Agentville Academy.txt

1.Next-gen Digital Helpers
Autonomous agents are the future of technology. They're intelligent, adaptable, and can learn from their experiences. Imagine having a digital assistant that anticipates your needs, simplifies complex tasks, and helps you achieve your goals faster.
Link: https://www.youtube.com/watch?v=fqVLjtvWgq8
2.How to improve multi-agent interactions?
The biggest challenge in the world of autonomous agents is to improve the quality of agents' performance over time. From MetaGPT to AutoGen, every researcher is trying to obviate this challenge.

In this video, Negar Mehr, assistant professor of aerospace engineering at UIUC, discusses the challenges of enabling safe and intelligent multi-agent interactions in autonomous systems.
Watch the video to understand the connection between the movies Beautiful Mind, Cinderella and Autonomous Agents!
Link: https://www.youtube.com/watch?v=G3JoGvZABoE&t=2426s
3.Survey of Autonomous Agents
Autonomous agents are the future of technology. They're intelligent, adaptable, and can learn from their experiences. Imagine having a digital assistant that anticipates your needs, simplifies complex tasks, and helps you achieve your goals faster.
Link: https://arxiv.org/abs/2308.11432v1
4.🧐 Can the Machines really think?
In this video, Feynman argues that while machines are better than humans in many things like arithmetic, problem-solving, and processing large amounts of data, machines will never achieve human-like thinking and intelligence. They would infact be smart and intelligent in their own ways and accomplish more complicated tasks than a human.
Link: https://www.youtube.com/watch?v=ipRvjS7q1DI
5. Six Must-Know Autonomous AI Agents
These new Autonomous AI Agents Automate and Optimize Workflows like never before
Most LLM-based multi-agent systems have been pretty good at handling simple tasks with predefined agents. But guess what? AutoAgents has taken it up a notch! 🚀
It dynamically generates and coordinates specialized agents, building an AI dream team tailored to various tasks. It's like having a squad of task-specific experts collaborating seamlessly.! 🏆🌐🔍
Link: https://huggingface.co/spaces/LinkSoul/AutoAgents
6.AI Agent Landscape: Overview 🌐
If you're as intrigued by the world of AI Agents as we are, you're in for a treat! Delve into e2b.dev's meticulously curated list of AI Agents, showcasing a diverse array of projects that includes both open-source and proprietary innovations. From AutoGPT to the latest AutoGen, the list covers all the latest and greatest from the world of autonomous agents!
All the agents are organized based on the tasks they excel at. How many of these have you explored?
Link: https://github.com/e2b-dev/awesome-ai-agents
7.MemGPT: LLM as operating system with memory
Ever wished AI could remember and adapt like humans? MemGPT turns that dream into reality! It's like a memory upgrade for language models. Dive into unbounded context with MemGPT and reshape the way we interact with AI.
This is a groundbreaking release from the creators of Gorilla! ✨
Link: https://memgpt.ai/
8.OpenAgents: AI Agents Work Freely To Create Software, Web Browse, Play with Plugins, & More!
A game-changing platform that's reshaping the way language agents work in the real world.
Unlike its counterparts, OpenAgents offers a fresh perspective. It caters to non-expert users, granting them access to a variety of language agents and emphasizing application-level designs. This powerhouse allows you to analyze data, call plugins, and take command of your browser—providing functionalities akin to ChatGPT Plus.
Link: https://youtu.be/htla3FzJTfg?si=_Nx5sIWftR4PPjbT
9.Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Using strong LLMs as judges to evaluate LLM models on open-ended questions.
Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, this paper explores using strong LLMs as judges to evaluate these models on more open-ended questions. 
Link: https://arxiv.org/abs/2306.05685
10.LLM Agents: When Large Language Models Do Stuff For You
These new Autonomous AI Agents Automate and Optimize Workflows like never before
We now have an idea of what LLM agents are, but how exactly do we go from LLM to an LLM agent? To do this, LLMs need two key tweaks.
First, LLM agents need a form of memory that extends their limited context window to “reflect” on past actions to guide future efforts. Next, the LLM needs to be able to do more than yammer on all day.
Link: https://deepgram.com/learn/llm-agents-when-language-models-do-stuff-for-you
11.The Growth Behind LLM-based Autonomous Agents
In the space of 2 years, LLMs have achieved notable successes, showing the wider public that AI applications have the potential to attain human-like intelligence. Comprehensive training datasets and a substantial number of model parameters work hand in hand in order to attain this.
