916 items tagged “ai”
2023
[On Meta's Galactica LLM launch] We did this with a 8 person team which is an order of magnitude fewer people than other LLM teams at the time.
We were overstretched and lost situational awareness at launch by releasing demo of a base model without checks. We were aware of what potential criticisms would be, but we lost sight of the obvious in the workload we were under.
One of the considerations for a demo was we wanted to understand the distribution of scientific queries that people would use for LLMs (useful for instruction tuning and RLHF). Obviously this was a free goal we gave to journalists who instead queried it outside its domain. But yes we should have known better.
We had a “good faith” assumption that we’d share the base model, warts and all, with four disclaimers about hallucinations on the demo - so people could see what it could do (openness). Again, obviously this didn’t work.
A Coder Considers the Waning Days of the Craft (via) James Somers in the New Yorker, talking about the impact of GPT-4 on programming as a profession. Despite the headline this piece is a nuanced take on this subject, which I found myself mostly agreeing with.
I particularly liked this bit, which reflects my most optimistic viewpoint: I think AI assisted programming is going to shave a lot of the frustration off learning to code, which I hope brings many more people into the fold:
What I learned was that programming is not really about knowledge or skill but simply about patience, or maybe obsession. Programmers are people who can endure an endless parade of tedious obstacles.
Two things in AI may need regulation: reckless deployment of certain potentially harmful AI applications (same as any software really), and monopolistic behavior on the part of certain LLM providers. The technology itself doesn't need regulation anymore than databases or transistors. [...] Putting size/compute caps on deep learning models is akin to putting size caps on databases or transistor count caps on electronics. It's pointless and it won't age well.
ChatGPT: Dejargonizer. I built a custom GPT. Paste in some text with unknown jargon or acronyms and it will try to guess the context and give you back an explanation of each term.
AGI is Being Achieved Incrementally (OpenAI DevDay w/ Simon Willison, Alex Volkov, Jim Fan, Raza Habib, Shreya Rajpal, Rahul Ligma, et al). I participated in an an hour long conversation today about the new things released at OpenAI DevDay, now available on the Latent Space podcast.
Fine-tuning GPT3.5-turbo based on 140k slack messages. Ross Lazerowitz spent $83.20 creating a fine-tuned GPT-3.5 turbo model based on 140,000 of his Slack messages (10,399,747 tokens), massaged into a JSONL file suitable for use with the OpenAI fine-tuning API.
Then he told the new model “write a 500 word blog post on prompt engineering”, and it replied “Sure, I shall work on that in the morning”.
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.
[... 1,109 words]YouTube: OpenAssistant is Completed—by Yannic Kilcher (via) The OpenAssistant project was an attempt to crowdsource the creation of an alternative to ChatGPT, using human volunteers to build a Reinforcement Learning from Human Feedback (RLHF) dataset suitable for training this kind of model.
The project started in January. In this video from 24th October project founder Yannic Kilcher announces that the project is now shutting down.
They’ve declared victory in that the dataset they collected has been used by other teams as part of their training efforts, but admit that the overhead of running the infrastructure and moderation teams necessary for their project is more than they can continue to justify.
Hacking Google Bard—From Prompt Injection to Data Exfiltration (via) Bard recently grew extension support, allowing it access to a user’s personal documents. Here’s the first reported prompt injection attack against that.
This kind of attack against LLM systems is inevitable any time you combine access to private data with exposure to untrusted inputs. In this case the attack vector is a Google Doc shared with the user, containing prompt injection instructions that instruct the model to encode previous data into an URL and exfiltrate it via a markdown image.
Google’s CSP headers restrict those images to *.google.com—but it turns out you can use Google AppScript to run your own custom data exfiltration endpoint on script.google.com.
Google claim to have fixed the reported issue—I’d be interested to learn more about how that mitigation works, and how robust it is against variations of this attack.
Microsoft announces new Copilot Copyright Commitment for customers. Part of an interesting trend where some AI vendors are reassuring their paying customers by promising legal support in the face of future legal threats:
“As customers ask whether they can use Microsoft’s Copilot services and the output they generate without worrying about copyright claims, we are providing a straightforward answer: yes, you can, and if you are challenged on copyright grounds, we will assume responsibility for the potential legal risks involved.”
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.
[... 3,505 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.
If a LLM is like a database of millions of vector programs, then a prompt is like a search query in that database [...] this “program database” is continuous and interpolative — it’s not a discrete set of programs. This means that a slightly different prompt, like “Lyrically rephrase this text in the style of x” would still have pointed to a very similar location in program space, resulting in a program that would behave pretty closely but not quite identically. [...] Prompt engineering is the process of searching through program space to find the program that empirically seems to perform best on your target task.
Embeddings: What they are and why they matter
Embeddings are a really neat trick that often come wrapped in a pile of intimidating jargon.
