470 items tagged “llms”
2023
Announcing Open Flamingo (via) New from LAION: “OpenFlamingo is a framework that enables training and evaluation of large multimodal models (LMMs)”. Multimodal here means it can answer questions about images—their interactive demo includes tools for image captioning, animal recognition, counting objects and visual question answering. Theye’ve released the OpenFlamingo-9B model built on top of LLaMA 7B and CLIP ViT/L-14—the model checkpoint is a 5.24 GB download from Hugging Face, and is available under a non-commercial research license. # 28th March 2023, 9:59 pm
By gaining mastery of language, A.I. is seizing the master key to civilization, from bank vaults to holy sepulchers.
What would it mean for humans to live in a world where a large percentage of stories, melodies, images, laws, policies and tools are shaped by nonhuman intelligence, which knows how to exploit with superhuman efficiency the weaknesses, biases and addictions of the human mind — while knowing how to form intimate relationships with human beings?
— Yuval Harari, Tristan Harris and Aza Raskin # 28th March 2023, 7:09 pm
LLaMA voice chat, with Whisper and Siri TTS. llama.cpp author Georgi Gerganov has stitched together the LLaMA language model, the Whisper voice to text model (with his whisper.cpp library) and the macOS “say” command to create an entirely offline AI agent that he can talk to with his voice and that can speak replies straight back to him. # 27th March 2023, 9:06 pm
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.
— Paul Kedrosky and Eric Norlin # 27th March 2023, 5:14 pm
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.
[... 3334 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.
— Geoffrey Litt # 27th March 2023, 6:10 am
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.
— Salvatore Sanfilippo # 26th March 2023, 2:55 pm
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. # 26th March 2023, 5:29 am
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. # 24th March 2023, 5:05 pm
I built a ChatGPT plugin to answer questions about data hosted in Datasette
Yesterday OpenAI announced support for ChatGPT plugins. It’s now possible to teach ChatGPT how to make calls out to external APIs and use the responses to help generate further answers in the current conversation.
[... 1801 words]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.
— James Vincent # 23rd March 2023, 12:10 am
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.
[... 1255 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.
[... 1950 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.
— Jack Clark # 22nd March 2023, 12:40 am
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. # 21st March 2023, 9:14 pm
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 # 21st March 2023, 6:27 pm
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). # 21st March 2023, 6:25 pm
Prompt Engineering. Extremely detailed introduction to the field of prompt engineering by Lilian Weng, who leads applied research at OpenAI. # 21st March 2023, 5:12 pm
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. # 21st March 2023, 5:04 pm
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.
— Andy Matuschak # 21st March 2023, 2:45 pm
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.
[... 1742 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. # 17th March 2023, 11:08 pm
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. # 17th March 2023, 10:54 pm
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.
[... 1751 words]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. # 17th March 2023, 12:04 am
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. # 16th March 2023, 11:41 pm
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. # 16th March 2023, 4:10 pm
As an NLP researcher I’m kind of worried about this field after 10-20 years. Feels like these oversized LLMs are going to eat up this field and I’m sitting in my chair thinking, “What’s the point of my research when GPT-4 can do it better?”
— Jeonghwan Kim # 16th March 2023, 5:39 am
I expect GPT-4 will have a LOT of applications in web scraping
The increased 32,000 token limit will be large enough to send it the full DOM of most pages, serialized to HTML—then ask questions to extract data
Or... take a screenshot and use the GPT4 image input mode to ask questions about the visually rendered page instead!
Might need to dust off all of those old semantic web dreams, because the world’s information is rapidly becoming fully machine readable
— Me # 16th March 2023, 1:09 am
bloomz.cpp (via) Nouamane Tazi Adapted the llama.cpp project to run against the BLOOM family of language models, which were released in July 2022 and trained in France on 45 natural languages and 12 programming languages using the Jean Zay Public Supercomputer, provided by the French government and powered using mostly nuclear energy.
It’s under the RAIL license which allows (limited) commercial use, unlike LLaMA.
Nouamane reports getting 16 tokens/second from BLOOMZ-7B1 running on an M1 Pro laptop. # 16th March 2023, 12:24 am