Read this report for a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
Link: https://www.kdnuggets.com/the-growth-behind-llmbased-autonomous-agents
12.AI Agents: Limits & Solutions
The world is buzzing with excitement about autonomous agents and all the fantastic things they can accomplish.
But let's get real - they do have their limitations. What's on the "cannot do" list? How do we tackle these challenges?
In a captivating talk by Silen Naihin, the mastermind behind AutoGPT, we dive deep into these limitations and the strategies to conquer them. And guess what? Agentville is already in action, implementing some of these cutting-edge techniques!
Link: https://www.youtube.com/watch?v=3uAC0CYuDHg&list=PLmqn83GIhSInDdRKef6STtF9nb2H9eiY6&index=79&t=55s
13.Multi-Agent system that combines LLM with DevOps
Meet DevOpsGPT: A Multi-Agent System that Combines LLM with DevOps Tools 
DevOpsGPT can transform requirements expressed in natural language into functional software using this novel approach, boosting efficiency, decreasing cycle time, and reducing communication expenses.
Link: https://www.marktechpost.com/2023/08/30/meet-devopsgpt-a-multi-agent-system-that-combines-llm-with-devops-tools-to-convert-natural-language-requirements-into-working-software/
14.Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration
Explore a novel multi-agent collaboration strategy that emulates the academic peer review process where each agent independently constructs its own solution, provides reviews on the solutions of others, and assigns confidence levels to its reviews.
Link: https://arxiv.org/pdf/2310.03903.pdf
15.Theory of Mind for Multi-Agent Collaboration via Large Language Models
This study evaluates LLM-based agents in a multi-agent cooperative text game with Theory of Mind (ToM) inference tasks, comparing their performance with Multi-Agent Reinforcement Learning (MARL) and planning-based baselines.
Link: https://arxiv.org/pdf/2310.10701.pdf
16.Multi-AI collaboration helps reasoning and factual accuracy in large language models
Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.
Link: https://news.mit.edu/2023/multi-ai-collaboration-helps-reasoning-factual-accuracy-language-models-0918
17.The impact of LLMs on marketplaces
LLMs and generative AI stand to be the next platform shift, enabling us to both interpret data and generate new content with unprecedented ease.
Over time, one could imagine that buyers may be able to specify their preferences in natural language with an agent that infers the parameters and their weights. This bot would then run the negotiation with the supply side (or their own bots, which would rely on their own parameters such as available supply, minimum margin, and time-to-end-of-season) and bid on their behalf.
Link: https://www.mosaicventures.com/patterns/the-impact-of-llms-on-marketplaces
18.MAgIC: Benchmarking LLM Powered Multi-Agents in Cognition, Adaptability, Rationality and Collaboration
In response to the growing use of Large Language Models in multi-agent environments, researchers at Stanford, NUS, ByteDance and Berkely, came up with a unique benchmarking framework named MAg. Tailored for assessing LLMs, it offers quantitative metrics across judgment, reasoning, collaboration, and more using diverse scenarios and games.
Link: https://arxiv.org/pdf/2311.08562.pdf
19.OpenAI launches customizable ChatGPT versions (GPTs) with a future GPT Store for sharing and categorization.
OpenAI has introduced a new feature called GPTs, enabling users to create and customize their own versions of ChatGPT for specific tasks or purposes. GPTs provide a versatile solution, allowing individuals to tailor AI capabilities, such as learning board game rules, teaching math, or designing stickers, to meet their specific needs.
Link: https://openai.com/blog/introducing-gpts


20.GPTs are just the beginning. Here Come Autonomous Agents
Generative AI has reshaped business dynamics. As we face a perpetual revolution, autonomous agents—adding limbs to the powerful brains of LLMs—are set to transform workflows. Companies must strategically prepare for this automation leap by redefining their architecture and workforce readiness.
Link: https://www.bcg.com/publications/2023/gpt-was-only-the-beginning-autonomous-agents-are-coming


21.Prompt Injection: Achilles heel of Autonomous Agents
Recent research in the world of LLMs highlight a concerning vulnerability: the potential hijacking of autonomous agents through prompt injection attacks. This article delves into the security risks unveiled, showcasing the gravity of prompt injection attacks on emerging autonomous AI agents and the implications for enterprises integrating these advanced technologies. 
Link: https://venturebeat.com/security/how-prompt-injection-can-hijack-autonomous-ai-agents-like-auto-gpt/