This channel is so refreshing. In a world filled with hype, you give us just the facts and a very clear overview and I'm very grateful. I can't wait to see one of these auto-language model thingies use a local offline LLM to do something useful. I guess I gotta return my candles now that I know I can't bring them in with me.

Dude I love how you always include a colab notebook to experiment with!

Thank you, Sam. That was very helpful to explain what BabyAGI is all about. Essentially, it's a task list builder with iterative refinement. When the algorithms are improved, there's definitely some value to be had here. I think we'll end up seeing the autonomy capped and more "task structure patterns" introduced. It's a great initial proof of concept that has been spawning others to try their hand with differing approaches. TBD.

This is nuts. In my mind I visualize these agents as complex text filters with fuzzy logic - at least that's how I explain it to my wife during my endless (positive) rants about AI. Still married after 30 years but the endless rants are probably stretching the relationship a little more than usual. I feel like a boy that just received a box of magical lego bricks.

Someone is working on getting this going with your local textgen instance. Who ever wants to work on this should. It will be powerful to be able to run something like this on your home computer. Llama is pretty smart and should give some very good results. Lets hope it comes out soon. If someone is in the process of getting this to work locally I would love to help in what ever way I can :)

Sometimes it’s hard to know what the differences are given AutoGPT, Jarvis, Langchain, BabyGPT etc.

I've spent a day playing on this code Saturday after playing with Auto-GPT. Few comments: It had a few bugs when I tested it. Some tasks generated more tasklist, and where not identified as such, and they are later caught up by their prioritizer creating a bizarre pattern of tasks having several ids and numbers in the begining of their name. Also, to avoid loop, there's easy optimization to just locally store a cache that keep result of past tasks, and preventing them to be re-inserted. I'm not a big fan of adding yet another API dependency (the pinecone one), as far as I understand, they're just using it to do concept search using OpenAI embeddings (i.e., they vectorized result of a task and store in pinecone). Unless you're planning to have a huge task like millions of documents, there's no need of an elastic resource here, you might as well run a local vector storage. After a few hours, I ended up implementing a containerized local weaviate to replace pinecone in their code. I'm curious why Auto-GPT and this project keep throwing pinecone in their dependencies...

Babyagi can be done in ChatGPT with plugin access. Auto-GPT actually performs actions instead of basic web scraping with tasks.

what I find interesting in these applications is "hallucinations" AutoGPT for example, commands have defined arguments, but quite often the model needs prompt reinforcement or it quite often uses incorrect arguments. I wonder if some of these projects overkill the use of the LLM and some functions like sorting or specific arguments to a command could be handled by "dumb" functions.

So glad subscribed to you! Wonderful content!

It will only be useful when people will have full ability to run stuff like that locally. Otherwise sharing personal details with it, is a bad bad idea. @Sam Witteveen, have you managed to run it with LLAMA?

How would one go about interacting with this programmatically, say from a microservice?

any chance to produce similar videos for SuperIGA? a video like this can greatly influence developer's preferences or even decision amoung similar agent sytems such as gptagent vs superiga vs this one (babyiga), due to lack of docs and examples; this channel has explained it so well that BabyIGA becomes my preference as I had many failure attempts with SuperIGA, even though I'm more attracted by the latter's concepts,I couldn't find good info about it, while I lack the ability & time to figure it out.

Do people realise that this is basically the implementation of the theory of the bicameral mind from the 70's? Featured in the 2016 TV show westworld season 1 were robots reach consciousness? It's a theory were one part of the brain which appears to be speaking giving orders, and a second part which listens and obeys. Isn't it funny? AutoGPT also kind of works the same.

I tried to demo this for my wife and of course pinecone was down 😩

Yeah, I wouldn't give it my CC too, as it would max it out in one go! 🤣

Uhm, this is pretty much exactly what auto-gpt does which was released like two weeks ago or so? Including the usage of Pinecone, etc.

"I dont think this is approaching agi in any way" - see you in a few month

I'm not sure Pinecone is needed for this. A Python list would work just as well for semantic search.

Thanks! How does this stack up to AutoGPT? Also, is there a way to use it on a local LLM?

I've been running babyagi over API to the open assistant 13b model, and it does pretty well, I am interested in melding the profession descriptions from CAMEL, or maybe just modifying CAMEL to support offline processing.

Next up, we rewrite AGI to work on Vicuna and make explosive noises in the world! 🤣

How much tokens it used in the end and thus $$? I think openai$$$ loves this kind of initiatives because they will generate extra money with their verbose answers....

Would you expect more from a baby?

I wish people wouldn't say things like "I don't think this a approaching AGI in anyway" without defining it first. By my definition we're already at AGI. It's artificial...it uses tools without instruction and has a feedback loop that let's it improve on itself. I've yet to have anyone tell me how humans are substantially different other than being organic and having an insanely efficient processing system. If you want to say that your idea of AGI is conscious then you need to state that as part of your definition. Otherwise, it just sounds like humans being arrogant about what we are. We're not special and that fact is going to very present soon.

And I'm the second one

First View!

These "AGI"s are over hyped garbage. Anyone who more than five minutes with them sees the novelty wear off. They can't do anything even slightly complex and most of what they actually can do is done more easily, and faster with just straight up chat GPT with the slight advantage that they can (when they don't fail) scape the web for info. But again with chat GPT plugins the same will soon be available to everyone and bing and phind already currently hook GPT up to the internet. It's interesting to see a LLM reasoning through some basic tasks but nothing more.