coauthor
Voyager
coauthor | Voyager | |
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1 | 53 | |
117 | 5,152 | |
- | 1.5% | |
1.8 | 4.7 | |
over 1 year ago | 29 days ago | |
JavaScript | JavaScript | |
- | MIT License |
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coauthor
Voyager
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Google Launches Gemini, Its "Most Powerful" AI Model to Date
Source: Conversation with Bing, 12/10/2023 (1) Wes Roth - YouTube. https://www.youtube.com/@WesRoth. (2) I've set most of my videos to Public again - Community. https://community.openai.com/t/ive-set-most-of-my-videos-to-public-again/24535. (3) AI Updates: Meta Develops Mind-Reading AI System, OpenAI’s Q* Is Here .... https://www.windermeresun.com/2023/11/20/ai-updates-meta-develops-mind-reading-ai-system-openais-q-is-here-how-economy-will-work-after-agi/. (4) David Shapiro. https://www.daveshap.io/. (5) undefined. https://natural20.com/. (6) undefined. https://arxiv.org/abs/2305.16291. (7) undefined. https://twitter.com/DrJimFan/status/1. (8) undefined. https://voyager.minedojo.org/. (9) undefined. https://minedojo.org/. (10) undefined. https://www.youtube.com/@DavidShapiroAutomator/videos.
- Is there any game that allow us to interact with it by python?
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A Coder Considers the Waning Days of the Craft
> AI cannot sustain itself trained on AI work.
This isn’t true. You can train LLMs entirely on synthetic data and get strong results. [0]
> If new languages, engines etc pop up it cannot synthesize new forms of coding without that code having existed in the first place.
You can describe the semantics to a LLM, have it generate code, tell it what went wrong (i.e. with compiler feedback), and then train on that. For an example of this workflow in a different context, see [1].
> And most importantly, it cannot fundamentally rationalize about what code does or how it functions.
Most competent LLMs can trivially describe what some code does and speculate on the reasoning behind it.
I don’t disagree that they’re flawed and imperfect, but I also do not think this is an unassailable state of affairs. They’re only going to get better from here.
[0]: https://arxiv.org/abs/2309.05463
[1]: https://voyager.minedojo.org/
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AutoGen: Enable Next-Gen Large Language Model Applications
In a way it is the same thing, agents are mostly an abstraction that make it easier to know what’s going on.
I think of agents more or less as python classes with a mixture of natural language and code functions. You design them to do something with information they produce, and to interface with other agents or “tools” in some way.
But all the agents can be the same language model under the hood, they are frames used to build different kinds of contexts.
And yes I think the idea is that emergent behaviour can be useful. This comes to mind
https://github.com/MineDojo/Voyager
But I think we are still a small ways off from being really smart about agents. My opinion is that we haven’t quite figured out what we are doing yet.
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Open/Local LLM support for MineDojo/Voyager
This k8s application deploys an instance of Voyager along with a Fabric Minecraft server with required fabric mods. It assumes you have a local deployment of a Large Language Model (LLM) with 4K-8K token context length with a compatible OpenAI API, including embeddings support.
- Voyager – Minecraft Embodied Agent with Large Language Models
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List of Awesome AI Agents like AutoGPT and BabyAGI / Many open-source Agents with code included!
In my opinion the most interesting Agents: Auto-GPT Github: https://github.com/Significant-Gravitas/Auto-GPT BabyAGI Github: https://github.com/yoheinakajima/babyagi Voyager Github: https://github.com/MineDojo/Voyager / Paper: https://arxiv.org/abs/2305.16291 I would also add: ChemCrow: Augmenting large-language models with chemistry tools Github: https://github.com/ur-whitelab/chemcrow-public/ Paper: https://arxiv.org/abs/2304.05376
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[D] - Are there any AI benchmarks that involve successful longterm problem solving when running as autonomous agents (like in autogpt)? How do we compare the effectiveness of models as agents?
Does this beat the voyager? I read about it and wondered what if we add a skill library to langchain/llamaindex agents. It could be the same vector store for storing static data but after each task is performed, the agent will evaluate and archive the recipe of steps to perform a new task. Next time when the agent is asked to perform a task, it can just look at the library to retrieve a recipe. Unlike traditional fine tuning, you dont update the model parameters, these recipes are much more interpretable and can be manually edited/inserted by humans. There may also be an automatic way to convert wikihow articles or youtube tutorials into recipes.
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GPT-4 was set free in Minecraft, here's what happened next...
Source. P.S. If you love geeking over AI updates, I have this free newsletter you might want to check out. Thank you!
Source.
What are some alternatives?
purple-pi - :purple_heart: LaTeX math wherever you want
GITM - Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory
Mine-ChatGPT - OpenSourced ChatGPT downloader in markdown format. Download all text or markdown-styled code blocks Fear no more that servers are down, under high load or OpenAI adding a new feature. Keep all yours chat's with you locally in the simple .md files.
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
NativeOverleaf - Next-level academia! Repository for the Native Overleaf project, attempting to integrate Overleaf with native OS features for macOS, Linux and Windows.
mineflayer - Create Minecraft bots with a powerful, stable, and high level JavaScript API.
Tweet-Emotion
llm-awq - AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
overleaf-relative-line-numbers - A basic chrome extension that changes the line numbering when using Overleaf
gorilla - Gorilla: An API store for LLMs
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
qlora - QLoRA: Efficient Finetuning of Quantized LLMs