Entries tagged ai in 2023
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Stuff we figured out about AI in 2023
2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.
[... 2960 words]Recommendations to help mitigate prompt injection: limit the blast radius
I’m in the latest episode of RedMonk’s Conversation series, talking with Kate Holterhoff about the prompt injection class of security vulnerabilities: what it is, why it’s so dangerous and why the industry response to it so far has been pretty disappointing.
[... 539 words]Many options for running Mistral models in your terminal using LLM
Mistral AI is the most exciting AI research lab at the moment. They’ve now released two extremely powerful smaller Large Language Models under an Apache 2 license, and have a third much larger one that’s available via their API.
[... 2063 words]The AI trust crisis
Dropbox added some new AI features. In the past couple of days these have attracted a firestorm of criticism. Benj Edwards rounds it up in Dropbox spooks users with new AI features that send data to OpenAI when used.
[... 1733 words]llamafile is the new best way to run a LLM on your own computer
Mozilla’s innovation group and Justine Tunney just released llamafile, and I think it’s now the single best way to get started running Large Language Models (think your own local copy of ChatGPT) on your own computer.
[... 650 words]Prompt injection explained, November 2023 edition
A neat thing about podcast appearances is that, thanks to Whisper transcriptions, I can often repurpose parts of them as written content for my blog.
[... 1357 words]I’m on the Newsroom Robots podcast, with thoughts on the OpenAI board
Newsroom Robots is a weekly podcast exploring the intersection of AI and journalism, hosted by Nikita Roy.
[... 1032 words]Deciphering clues in a news article to understand how it was reported
Written journalism is full of conventions that hint at the underlying reporting process, many of which are not entirely obvious. Learning how to read and interpret these can help you get a lot more out of the news.
[... 1456 words]Exploring GPTs: ChatGPT in a trench coat?
The biggest announcement from last week’s OpenAI DevDay (and there were a LOT of announcements) was GPTs. Users of ChatGPT Plus can now create their own, custom GPT chat bots that other Plus subscribers can then talk to.
[... 5699 words]ospeak: a CLI tool for speaking text in the terminal via OpenAI
I attended OpenAI DevDay today, the first OpenAI developer conference. It was a lot. They released a bewildering array of new API tools, which I’m just beginning to wade my way through fully understanding.
[... 1109 words]Now add a walrus: Prompt engineering in DALL‑E 3
Last year I wrote about my initial experiments with DALL-E 2, OpenAI’s image generation model. I’ve been having an absurd amount of fun playing with its sequel, DALL-E 3 recently. Here are some notes, including a peek under the hood and some notes on the leaked system prompt.
[... 3505 words]Execute Jina embeddings with a CLI using llm-embed-jina
Berlin-based Jina AI just released a new family of embedding models, boasting that they are the “world’s first open-source 8K text embedding model” and that they rival OpenAI’s text-embedding-ada-002 in quality.
Embeddings: What they are and why they matter
Embeddings are a really neat trick that often come wrapped in a pile of intimidating jargon.
[... 5835 words]Open questions for AI engineering
Last week I gave the closing keynote at the AI Engineer Summit in San Francisco. I was asked by the organizers to both summarize the conference, summarize the last year of activity in the space and give the audience something to think about by posing some open questions for them to take home.
[... 6928 words]Multi-modal prompt injection image attacks against GPT-4V
GPT4-V is the new mode of GPT-4 that allows you to upload images as part of your conversations. It’s absolutely brilliant. It also provides a whole new set of vectors for prompt injection attacks.
[... 889 words]Weeknotes: the Datasette Cloud API, a podcast appearance and more
Datasette Cloud now has a documented API, plus a podcast appearance, some LLM plugins work and some geospatial excitement.
[... 1243 words]Talking Large Language Models with Rooftop Ruby
I’m on the latest episode of the Rooftop Ruby podcast with Collin Donnell and Joel Drapper, talking all things LLM.
[... 15489 words]Build an image search engine with llm-clip, chat with models with llm chat
LLM is my combination CLI tool and Python library for working with Large Language Models. I just released LLM 0.10 with two significant new features: embedding support for binary files and the llm chat command.
LLM now provides tools for working with embeddings
LLM is my Python library and command-line tool for working with language models. I just released LLM 0.9 with a new set of features that extend LLM to provide tools for working with embeddings.
[... 3466 words]Making Large Language Models work for you
I gave an invited keynote at WordCamp 2023 in National Harbor, Maryland on Friday.
[... 14188 words]How I make annotated presentations
Giving a talk is a lot of work. I go by a rule of thumb I learned from Damian Conway: a minimum of ten hours of preparation for every one hour spent on stage.
[... 2122 words]Catching up on the weird world of LLMs
I gave a talk on Sunday at North Bay Python where I attempted to summarize the last few years of development in the space of LLMs—Large Language Models, the technology behind tools like ChatGPT, Google Bard and Llama 2.
[... 10489 words]Run Llama 2 on your own Mac using LLM and Homebrew
Llama 2 is the latest commercially usable openly licensed Large Language Model, released by Meta AI a few weeks ago. I just released a new plugin for my LLM utility that adds support for Llama 2 and many other llama-cpp compatible models.
[... 1423 words]Accessing Llama 2 from the command-line with the llm-replicate plugin
The big news today is Llama 2, the new openly licensed Large Language Model from Meta AI. It’s a really big deal:
[... 1206 words]Weeknotes: Self-hosted language models with LLM plugins, a new Datasette tutorial, a dozen package releases, a dozen TILs
A lot of stuff to cover from the past two and a half weeks.
[... 1742 words]My LLM CLI tool now supports self-hosted language models via plugins
LLM is my command-line utility and Python library for working with large language models such as GPT-4. I just released version 0.5 with a huge new feature: you can now install plugins that add support for additional models to the tool, including models that can run on your own hardware.
[... 1656 words]Weeknotes: symbex, LLM prompt templates, a bit of a break
I had a holiday to the UK for a family wedding anniversary and mostly took the time off... except for building symbex, which became one of those projects that kept on inspiring new features.
[... 1120 words]Symbex: search Python code for functions and classes, then pipe them into a LLM
I just released a new Python CLI tool called Symbex. It’s a search tool, loosely inspired by ripgrep, which lets you search Python code for functions and classes by name or wildcard, then see just the source code of those matching entities.
[... 1183 words]Understanding GPT tokenizers
Large language models such as GPT-3/4, LLaMA and PaLM work in terms of tokens. They take text, convert it into tokens (integers), then predict which tokens should come next.
[... 1570 words]It’s infuriatingly hard to understand how closed models train on their input
One of the most common concerns I see about large language models regards their training data. People are worried that anything they say to ChatGPT could be memorized by it and spat out to other users. People are concerned that anything they store in a private repository on GitHub might be used as training data for future versions of Copilot.
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