emerging-trajectories VS joia

I think LLMs can do a lot more than people assume, but they need to be given the proper frameworks.

When was the last time a researcher, economist, etc. was given 10,000 papers and simply told "do some original work"? That's not how it works. Daniel (the author) provides some good examples where _streamlined_ work can happen, but again, this is pretty basic stuff.

To push this further, though, imagine LLMs that fill in frameworks... A few steps here: (1) do a lit review, (2) fill in the framework, (3) discuss what might be missing, and maybe even try and fill in the missing information.

I'm doing something like this with politics and economics (see: https://emergingtrajectories.com/) and it works generally well. I think with a ton more engineering, curating of knowledge bases, etc., one can get these LLMs to actually find some new "nuggets" of information.

Admittedly, it's very hard, but I think there's something there.

RAG will have a place in the LLM world, since it's a way to obtain data/facts/info for relevant queries.

Since you asked about alternatives...

(a) "World models" where LLMs structure information into code, structured data, etc. and query those models will likely be a thing. AlphaGeometry uses this[1], and people have tried to abstract this in different ways[2].

(b) Depending on how you define RAG, knowledge graphs could be a form of RAG or alternatively an alternative to them. Companies like Elemental Cognition[3] are building distinct alternatives to RAG that use such graphs and give LLMs the ability to run queries on said graphs. Another approach here is to build "fact databases" where, you structure observations about the world into standalone concepts/ideas/observations and reference those[4]. Again, similar to RAG but not quite RAG as we know it today.

[1] https://deepmind.google/discover/blog/alphageometry-an-olymp...

[2] https://arxiv.org/abs/2306.12672

[3] https://ec.ai/

[4] https://emergingtrajectories.com/

For those interested in using search-augmented "reasoning", I implemented something similar in Emerging Trajectories[1], an open source package that forecasts geopolitical and economic events. We extract facts[2] from various websites (Google searches, news articles, RSS feeds) and have the LLM generate a hypothesis on a metric.

We're tracking the info forecasts to see how well this does for future events. For example, we're pitting the LLMs against each other to predict March 2024 CPI[3].

[1] https://emergingtrajectories.com/

[2] Sample code: https://github.com/wgryc/emerging-trajectories/blob/main/eme...

[3] https://emergingtrajectories.com/a/statement/28

I'm working on research agents to help with economic, financial, and political research. These agents are open source (see: https://github.com/wgryc/emerging-trajectories).

The use cases are pretty straight forward and low risk:

1. Run a Google web search.

2. Query a news API.

3. Write a document based on the above, while citing sources.

Here's an example of something written yesterday, where I'm forecasting whether July 2024 will be the hottest on record: https://emergingtrajectories.com/a/forecast/74

This is working well in that the writeups are great and there are some "aha" moments, like the agent finding and referencing the The National Snow and Ice Data Center (NSIDC)... Very cool! I wouldn't have thought of it.

Then there's the part where the agent also tells me that the Oregon Department of Transportation has holidays during the summer, which doesn't matter at all.

So, YMMV, as they say... But I am more productive with these agents. I wouldn't publish anything formally without confirming and reviewing the content, though.

LLM agents to forecast geopolitical and economic events.

- Site: https://emergingtrajectories.com/

- GitHub repo: https://github.com/wgryc/emerging-trajectories

I've helped a number of companies build various sorts of LLM-powered apps (chatbots mainly) and found it interesting but not incredibly inspiring. The above is my attempt to build something no one else is working on.

It's been a lot of fun. Not sure if it'll be a "thing" ever, but I enjoy it.

I've built an open-source ChatGPT UI designed for team collaboration.

Github Link: https://github.com/joiahq/joia

Benefits vs the original:

Over the past few months, we've been building Joia, an open-source alternative to "ChatGPT for Teams" that allows organizations to provide their people with access to Large Language Models. Joia's Chat UI connects not only to OpenAI but also to any LLM, including popular open-source ones like Llama 2, Mixtral, and Zephyr.

We're thrilled to share our beta with you at https://github.com/joiahq/joia

Why We Built This:

We believe that all organizations, from startups to enterprises, will soon integrate LLMs into their day-to-day operations. And while the AI ecosystem is flourishing with a wide range of models, the current providers like ChatGPT, Bard or Copilot are creating closed environments that limit interactions to their owned models only.

We envision a future where AI is interoperable, and where people will choose the pick best model for their job. We are building Joia to make this freedom of choice a reality, offering an open-source product that is easy to use, organizations-ready and built for collaboration.

Features of Our Beta Version:

User Management: Easily provide user access to LLMs by adding them to a collaborative workspace.

Multi-LLM Access: Interact smoothly with models like GPT-4, Llama 2, Mixtral, Zephyr, Perplexity, Gemini, and others. More to be added soon.

Reusable Chatbots: Create pre-configured chatbots (also known as GPTs within the OpenAI ecosystem) and share them with your teammates.

Bring Your Own API Keys: Connect directly with the LLM provider of your choice by simply inserting their API keys. We currently support OpenAI, Openrouter, and Amazon Bedrock; with more to come soon.

Purchase Credits with Us and Consolidate Payments: In our cloud version, you can buy credits and centralize the billing for all users in the organization.

Open Source License: We offer the software under the Affero license, allowing anyone to self-host the platform.

We're still in the early stages and eagerly welcome your feedback and suggestions. Please feel free to try it out at no cost and tell us what you think.

Our main website: https://joia.so

GitHub: https://github.com/joiahq/joia

[Coming soon] Open source version: We are just preparing the code to make it available to anyone who wants to self host it. We'll also welcome anyone willing to contribute. Check out our Github Repository and star it if you're interested in the project!