1,562 posts tagged “ai”
"AI is whatever hasn't been done yet"—Larry Tesler
2024
Talking about Open Source LLMs on Oxide and Friends
I recorded an episode of the Oxide and Friends podcast on Monday, talking with Bryan Cantrill and Adam Leventhal about Open Source LLMs.
[... 1,995 words]Open Source LLMs with Simon Willison. I was invited to the Oxide and Friends weekly audio show (previously on Twitter Spaces, now using broadcast using Discord) to talk about open source LLMs, and to respond to a very poorly considered op-ed calling for them to be regulated as “uniquely dangerous”. It was a really fun conversation, now available to listen to as a podcast or YouTube audio-only video.
On being listed in the court document as one of the artists whose work was used to train Midjourney, alongside 4,000 of my closest friends (via) Poignant webcomic from Cat and Girl.
I want to make my little thing and put it out in the world and hope that sometimes it means something to somebody else.
Without exploiting anyone.
And without being exploited.
You likely have a TinyML system in your pocket right now: every cellphone has a low power DSP chip running a deep learning model for keyword spotting, so you can say "Hey Google" or "Hey Siri" and have it wake up on-demand without draining your battery. It’s an increasingly pervasive technology. [...]
It’s astonishing what is possible today: real time computer vision on microcontrollers, on-device speech transcription, denoising and upscaling of digital signals. Generative AI is happening, too, assuming you can find a way to squeeze your models down to size. We are an unsexy field compared to our hype-fueled neighbors, but the entire world is already filling up with this stuff and it’s only the very beginning. Edge AI is being rapidly deployed in a ton of fields: medical sensing, wearables, manufacturing, supply chain, health and safety, wildlife conservation, sports, energy, built environment—we see new applications every day.
Daniel Situnayake explains TinyML in a Hacker News comment. Daniel worked on TensorFlow Lite at Google and co-wrote the TinyML O’Reilly book. He just posted a multi-paragraph comment on Hacker News explaining the term and describing some of the recent innovations in that space.
“TinyML means running machine learning on low power embedded devices, like microcontrollers, with constrained compute and memory.”
More than an OpenAI Wrapper: Perplexity Pivots to Open Source. I’m increasingly impressed with Perplexity.ai—I’m using it on a daily basis now. It’s by far the best implementation I’ve seen of LLM-assisted search—beating Microsoft Bing and Google Bard at their own game.
A year ago it was implemented as a GPT 3.5 powered wrapper around Microsoft Bing. To my surprise they’ve now evolved way beyond that: Perplexity has their own search index now and is running their own crawlers, and they’re using variants of Mistral 7B and Llama 70B as their models rather than continuing to depend on OpenAI.
Budgeting with ChatGPT (via) Jon Callahan describes an ingenious system he set up to categorize his credit card transactions using GPT 3.5. He has his bank email him details of any transaction over $0, then has an email filter to forward those to Postmark, which sends them via a JSON webhook to a custom Deno Deploy app which cleans the transaction up with a GPT 3.5 prompt (including guessing the merchant) and submits the results to a base in Airtable.
AI versus old-school creativity: a 50-student, semester-long showdown (via) An interesting study in which 50 university students “wrote, coded, designed, modeled, and recorded creations with and without AI, then judged the results”.
This study seems to explore the approach of incremental prompting to produce an AI-driven final results. I use GPT-4 on a daily basis but my usage patterns are quite different: I very rarely let it actually write anything for me, instead using it as brainstorming partner, or to provide feedback, or as API reference or a thesaurus.
You Can Build an App in 60 Minutes with ChatGPT, with Geoffrey Litt (via) YouTube interview between Dan Shipper and Geoffrey Litt. They talk about how ChatGPT can build working React applications and how this means you can build extremely niche applications that you woudn’t have considered working on before—then to demonstrate that idea, they collaborate to build a note-taking app to be used just during that specific episode recording, pasting React code from ChatGPT into Replit.
Geoffrey: “I started wondering what if we had a world where everybody could craft software tools that match the workflows they want to have, unique to themselves and not just using these pre-made tools. That’s what malleable software means to me.”
The Random Transformer (via) “Understand how transformers work by demystifying all the math behind them”—Omar Sanseviero from Hugging Face meticulously implements the transformer architecture behind LLMs from scratch using Python and numpy. There’s a lot to take in here but it’s all very clearly explained.
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia. This paper describes a really interesting LLM system that runs Retrieval Augmented Generation against Wikipedia to help answer questions, but includes a second step where facts in the answer are fact-checked against Wikipedia again before returning an answer to the user. They claim “97.3% factual accuracy of its claims in simulated conversation” on a GPT-4 backed version, and also see good results when backed by LLaMA 7B.
The implementation is mainly through prompt engineering, and detailed examples of the prompts they used are included at the end of the paper.
What I should have said about the term Artificial Intelligence
With the benefit of hindsight, I did a bad job with my post, It’s OK to call it Artificial Intelligence a few days ago.
[... 376 words]Mixtral of Experts. The Mixtral paper is out, exactly a month after the release of the Mixtral 8x7B model itself. Thanks to the paper I now have a reasonable understanding of how a mixture of experts model works: each layer has 8 available blocks, but a router model selects two out of those eight for each token passing through that layer and combines their output. “As a result, each token has access to 47B parameters, but only uses 13B active parameters during inference.”
The Mixtral token context size is an impressive 32k, and it compares extremely well against the much larger Llama 70B across a whole array of benchmarks.
Unsurprising but disappointing: there’s nothing in the paper at all about what it was trained on.
