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Weeknotes: Citus Con, PyCon and three new niche museums
I’ve had a busy week in terms of speaking: on Tuesday I gave an online keynote at Citus Con, “Big Opportunities in Small Data”. I then flew to Salt Lake City for PyCon that evening and gave a three hour workshop on Wednesday, “Data analysis with SQLite and Python”.
[... 225 words]Data analysis with SQLite and Python for PyCon 2023
I’m at PyCon 2023 in Salt Lake City this week.
[... 347 words]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.
[... 1,077 words]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.
[... 2,276 words]sqlite-history: tracking changes to SQLite tables using triggers (also weeknotes)
In between blogging about ChatGPT rhetoric, micro-benchmarking with ChatGPT Code Interpreter and Why prompt injection is an even bigger problem now I managed to ship the beginnings of a new project: sqlite-history.
[... 1,680 words]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.
[... 2,302 words]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:
[... 2,939 words]Thoughts on AI safety in this era of increasingly powerful open source LLMs
This morning, VentureBeat published a story by Sharon Goldman: With a wave of new LLMs, open source AI is having a moment — and a red-hot debate. It covers the explosion in activity around openly available Large Language Models such as LLaMA—a trend I’ve been tracking in my own series LLMs on personal devices—and talks about their implications with respect to AI safety.
[... 782 words]The Changelog podcast: LLMs break the internet
I’m the guest on the latest episode of The Changelog podcast: LLMs break the internet. It’s a follow-up to the episode we recorded six months ago about Stable Diffusion.
[... 454 words]Working in public
I participated in a panel discussion this week for path to Citus Con, a series of Discord audio events that are happening in the run up to the Citus Con 2023 later this month.
[... 546 words]We need to tell people ChatGPT will lie to them, not debate linguistics
ChatGPT lies to people. This is a serious bug that has so far resisted all attempts at a fix. We need to prioritize helping people understand this, not debating the most precise terminology to use to describe it.
[... 1,174 words]Weeknotes: A new llm CLI tool, plus automating my weeknotes and newsletter
I started publishing weeknotes in 2019 partly as a way to hold myself accountable but mainly as a way to encourage myself to write more.
[... 830 words]Semi-automating a Substack newsletter with an Observable notebook
I recently started sending out a weekly-ish email newsletter consisting of content from my blog. I’ve mostly automated that, using an Observable Notebook to generate the HTML. Here’s how that system works.
[... 2,520 words]Think of language models like ChatGPT as a “calculator for words”
One of the most pervasive mistakes I see people using with large language model tools like ChatGPT is trying to use them as a search engine.
[... 1,162 words]What AI can do for you on the Theory of Change podcast
Matthew Sheffield invited me on his show Theory of Change to talk about how AI models like ChatGPT, Bing and Bard work and practical applications of things you can do with them.
[... 548 words]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.
[... 3,336 words]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.
[... 1,801 words]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]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]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]Stanford Alpaca, and the acceleration of on-device large language model development
On Saturday 11th March I wrote about how Large language models are having their Stable Diffusion moment. Today is Monday. Let’s look at what’s happened in the past three days.
[... 2,055 words]Large language models are having their Stable Diffusion moment
The open release of the Stable Diffusion image generation model back in August 2022 was a key moment. I wrote how Stable Diffusion is a really big deal at the time.
[... 1,815 words]ChatGPT couldn’t access the internet, even though it really looked like it could
A really common misconception about ChatGPT is that it can access URLs. I’ve seen many different examples of people pasting in a URL and asking for a summary, or asking it to make use of the content on that page in some way.
[... 1,745 words]Weeknotes: NICAR, and an appearance on KQED Forum
I spent most of this week at NICAR 2023, the data journalism conference hosted this year in Nashville, Tennessee.
[... 1,941 words]Thoughts and impressions of AI-assisted search from Bing
It’s been a wild couple of weeks.
[... 1,763 words]In defense of prompt engineering
Prompt engineering as a discipline doesn’t get nearly the respect it deserves.
[... 924 words]I talked about Bing and tried to explain language models on live TV!
Yesterday evening I was interviewed by Natasha Zouves on NewsNation, on live TV (over Zoom).
[... 1,697 words]Analytics: Hacker News v.s. a tweet from Elon Musk
My post Bing: “I will not harm you unless you harm me first” really took off.
[... 817 words]Bing: “I will not harm you unless you harm me first”
Last week, Microsoft announced the new AI-powered Bing: a search interface that incorporates a language model powered chatbot that can run searches for you and summarize the results, plus do all of the other fun things that engines like GPT-3 and ChatGPT have been demonstrating over the past few months: the ability to generate poetry, and jokes, and do creative writing, and so much more.
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