At
@aiDotEngineer
this evening, I shared that the text autoencoder model I've been prototyping with, which I call Contra โจ, is on
@huggingface
!
Some starter code + demos๐
Colab notebook โ
Slides โ
Model โ
1/ In 2021, we shared next-gen language + conversation capabilities powered by our Language Model for Dialogue Applications (LaMDA). Coming soon: Bard, a new experimental conversational
#GoogleAI
service powered by LaMDA.
Made a little CLI that just pipes my programming questions to GPT-3, so I now can ask it stuff when I'm in the command line!
LLMs are better than Stack Overflow now โ I just ask it, and it gives me a comprehensive answer in one shot, right there in my terminal, in a couple secs.
NEW PROJECT โย I made a "personal search engine" that lets me search all my blogs, tweets, journals, notes, contacts, & more at once ๐
It's called Monocle, and features a full text search system written in Ink ๐
GitHub โจ๏ธ
Demo ๐
all these fancy nyc cafes with their no laptop policies are getting out of control
gonna open a cafe where you can't come in unless you have more than 50 unread emails and a sidebar overflowing w slack notifications and can't leave unless you clear em all out
Life update๐
I'm very excited to be joining
@NotionHQ
to continue prototyping and researching ways AI can help us be more creative, thoughtful, and productive!
Looking forward to learning from the team and bringing some of my ideas from the past year to a tool loved by many ๐
I built a personal chatbot from my personal corpus[1] a couple weeks ago on fully open-source LMs. On a whim I gave it iMessage.
Didn't expect the iMessage bit to matter, but it made a huge difference in how it feels to interact. Much more natural.
[1]
Half of
@amasad
tweets these days are like "Replit users can now run their own fusion reactor from their bedroom. We had this idea last weekend and it took our two engineers two lunch breaks to build. By next month we'll have a million teenage coders generating fusion power."
Small rant about LLMs and how I see them being put, rather thoughtlessly IMO, into productivity tools. ๐
TL;DR โ Most knowledge work isn't a text-generation task, and your product shouldn't ship an implementation detail of LLMs as the end-user interface
Built a token-wise likelihood visualizer for GPT-2 over the weekend. There are some interesting patterns and behaviors you can easily pick up from a visualization like this, like induction heads and which kinds of words/grammar LMs like to guess.
A tragically underutilized fact in productivity software today is that most people's entire textual datasets for a lifetime can fit in modern PCs' RAM.
Just load it up & search it in memory. We don't need to send everything across the planet. Things can be /so much/ faster.
Today's experiment ๐ชโ Inverting OpenAI's embedding-ada-002 model to reconstruct input texts from just embeddings.
A LOT of interesting tidbits here. I'll begin with these (cherry-picked) samples. Left column is input, middle is reconstructed from each paragraph's embedding only
ML research githubs are all like "This repo lets you reproduce and build on our results. Simply run
./scripts/train_best_model.py --with-ffn=32 --gru-cache=100 --magic-sauce --m_dims=3.1415926535 --unicorns=no_exist
* --m-dims should be set to exact value of Pi for best results
NEW DEMO!
Exploring the "length" dimension in the latent space of a language model โจ
By scrubbing up/down across the text, I'm moving this sentence up and down a direction in the embedding space corresponding to text length โ producing summaries w/ precise length control (1/n)
some life update๐
Last week was my last at Ideaflow. Starting 2022, I'm working full-time on building products, prototypes, and experiments investigating how we can build better software tools for creating and thinking.
More coming soon, but wanted to get the news out early :)
Weird idea: chunk size when doing retrieval-augmented generation is an annoying hyperparam & feels naive to tune it to a global constant value.
Could we train an e2e chunking model? i.e. system that takes in a long passage, and outputs a sequence of [span, embedding] pairs?
Wow, I just got
@AnthropicAI
's sparse autoencoder-based feature decomposition technique to work* for text embeddings ๐
Screenshot below. In order, this output shows:
1. max-activating examples for that feature from the Minipile dataset
2. min-activating examples from the sameโฆ
This is your periodic reminder that user interfaces are important, and text is a good lowest common denominator, not the endgame. The world and our senses have a lot more to offer.
I sat down with
@danshipper
to talk about how I work!
I go through the tools I use for my work and why, focusing on the ones that leverage LLMs to help me read and think. Also some peek into my past prototypes, and recs for book that inspire my work ๐
Brewing currently ๐งช
Exploring a language model's latent space on a connected canvas, branching from a single idea through connections to a tree of alternate realities.
Things that are horrifically harder than they should be:
- Text rendering
- Rich text editors
- Implementing undo/redo that won't make you pull your hair out (when mixed with autocorrect, formatting, page navigation, etc. etc.)
Tonight I'm wrestling with the third, apparently!
This morning, I've been sketching out ideas for a chat interface to language models that treat branching/multiple timelines as a first-class concept and try to make heavily branch-y threads navigable.
Some notes I've been taking...
Good tools admit virtuosity โ they have low floors and high ceilings, and are open to beginners but support mastery, so that experts can deftly close the gap between their taste and their craft.
Prompt engineering does not admit virtuosity. We need something better.
Quick little hack ๐ฆ โย a GPT token probability visualizer
Given lots of interest in my little LLM visualization from earlier in the year and a little encouragement from
@simonw
, I decided to break this out into its own little fully client-side app!
๐
It's been a wild week for me.
