I'm thinking something that you add your API keys, and it has a pleasant interface for writing prompts, seeing results, and storing all the prompts and outputs for your reference. Maybe integrating with more general tools for thought. This is what I've found so far that attempts to let you...
In case it's helpful to others, I have found the term 'stochastic chameleon' to be a memorable way to describe this concept of a simulator (and a more useful one than a parrot, though inspired by that). A simulator, like a chameleon (and unlike a parrot), is doing its best to fit the distribution.
I'm thinking something that you add your API keys, and it has a pleasant interface for writing prompts, seeing results, and storing all the prompts and outputs for your reference. Maybe integrating with more general tools for thought.
This is what I've found so far that attempts to let you play with GPT-3 and other language models locally, none of which seem mature.
- https://github.com/pratos/gpt3-exp
- https://github.com/thesephist/calamity
- https://prompts.ai
Is there anything better out there that I've missed? Is anyone working on this? (It could e.g. be a VS Code plugin.)
(Pulled from https://www.lesswrong.com/posts/spBoxzcaCrqXqyQHq/using-gpt-3-to-augment-human-intelligence?commentId=q99FHPX7pSee3v49S where Kaj_Sotola has a the suggestion of trying "GPT-for-fiction-writing sites, e.g. WriteHolo", but I haven't seen any that work that well for this.)
I agree that language models like GPT-3 can be helpful in many of these ways—and the prompt you link to is great.
However, the OpenAI playground is a terrible "Tool for Thought" from a UX perspective.
Is there any great local (or web) app for exploring/learning with language models? (Or even just for GPT-3?) I'm thinking something that you add your API keys, and it has a pleasant interface for writing prompts, seeing results and storing all the outputs for your reference. Maybe integrating with more general tools for thought.
This is what I've found so far that attempts to let you play with GPT-3 and other language models locally, none of which seem mature.
- https://github.com/pratos/gpt3-exp
- https://github.com/thesephist/calamity
- https://prompts.ai
Is there anything better out there that I've missed?
Thanks for sharing this well-organized appendix and links!
As someone working on ~ the multi-stakeholder problem (likely closest to multi/single in ARCHES), it's interesting to have a summary of what you see the most relevant research being.
I think this piece is highly applicable to a particular kind of organization, with particular kinds of goals. However, it would be stronger if described that scope up front (and alluded that variants are needed in many other environments).
It might apply most to non-profits with full-time staff that aim to grow, (or are already large) and have a large pool of potential funders available if they can demonstrate effectiveness at achieving their missions, or some other reliable revenue generation. (Essentially the non-profit equivalent of venture funded startups, except maximizing mission instead of profit.)
For example, a volunteer-run community garden non-profit is going to have different core challenges! Especially if the board is the... (read more)
Many of the references and communities I might have suggested have already been listed (e.g. Metagov) so I won't repeat them (and I've also discovered some helpful new ones!).
One I didn't see mentioned is the the work of Claudia Chwalisz and her team at the OECD, which I've found immensely valuable—not only their excellent reports, summaries, etc. but an Airtable documenting hundreds of real-world governance experiments around the world (and I'm happy to connect folks into the community of practitioners implementing these new approaches to support significant policy decisions).
I have also personally been exploring many these questions at Harvard, focused primarily on governance of FAAMG companies given their impact on our 'collective... (read more)
I've seen a bunch of posts on light and sleep here, but nothing detailed re. mattresses. I ran across this which might prove valuable to future searchers, re. a modular approach to sleep design: https://www.reddit.com/r/Mattress/comments/otdqms/diy_mattresses_an_introductory_guide/
I'd also be curious to hear if others find this to be epistemically sound. Given the value of sleep, having a process for efficiently finding a near ideal sleep surface (or at least one which doesn't cause pain) seems rather important.
Assuming this is verified, contrastive decoding (or something roughly analogous to it) seems like could be helpful to mitigate this? There are many variants, but one might be actually intentionally training both the luigi and waluigi, and sampling from the difference of those distributions for each token. One could also just do this at inference time perhaps, prepending a prompt that would collapse into the waluigi and choosing tokens that are the least likely to be from that distribution. (Simplification, but hopefully gets the point across)