OpenAI and journalism. Bit of a misleading title here: this is OpenAI’s first public response to the lawsuit filed by the New York Times concerning their use of unlicensed NYT content to train their models.
We believe that AI tools are at their best when they incorporate and represent the full diversity and breadth of human intelligence and experience. [...] Because copyright today covers virtually every sort of human expression– including blog posts, photographs, forum posts, scraps of software code, and government documents–it would be impossible to train today’s leading AI models without using copyrighted materials. Limiting training data to public domain books and drawings created more than a century ago might yield an interesting experiment, but would not provide AI systems that meet the needs of today’s citizens.
Does GPT-2 Know Your Phone Number? (via) This report from Berkeley Artificial Intelligence Research in December 2020 showed GPT-3 outputting a full page of chapter 3 of Harry Potter and the Philosopher’s Stone—similar to how the recent suit from the New York Times against OpenAI and Microsoft demonstrates memorized news articles from that publication as outputs from GPT-4.
Text Embeddings Reveal (Almost) As Much As Text. Embeddings of text—where a text string is converted into a fixed-number length array of floating point numbers—are demonstrably reversible: “a multi-step method that iteratively corrects and re-embeds text is able to recover 92% of 32-token text inputs exactly”.
This means that if you’re using a vector database for embeddings of private data you need to treat those embedding vectors with the same level of protection as the original text.
It’s OK to call it Artificial Intelligence
Update 9th January 2024: This post was clumsily written and failed to make the point I wanted it to make. I’ve published a follow-up, What I should have said about the term Artificial Intelligence which you should read instead.
[... 1,818 words]GPT in 500 lines of SQL (via) Utterly brilliant piece of PostgreSQL hackery by Alex Bolenok, who implements a full GPT-2 style language model in SQL on top of pg_vector. The final inference query is 498 lines long!
Microsoft Research relicense Phi-2 as MIT (via) Phi-2 was already an interesting model—really strong results for its size—made available under a non-commercial research license. It just got significantly more interesting: Microsoft relicensed it as MIT open source.
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (via) NIST—the National Institute of Standards and Technology, a US government agency, released a 106 page report on attacks against modern machine learning models, mostly covering LLMs.
Prompt injection gets two whole sections, one on direct prompt injection (which incorporates jailbreaking as well, which they misclassify as a subset of prompt injection) and one on indirect prompt injection.
They talk a little bit about mitigations, but for both classes of attack conclude: “Unfortunately, there is no comprehensive or foolproof solution for protecting models against adversarial prompting, and future work will need to be dedicated to investigating suggested defenses for their efficacy.”
My blog’s year archive pages now have tag clouds (via) Inspired by the tag cloud I used in my recent 2023 AI roundup post, I decided to add a tag cloud to the top of every one of my archive-by-year pages showing what topics I had spent the most time with that year.
I already had old code for this, so I pasted it into GPT-4 along with an example of the output of my JSON endpoint from Django SQL Dashboard and had it do most of the work for me.
Since the advent of ChatGPT, and later by using LLMs that operate locally, I have made extensive use of this new technology. The goal is to accelerate my ability to write code, but that's not the only purpose. There's also the intent to not waste mental energy on aspects of programming that are not worth the effort.
[...] Current LLMs will not take us beyond the paths of knowledge, but if we want to tackle a topic we do not know well, they can often lift us from our absolute ignorance to the point where we know enough to move forward on our own.
2023
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.
[... 2,974 words]Pushing ChatGPT’s Structured Data Support To Its Limits. The GPT 3.5, 4 and 4 Turbo APIs all provide “function calling”—a misnamed feature that allows you to feed them a JSON schema and semi-guarantee that the output from the prompt will conform to that shape.
Max explores the potential of that feature in detail here, including some really clever applications of it to chain-of-thought style prompting.
He also mentions that it may have some application to preventing prompt injection attacks. I’ve been thinking about function calls as one of the most concerning potential targets of prompt injection, but Max is right in that there may be some limited applications of them that can help prevent certain subsets of attacks from taking place.
OpenAI Begins Tackling ChatGPT Data Leak Vulnerability (via) ChatGPT has long suffered from a frustrating data exfiltration vector that can be triggered by prompt injection attacks: it can be instructed to construct a Markdown image reference to an image hosted anywhere, which means a successful prompt injection can request the model encode data (e.g. as base64) and then render an image which passes that data to an external server as part of the query string.
Good news: they've finally put measures in place to mitigate this vulnerability!
The fix is a bit weird though: rather than block all attempts to load images from external domains, they have instead added an additional API call which the frontend uses to check if an image is "safe" to embed before rendering it on the page.
This feels like a half-baked solution to me. It isn't available in the iOS app yet, so that app is still vulnerable to these exfiltration attacks. It also seems likely that a suitable creative attack could still exfiltrate data in a way that outwits the safety filters, using clever combinations of data hidden in subdomains or filenames for example.
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]Facebook Is Being Overrun With Stolen, AI-Generated Images That People Think Are Real. Excellent investigative piece by Jason Koebler digging into the concerning trend of Facebook engagement farming accounts who take popular aspirational images and use generative AI to recreate hundreds of variants of them, which then gather hundreds of comments from people who have no idea that the images are fake.
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.
[... 2,063 words]Google DeepMind used a large language model to solve an unsolvable math problem. I’d been wondering how long it would be before we saw this happen: a genuine new scientific discovery found with the aid of a Large Language Model.
DeepMind found a solution to the previously open “cap set” problem using Codey, a fine-tuned variant of PaLM 2 specializing in code. They used it to generate Python code and found a solution after “a couple of million suggestions and a few dozen repetitions of the overall process”.