Simon Willison’s Weblog

Google was accidentally leaking its Bard AI chats into public search results. I’m quoted in this piece about yesterday’s Bard privacy bug: it turned out the share URL and “Let anyone with the link see what you’ve selected” feature wasn’t correctly setting a noindex parameter, and so some shared conversations were being swept up by the Google search crawlers. Thankfully this was a mistake, not a deliberate design decision, and it should be fixed by now. # 27th September 2023, 7:35 pm

Rethinking the Luddites in the Age of A.I. I’ve been staying way clear of comparisons to Luddites in conversations about the potential harmful impacts of modern AI tools, because it seemed to me like an offensive, unproductive cheap shot.

This article has shown me that the comparison is actually a lot more relevant—and sympathetic—than I had realized.

In a time before labor unions, the Luddites represented an early example of a worker movement that tried to stand up for their rights in the face of transformational, negative change to their specific way of life.

“Knitting machines known as lace frames allowed one employee to do the work of many without the skill set usually required” is a really striking parallel to what’s starting to happen with a surprising array of modern professions already. # 26th September 2023, 11:45 pm

A Hackers’ Guide to Language Models. Jeremy Howard’s new 1.5 hour YouTube introduction to language models looks like a really useful place to catch up if you’re an experienced Python programmer looking to start experimenting with LLMs. He covers what they are and how they work, then shows how to build against the OpenAI API, build a Code Interpreter clone using OpenAI functions, run models from Hugging Face on your own machine (with NVIDIA cards or on a Mac) and finishes with a demo of fine-tuning a Llama 2 model to perform text-to-SQL using an open dataset. # 25th September 2023, 12:24 am

LLM 0.11. I released LLM 0.11 with support for the new gpt-3.5-turbo-instruct completion model from OpenAI.

The most interesting feature of completion models is the option to request “log probabilities” from them, where each token returned is accompanied by up to 5 alternatives that were considered, along with their scores. # 19th September 2023, 3:28 pm

Simulating History with ChatGPT (via) Absolutely fascinating new entry in the using-ChatGPT-to-teach genre. Benjamin Breen teaches history at UC Santa Cruz, and has been developing a sophisticated approach to using ChatGPT to play out role-playing scenarios involving different periods of history. His students are challenged to participate in them, then pick them apart—fact-checking details from the scenario and building critiques of the perspectives demonstrated by the language model. There are so many quotable snippets in here, I recommend reading the whole thing. # 13th September 2023, 3:36 am

The AI-assistant wars heat up with Claude Pro, a new ChatGPT Plus rival. I’m quoted in this piece about the new Claude Pro $20/month subscription from Anthropic:

> Willison has also run into problems with Claude’s morality filter, which has caused him trouble by accident: “I tried to use it against a transcription of a podcast episode, and it processed most of the text before—right in front of my eyes—it deleted everything it had done! I eventually figured out that they had started talking about bomb threats against data centers towards the end of the episode, and Claude effectively got triggered by that and deleted the entire transcript.” # 10th September 2023, 5:07 pm

promptfoo: How to benchmark Llama2 Uncensored vs. GPT-3.5 on your own inputs. promptfoo is a CLI and library for “evaluating LLM output quality”. This tutorial in their documentation about using it to compare Llama 2 to gpt-3.5-turbo is a good illustration of how it works: it uses YAML files to configure the prompts, and more YAML to define assertions such as “not-icontains: AI language model”. # 10th September 2023, 4:19 pm

Matthew Honnibal from spaCy on why LLMs have not solved NLP. A common trope these days is that the entire field of NLP has been effectively solved by Large Language Models. Here’s a lengthy comment from Matthew Honnibal, creator of the highly regarded spaCy Python NLP library, explaining in detail why that argument doesn’t hold up. # 9th September 2023, 9:30 pm

hubcap.php (via) This PHP script by Dave Hulbert delights me. It’s 24 lines of code that takes a specified goal, then calls my LLM utility on a loop to request the next shell command to execute in order to reach that goal... and pipes the output straight into exec() after a 3s wait so the user can panic and hit Ctrl+C if it’s about to do something dangerous! # 6th September 2023, 3:45 pm

Using ChatGPT Code Intepreter (aka “Advanced Data Analysis”) to analyze your ChatGPT history. I posted a short thread showing how to upload your ChatGPT history to ChatGPT itself, then prompt it with “Build a dataframe of the id, title, create_time properties from the conversations.json JSON array of objects. Convert create_time to a date and plot it daily”. # 6th September 2023, 3:42 pm

Perplexity: interactive LLM visualization (via) I linked to a video of Linus Lee’s GPT visualization tool the other day. Today he’s released a new version of it that people can actually play with: it runs entirely in a browser, powered by a 120MB version of the GPT-2 ONNX model loaded using the brilliant Transformers.js JavaScript library. # 6th September 2023, 3:33 am

A token-wise likelihood visualizer for GPT-2. Linus Lee built a superb visualization to help demonstrate how Large Language Models work, in the form of a video essay where each word is coloured to show how “surprising” it is to the model. It’s worth carefully reading the text in the video as each term is highlighted to get the full effect. # 5th September 2023, 3:39 am

A practical guide to deploying Large Language Models Cheap, Good *and* Fast. Joel Kang’s extremely comprehensive notes on what he learned trying to run Vicuna-13B-v1.5 on an affordable cloud GPU server (a T4 at $0.615/hour). The space is in so much flux right now—Joel ended up using MLC but the best option could change any minute.

Vicuna 13B quantized to 4-bit integers needed 7.5GB of the T4’s 16GB of VRAM, and returned tokens at 20/second.

An open challenge running MLC right now is around batching and concurrency: “I did try making 3 concurrent requests to the endpoint, and while they all stream tokens back and the server doesn’t OOM, the output of all 3 streams seem to actually belong to a single prompt.” # 4th September 2023, 1:43 pm