Update 11/04:: At the Spells & Promises exhibition, I brought a 3D-printed sculpture to life with gedankenpolizei, giving it awareness of its surroundings and making it share impressions of bystanders—one of whom filmed this video:
WhatsApp.Video.2024-11-03.at.21.11.51.mp4
I be me, you be the thought police
gedankenpolizei is an experiment where you get to observe a continuous stream of consciousness from an AI, in real-time. The project simulates a hurried, frantic inner monologue that we sometimes find ourselves having. The AI has continuous vision (but it can't hear) and might comment on what it sees, or it might choose to ignore it altogether.
The videos on this page are completely unscripted and in realtime. Making AIs understand and react to what they can see around themselves in realtime is part of ongoing research into more relatable NPCs (non-playable characters) that are aware of their environment.
You are the judge of the inner thoughts of a "sentient" simulacrum ... a dystopian visualization of the thought police creeping ever closer to our deepest thoughts. I mean, that's dark, but also serious fun when you have it running in the background. Plus you finally found a new excuse to use lolcat :)
I used Python 3.10.12. Install the runtime in a venv, clone this repo and then do
pip install -r requirements.txtas usual.
Then create an env file:
touch .envand insert your project ID for Vertex AI into the newly created .env file:
PROJECT_ID=[key here]Note that:
- A project ID is not the same as a Google API key, and if you haven't authenticated locally, you will need extra credentials (eg. via setting
GOOGLE_APPLICATION_CREDENTIALS). - I used Gemini Flash and Pro 1.5 with safety turned off and maxed out RPM at 1000. I also chose a server location closeby to minimize latency; if you experience latency issues you can set the location by changing the
gemini.locationkey inconfig.yaml.
Then install the following programs:
ffmpeg # Probably on your system already
websocat # https://github.com/vi/websocatand run the demo script used to generate the video above to see it in action:
google-chrome client/client.html & # Or Chromium or Firefox -- see QA.md
scripts/demo.shYou can press SPACE to move the camera around and C to explore the weirdness.
To get the same output quality as the demo, you need to activate the SLOW thought stream, and things are a bit more involved. Either you scrape and process it yourself as explained in REDDIT.md, or you acquire Hugging Face tokens from me1 and put them in the .env file next to the PROJECT_ID:
PROJECT_ID=[key here]
HF_TOKEN_READ=[key here]
HF_TOKEN_WRITE=[key here]Then you can run the following commands to cold-start and cache the embedding model and seeding SLOW thoughts:
python -m src.slow.embed # Download embedding model
python -m src.slow.thought # Download seed SLOW thoughtsThis is not required but enables a smooth first run.
The AI consists of Gemini 1.5 Flash and Pro, the former handling vision input and the latter outputting the raw stream of thoughts at maximum temperature. One reason to write this code was to get more insight at the less explored high temperature regime of LLMs (Gemini in this case).
If vision input is enabled, the round-trip architecture has a latency of max 1.5 seconds; meaning that the AI is generally aware of you typing code or waving your hand max 1.5 seconds after the fact. Lower latencies up to 0.7 seconds are generally possible and can be explored by tinkering with the parameters in config.yaml or passed directly through command line switches in --config key:value form.
The prompts used for each module are in data/prompts/. Importantly, the design of gedankenpolizei uses minimal prompt engineering. Actually one of the things I learned while tuning for this partcular purpose is that "everytime I fired a prompt instruction, the output quality went up". For example, the system prompt conditioning the generation of the stream of consciousness is just:
$ cat data/prompts/raw/gemini.system_prompt
I don't know what or who you are, only that you are immersed in RAW thoughts that flow spontaneously from deeper SLOW and present FAST thoughts and moods.
Complete the RAW stream directly, without repeating any of it!Nothing of the output is scripted; there is minimal guidance.
The code consists of a fancy frontend (client.html) which delivers a dystopian visualization of the project, and a text-based backend which calculates the RAW thoughts, from FAST and SLOW input thoughts, as a walk in semantic (embedding) space.2 The logic is set up as a stream of data transformers. For example,
# Start frontend
google-chrome client/client.html &
# Grab the webcam stream exposed by the frontend
grab/websocket | \
# Process the webcam stream in near-realtime into FAST thoughts
python -m src.fast.narrate --jsonl --output-frames | \
# Process FAST and SLOW thoughts into RAW thoughts and send back to frontend
python -m src.raw.stream --roll-tape 2> >(emit/websocket)This command runs the backend in the terminal and sends its output to the Javascript frontend via stderr. In the GIFs below you can see how the backend output (on the left) is rendered in the frontend (on the right) in realtime:
You can choose an optional vision input source by running one of the programs in grab/; available input sources are webcam, screencast, movie files.
For example, I like to keep gedankenpolizei open in a monitor while it watches me code; its quirky thoughts give a sort of lightness to the moment or provide some creative distraction:
grab/screen | \
python -m src.fast.narrate --jsonl --output-frames | \
python -m src.raw.streamYou can choose the stream of consciousness output source by running one of the programs in emit/ to transform the output in a fancy way, but this is completely optional and just adds persona to the output. I like to use cool-retro-term for example:
python -m src.raw.stream | emit/retrotermCombined with the previous command enabling it to see its own output, this gives a kind of HAL 9000 feeling (at least to me):
Finally, it is also possible to monitor the data streams by running one of the programs in monitor/, for example to visualize the vision input stream:
monitor/log logs/src/fast/narrate.mdNote: the optional "addons" of this section require installing some additional programs:
mdcat # https://github.com/swsnr/mdcat
lolcat
cool-retro-term- I want to thank Thalïa Viaene for unwavering support and love and Mathias Mu for artistic bro-support and working together on the NEO SEER project. He is the artist that designed and made the 3D sculpture in the video above.
- Thanks to Hannes Blommaert for capturing the video of the 3D sculpture above.
- Thanks to Google for providing an incredible LLM, generous credits and a stable API in terms of service and latency!
- Further special thanks go to three.js, websocat and mdcat projects for being great solutions to problems I had; and of course all other open source projects used by this repo.