
After three decades of working with software, I'm also seeing myself learning faster using ChatGPT. So apparently it works even for us more seasoned programmers.
scrapeghost (via) Scraping is a really interesting application for large language model tools like GPT3. James Turk’s scrapeghost is a very neatly designed entrant into this space—it’s a Python library and CLI tool that can be pointed at any URL and given a roughly defined schema (using a neat mini schema language) which will then use GPT3 to scrape the page and try to return the results in the supplied format.
Hello Dolly: Democratizing the magic of ChatGPT with open models. A team at DataBricks applied the same fine-tuning data used by Stanford Alpaca against LLaMA to a much older model—EleutherAI’s GPT-J 6B, first released in May 2021. As with Alpaca, they found that instruction tuning took the raw model—which was extremely difficult to interact with—and turned it into something that felt a lot more like ChatGPT. It’s a shame they reused the license-encumbered 52,000 training samples from Alpaca, but I doubt it will be long before someone recreates a freely licensed alternative to that training set.
ChatGPT plugins. ChatGPT is getting a plugins mechanism, which will allow developers to provide extra capabilities to ChatGPT, like looking up restaurants on OpenTable or fetching data from APIs. This feels like the kind of feature that could obsolete—or launch—a thousand startups. It also makes ChatGPT much more interesting as a general purpose tool, as opposed to something that only works as an interface to a language model.
mitsua-diffusion-one (via) “Mitsua Diffusion One is a latent text-to-image diffusion model, which is a successor of Mitsua Diffusion CC0. This model is trained from scratch using only public domain/CC0 or copyright images with permission for use.” I’ve been talking about how much I’d like to try out a “vegan” AI model trained entirely on out-of-copyright images for ages, and here one is! It looks like the training data mainly came from CC0 art gallery collections such as the Metropolitan Museum of Art Open Access.
If you ask Microsoft’s Bing chatbot if Google’s Bard chatbot has been shut down, it says yes, citing as evidence a news article that discusses a tweet in which a user asked Bard when it would be shut down and Bard said it already had, itself citing a comment from Hacker News in which someone joked about this happening, and someone else used ChatGPT to write fake news coverage about the event.
Weeknotes: AI won’t slow down, a new newsletter and a huge Datasette refactor
I’m a few weeks behind on my weeknotes, but it’s not through lack of attention to my blog. AI just keeps getting weirder and more interesting.
[... 1,255 words]Don’t trust AI to talk accurately about itself: Bard wasn’t trained on Gmail
Earlier this month I wrote about how ChatGPT can’t access the internet, even though it really looks like it can. Consider this part two in the series. Here’s another common and non-intuitive mistake people make when interacting with large language model AI systems: asking them questions about themselves.
[... 1,950 words]GPT-4, like GPT-3 before it, has a capability overhang; at the time of release, neither OpenAI or its various deployment partners have a clue as to the true extent of GPT-4's capability surface - that's something that we'll get to collectively discover in the coming years. This also means we don't know the full extent of plausible misuses or harms.
The Age of AI has begun. Bill Gates calls GPT-class large language models “the most important advance in technology since the graphical user interface”. His essay here focuses on the philanthropy angle, mostly from the point of view of AI applications in healthcare, education and concerns about keeping access to these new technologies as equitable as possible.
Here are some absurdly expensive things you can do on a trip to Tokyo: Buy a golden toilet. There is a toilet in Tokyo that is made of gold and costs around 10 million yen. If you are looking for a truly absurd experience, you can buy this toilet and use it for your next bowel movement. [...]
Google Bard is now live. Google Bard launched today. There’s a waiting list, but I made it through within a few hours of signing up, as did other people I’ve talked to. It’s similar to ChatGPT and Bing—it’s the same chat interface, and it can clearly run searches under the hood (though unlike Bing it doesn’t tell you what it’s looking for).
Prompt Engineering. Extremely detailed introduction to the field of prompt engineering by Lilian Weng, who leads applied research at OpenAI.
Bing Image Creator comes to the new Bing. Bing Chat is integrating DALL-E directly into their interface, giving it the ability to generate images when prompted to do so.