- 2x HN, 2x
@ProductHunt
- 100k site visits
- 1.4k๐2k followers
- Good convos w/ founders, VC folks
My main takeaway:
There is SO MUCH room in the world for projects that don't necessarily aspire to solve the world's problems. Fun is ok, too.
We don't talk enough about the fact that most creative software on the computer works by simulating a fake piece of paper and a fake typewriter or pen just so we don't have to think of or learn fundamentally new interaction modes for this fundamentally new medium.
Even the best current "tools for thought" apps require you to remember to manually make all the connections between your ideas.
Is anyone working on making the computer participate and help in this process? Suggesting connections? Finding missing links? I want to talk to you ๐
Embedding features learned with sparse autoencoders can make semantic edits to text โจ
(+ a reading/highlighting demo)
I've built an interface to explore and visualize GPT-4 labelled features learned from a text embedding model's latent space. Here's a little video, more in ๐
So I haven't done this before for some reason, but I laid out all my projects listed on side by side, and...
... yeah. I've been busy ๐
A little over 120 projects in all, most of them still functional and online! Gotta celebrate milestones sometimes ๐ช
A while ago I complained here about persistent storage in Google Colab.
Have been using
@LightningAI
Studios for a while now for:
- Full VSCode (incl. GH Copilot)
- Persisted files shared across notebooks
- Multi-GPU/node (!!)
It's been great. Feels like a remote ML workstation
Sometimes I feel like there are two visions of the future at the edges of tech right now:
To engineer scarcity into everything (crypto)
To engineer scarcity out of everything (generative AI)
Cyberpunk vs. solarpunk. Singularity vs. singularity.
Thinking about Makepad's continuous code folding animation again. Feels like we should be able to do this with prose text now โย find the key ideas/sentences and zoom out the rest of a document.
Thinking about building a "personal search engine"
A search engine that only indexes my blog, my Tweets, my journal, my calendar/email and contacts, my photos, and browser history.
I want to have better memory without having to remember more stuff. What else should it index?
Encouraged by some conversations I've had recently, I put together a list of links/papers/reports you might find interesting if you like my work.
Covers interpretability + model visualization, interface thinking, stories/fiction. I'll be adding more.
if artificial neural networks are a kind of alien intelligence, can we use it to imagine alien languages?
how could a NN teach itself to "write down" information without any human priors of what writing looks like?
Back in 2022 in my โจexperimentalโจ era I wrote down a whole bunch of ideas for tools and interfaces I want to make, but didn't get to actually prototype many of them. Here's a thread of the ones I think would still be interesting, starting with this weird mobile browser concept.
to date, this is still the best demo I've built/found to explain to folks outside of NLP how an LLM works.
Interactively visualizing autoregressive sampling from a GPT-style model.
Built a token-wise likelihood visualizer for GPT-2 over the weekend. There are some interesting patterns and behaviors you can easily pick up from a visualization like this, like induction heads and which kinds of words/grammar LMs like to guess.
Anthropic's rigor in research, their long-term principled foresight and short-term prioritization, thorough reports, and (perhaps most obviously) class-leading research communication and collaboration frequently has me in awe. Need more orgs like this.
Interesting concept msft is calling "Token healing" in their Guidance project (seems similar to LMQL):
Simple clever workaround for the "don't end your prompts with a whitespace" problem. Surprised I haven't seen it before.
This is a really interesting way to visualize QKV attention! I don't think I've seen it anywhere else. The embeddings as visualized here are kind of useless but combined with sparse autoencoder-based features from more recent work, might be interesting?
source: chemBERTa paper
Think i found a better (gradient-based) way to edit stuff in latent space. Far more precise and steerable than previous methods of just moving embeddings in different directions or adding vectors together ๐
It seems like almost everyone is building something on GPT3 these days.
But few have ever looked at its parameters.
I spent the last year studying all 175B parameters of GPT-3. Here are my favorite 6B ๐งต
(1/6000000001)
Good user interfaces don't just lower barrier to entry. They present mental models that align with underlying software behaviors, so users can contend with complexity when necessary.
Chat has to be the start, not the end, of AI UI. Talk to Nick if you're working in this space!
@whrobbins
I would argue this is a side effect of a more fundamental character trait: knowing very clearly and to a high degree of confidence what it is they want to do with their time/resources, independent of the in-vogue pursuit of the voices around them.
I feel like a good AI app shouldn't "write for you" or "search for you" any more than a good drawing app "sketches for you."
Inventing better media and tools, not replacements and prosthetics.
NEW PROJECT โ ๐ฆ YC Vibe Check ๐ฆ
YCVC is a semantic search engine over *every YC company ever*. Type in an idea, vertical, or problem space and see every YC co that's worked on it, and even some stories about them online.
๐
๐ป
Ever been coding and thought, "Boy, this would be way better if code felt like a tabloid magazine page with clickbaity headlines!"
Well have I GOT a PROGRAMMING LANGUAGE for YOU ๐
Launching "Tabloid," a clickbait language
๐ฅ
๐ป
google colab would simply be an unstoppable tool if only there was a persisted storage mechanism that did not require using your personal Google Drive to store absolutely everything
Sharing our next step in AI today!
๐ก๐ผ๐๐ถ๐ผ๐ป ๐๐ ๐ค&๐ ๐ชโask questions, search, and synthesize info in your Notion.
Our team took SOTA in LLMs and pushed beyond it for the last months. We're really proud of where it is today & where we're headed๐