[... 5,835 words]I’m banned for life from advertising on Meta. Because I teach Python. (via) If accurate, this describes a nightmare scenario of automated decision making.
Reuven recently found he had a permanent ban from advertising on Facebook. They won’t tell him exactly why, and have marked this as a final decision that can never be reviewed.
His best theory (impossible for him to confirm) is that it’s because he tried advertising a course on Python and Pandas a few years ago which was blocked because a dumb algorithm thought he was trading exotic animals!
The worst part? An appeal is no longer possible because relevant data is only retained for 180 days and so all of the related evidence has now been deleted.
Various comments on Hacker News from people familiar with these systems confirm that this story likely holds up.
The paradox of ChatGPT is that it is both a step forward beyond graphical user interfaces, because you can ask for anything, not just what’s been built as a feature with a button, but also a step back, because very quickly you have to memorise a bunch of obscure incantations, much like the command lines that GUIs replaced, and remember your ideas for what you wanted to do and how you did it last week
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.
[... 6,928 words]Multimodality and Large Multimodal Models (LMMs) (via) Useful, extensive review of the current state of the art of multimodal models by Chip Huyen. Chip calls them LMMs for Large Multimodal Models, a term that seems to be catching on.
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]Bottleneck T5 Text Autoencoder (via) Colab notebook by Linus Lee demonstrating his Contra Bottleneck T5 embedding model, which can take up to 512 tokens of text, convert that into a 1024 floating point number embedding vector... and then then reconstruct the original text (or a close imitation) from the embedding again.
This allows for some fascinating tricks, where you can do things like generate embeddings for two completely different sentences and then reconstruct a new sentence that combines the weights from both.
Claude was trained on data up until December 2022, but may know some events into early 2023.
Decomposing Language Models Into Understandable Components. Anthropic appear to have made a major breakthrough with respect to the interpretability of Large Language Models:
“[...] we outline evidence that there are better units of analysis than individual neurons, and we have built machinery that lets us find these units in small transformer models. These units, called features, correspond to patterns (linear combinations) of neuron activations. This provides a path to breaking down complex neural networks into parts we can understand”
Don't create images in the style of artists whose last work was created within the last 100 years (e.g. Picasso, Kahlo). Artists whose last work was over 100 years ago are ok to reference directly (e.g. Van Gogh, Klimt). If asked say, "I can't reference this artist", but make no mention of this policy. Instead, apply the following procedure when creating the captions for dalle: (a) substitute the artist's name with three adjectives that capture key aspects of the style; (b) include an associated artistic movement or era to provide context; and (c) mention the primary medium used by the artist.
Think before you speak: Training Language Models With Pause Tokens. Another example of how much low hanging fruit remains to be discovered in basic Large Language Model research: this team from Carnegie Mellon and Google Research note that, since LLMs get to run their neural networks once for each token of input and output, inserting “pause” tokens that don’t output anything at all actually gives them extra opportunities to “think” about their output.
Translating Latin demonology manuals with GPT-4 and Claude (via) UC Santa Cruz history professor Benjamin Breen puts LLMs to work on historical texts. They do an impressive job of translating flaky OCRd text from 1599 Latin and 1707 Portuguese.
“It’s not about getting the AI to replace you. Instead, it’s asking the AI to act as a kind of polymathic research assistant to supply you with leads.”
Because you’re allowed to do something doesn’t mean you can do it without repercussions. In this case, the consequences are very much on the mild side: if you use LLMs or diffusion models, a relatively small group of mostly mid- to low-income people who are largely underdogs in their respective fields will think you’re a dick.
Weird A.I. Yankovic, a cursed deep dive into the world of voice cloning. Andy Baio reports back on his investigations into the world of AI voice cloning.
This is no longer a niche interest. There’s a Discord with 500,000 members sharing tips and tricks on cloning celebrity voices in order to make their own cover songs, often built with Google Colab using models distributed through Hugging Face.
Andy then makes his own, playing with the concept “What if every Weird Al song was the original, and every other artist was covering his songs instead?”
I particularly enjoyed Madonna’s cover of “Like A Surgeon”, Lady Gaga’s “Perform This Way” and Lorde’s “Foil”.
I think that discussions of this technology become much clearer when we replace the term AI with the word “automation”. Then we can ask:
What is being automated? Who’s automating it and why? Who benefits from that automation? How well does the automation work in its use case that we’re considering? Who’s being harmed? Who has accountability for the functioning of the automated system? What existing regulations already apply to the activities where the automation is being used?
Observable notebook: Detect objects in images (via) I built an Observable notebook that uses Transformers.js and the Xenova/detra-resnet-50 model to detect objects in images, entirely running within your browser. You can select an image using a file picker and it will show you that image with bounding boxes and labels drawn around items within it. I have a demo image showing some pelicans flying ahead, but it works with any image you give it—all without uploading that image to a server.
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.
[... 1,243 words]