Adobe made an AI image generator — and says it didn’t steal artists’ work to do it. Adobe Firefly is a brand new text-to-image model which Adobe claim was trained entirely on fully licensed imagery—either out of copyright, specially licensed or part of the existing Adobe Stock library. I’m sure they have the license, but I still wouldn’t be surprised to hear complaints from artists who licensed their content to Adobe Stock who didn’t anticipate it being used for model training.
OpenAI to discontinue support for the Codex API (via) OpenAI shutting off access to their Codex model—a GPT3 variant fine-tuned for code related tasks, but that was being used for all sorts of other purposes—partly because it had been in a beta phase for over a year where OpenAI didn’t charge anything for it. This feels to me like a major strategic misstep for OpenAI: they’re only giving three days notice, which is shaking people’s confidence in them as a stable platform for building on at the very moment when competition from other vendors (and open source alternatives) is heating up.
Was on a plane yesterday, studying some physics; got confused about something and I was able to solve my problem by just asking alpaca-13B—running locally on my machine—for an explanation. Felt straight-up spooky.
A conversation about prompt engineering with CBC Day 6
I’m on Canadian radio this morning! I was interviewed by Peter Armstrong for CBC Day 6 about the developing field of prompt engineering.
[... 1,742 words]Fine-tune LLaMA to speak like Homer Simpson. Replicate spent 90 minutes fine-tuning LLaMA on 60,000 lines of dialog from the first 12 seasons of the Simpsons, and now it can do a good job of producing invented dialog from any of the characters from the series. This is a really interesting result: I’ve been skeptical about how much value can be had from fine-tuning large models on just a tiny amount of new data, assuming that the new data would be statistically irrelevant compared to the existing model. Clearly my mental model around this was incorrect.
The Unpredictable Abilities Emerging From Large AI Models (via) Nice write-up of the most interesting aspect of large language models: the fact that they gain emergent abilities at certain “breakthrough” size points, and no-one is entirely sure they understand why.
Could you train a ChatGPT-beating model for $85,000 and run it in a browser?
I think it’s now possible to train a large language model with similar functionality to GPT-3 for $85,000. And I think we might soon be able to run the resulting model entirely in the browser, and give it capabilities that leapfrog it ahead of ChatGPT.
[... 1,751 words]A simple Python implementation of the ReAct pattern for LLMs. I implemented the ReAct pattern (for Reason+Act) described in this paper. It's a pattern where you implement additional actions that an LLM can take - searching Wikipedia or running calculations for example - and then teach it how to request that those actions are run, then feed their results back into the LLM.
Web Stable Diffusion (via) I just ran the full Stable Diffusion image generation model entirely in my browser, and used it to generate an image (of two raccoons eating pie in the woods, see “via” link). I had to use Google Chrome Canary since this depends on WebGPU which still isn’t fully rolled out, but it worked perfectly.
The surprising ease and effectiveness of AI in a loop (via) Matt Webb on the langchain Python library and the ReAct design pattern, where you plug additional tools into a language model by teaching it to work in a “Thought... Act... Observation” loop where the Act specifies an action it wishes to take (like searching Wikipedia) and an extra layer of software than carries out that action and feeds back the result as the Observation. Matt points out that the ChatGPT 1/10th price drop makes this kind of model usage enormously more cost effective than it was before.
Transformers.js. Hugging Face Transformers is a library of Transformer machine learning models plus a Python package for loading and running them. Transformers.js provides a JavaScript alternative interface which runs in your browser, thanks to a set of precompiled WebAssembly binaries for a selection of models. This interactive demo is incredible: in particular, try running the Image classification with google/vit-base-patch16-224 (91MB) model against any photo to get back labels representing that photo. Dropping one of these models onto a page is as easy as linking to a hosted CDN script and running a few lines of JavaScript.
Train and run Stanford Alpaca on your own machine. The team at Replicate managed to train their own copy of Stanford’s Alpaca—a fine-tuned version of LLaMA that can follow instructions like ChatGPT. Here they provide step-by-step instructions for recreating Alpaca yourself—running the training needs one or more A100s for a few hours, which you can rent through various cloud providers.