456 items tagged “generativeai”
2023
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.
— François Chollet # 25th October 2023, 11:26 pm
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]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
— Benedict Evans # 17th October 2023, 11:09 pm
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]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. # 14th October 2023, 7:51 pm
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. # 10th October 2023, 2:12 am
Claude was trained on data up until December 2022, but may know some events into early 2023.
— How up-to-date is Claude's training data? # 9th October 2023, 1:25 am
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” # 8th October 2023, 3:43 pm
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.
— DALL-E 3 leaked prompt # 7th October 2023, 7:35 pm
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. # 4th October 2023, 4:23 pm
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.” # 4th October 2023, 1:49 am
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.
— Baldur Bjarnason # 3rd October 2023, 4:03 pm
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”. # 2nd October 2023, 6:50 pm
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]Looking at LLMs as chatbots is the same as looking at early computers as calculators. We’re seeing an emergence of a whole new computing paradigm, and it is very early.
— Andrej Karpathy # 28th September 2023, 8:50 pm
Finding Bathroom Faucets with Embeddings. Absolutely the coolest thing I’ve seen someone build on top of my LLM tool so far: Drew Breunig is renovating a bathroom and needed a way to filter through literally thousands of options for facet taps. He scraped 20,000 images of fixtures from a plumbing supply site and used LLM to embed every one of them via CLIP... and now he can ask for “faucets that look like this one”, or even run searches for faucets that match “Gawdy” or “Bond Villain” or “Nintendo 64”. Live demo included! # 27th September 2023, 6:18 pm
The profusion of dubious A.I.-generated content resembles the badly made stockings of the nineteenth century. At the time of the Luddites, many hoped the subpar products would prove unacceptable to consumers or to the government. Instead, social norms adjusted.
— Kyle Chayka # 27th September 2023, 12:26 am
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
We already know one major effect of AI on the skills distribution: AI acts as a skills leveler for a huge range of professional work. If you were in the bottom half of the skill distribution for writing, idea generation, analyses, or any of a number of other professional tasks, you will likely find that, with the help of AI, you have become quite good.
— Ethan Mollick # 25th September 2023, 4:37 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
In the long term, I suspect that LLMs will have a significant positive impact on higher education. Specifically, I believe they will elevate the importance of the humanities. [...] LLMs are deeply, inherently textual. And they are reliant on text in a way that is directly linked to the skills and methods that we emphasize in university humanities classes.
— Benjamin Breen # 13th September 2023, 3:40 am
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
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.
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