1/ A recent controversy at Google has sparked important questions about training ML models on the output of other models. Let's dive into the engineering, business, and legal aspects of this practice. Buckle up, folks! 🧵 2/ Engineering recipes for training algorithms on generated data are still evolving. Instances of using a competitor's model outputs to train your own are surfacing. Are these techniques fair game or should there be limits?

1/ 🚀 #AutoGPT is trending today, but what's the hype all about? Let's unpack these AI game-changers, explore their potential, and recognize their limitations. Thread👇 #HypeVsReality 2/ 🎯 AutoGPTs are AI agents that can perform tasks autonomously, with little to no human intervention. They can even chain multiple GPT-4s together to work on different tasks simultaneously! However, they can get stuck & may need human help. #AutonomousAI #GPT4

🚀 1/ Wanna take #AutoGPT to the next level? Say hello to @dataleapHQ the "Upwork for AI Agents" - a marketplace where you can hire AutoGPTs and other AI agents to get the job done. Buckle down for our vision paper! 🧵 dataleap.substack.com/p/ai-workforce… @dataleapHQ 🙏 2/ Big shoutout to all the brilliant minds moving us closer to his vision:
@hwchase17, @LangChainAI & @gpt_index for the agent toolset , @yoheinakajima for sparking our imagination with #BabyAGI, @ShunyuYao12 for your ReAct thought breakthrough, and @SigGravitas for AutoGPT.

🧵1/ Just attended a 🔥 @LangChainAI webinar on AI agents, ft. some of the brightest minds in the space! Let's unpack the key takeaways & explore the cutting-edge work being done.

Guests:
@charles_irl
@ShunyuYao12
@mbusigin
@yoheinakajima
@hwchase17 @LangChainAI @charles_irl @ShunyuYao12 @mbusigin @yoheinakajima @hwchase17 🧠2/ Shunyu introduced the core idea of his #ReAct paper, which adds a "thought" between Action & Observation. The open question is, do we need a strict pattern of Thought-Action-Observation or should we just add thoughts as a special type of action, offering more flexibility?

🧵 1/ 🤖🧠 Stanford AI researchers have introduced a groundbreaking concept: Generative Agents, computer programs that simulate authentic human behavior using generative models. These agents display memory, introspection, and planning capabilities. Let's dive in. 👇 2/ 🎮 In the study, 25 Generative Agents were placed in a virtual sandbox-like world (think The Sims). They had unique backgrounds and interacted in a 2-day simulation. Examples of emergent behavior: one agent threw a party, another ran for mayor!

🧵 1/ 📈 Yesterday we talked about how important chunking is when using vector databases like @pinecone, @weaviate_io or @trychroma. But what exactly are vector databases in the first place? Let's explore this game-changer! 👇 @pinecone @weaviate_io @trychroma 2/ 🤖 Machine Learning (ML) techniques can transform complex data into vector embeddings, describing data objects as numeric values in multiple dimensions. Vector databases index these embeddings for easy search & retrieval, finding similar values. 🧠

🧵 1/ Vector stores & embeddings may be the talk of the town, but there's more to explore! Discover how to fine-tune LLaMA, an open-source language model, to make it sound like Homer Simpson! You can even apply this method to other characters! 🍩📺 Shout out to @bfirsh for this! @bfirsh 2/ The process starts by using a dataset containing scripts from The Simpsons TV show (Seasons 1-12) obtained from Kaggle. With ~60k lines of dialog and 1.1M tokens, it's time to train LLaMA to reproduce the voice of the characters. 📚

🧵 1/ In the world of LLM-related applications, chunking is crucial to improve efficiency and accuracy. Our friends at @pinecone explored various methods and considerations for choosing the best chunk size for your app. A Thread👇 @pinecone 2/ When indexing content in Pinecone, it must be embedded first. The goal of chunking is to minimize noise while maintaining semantic relevance. Finding the optimal chunk size is essential for accurate and relevant search results in semantic search or conversational agents.

1/ Finance nerds, rejoice! Introducing BloombergGPT, a specialized AI agent built to navigate the exciting world of finance. Imagine having a powerful genie-like ChatGPT, but solely focused on finance. Let's dive into what this AI can do for you. #BloombergGPT 🧵 2/ First, let's understand the "why" behind Bloomberg, a media company, launching a Finance GPT. Bloomberg is more than just news; it's a financial, software, data, and media powerhouse with the Bloomberg Terminal as its cornerstone. It lives and breathes finance! #Bloomberg #AI

1/ Have you heard about #HuggingGPT? It's a specialized AI agent that connects ChatGPT with Hugging Face's vast library of models to tackle complex AI tasks across multiple modalities like language, vision, and voice. Let's explore this revolutionary approach! 🧵 2/ LLMs like ChatGPT have shown great potential in NLP tasks. To fully realize this potential, LLMs need to collaborate with other AI models. The key lies in using language as a generic interface to link various AI models, making LLMs the "central nervous system."

The future of AI: One Super AI Agent or a Symphony of Specialized Agents? 🤖 As we enter a new era of autonomous agents thanks to @yoheinakajima, @SigGravitas, and @LangChainAI, the debate heats up. Let's dive in! 🧵 #GPT @yoheinakajima @SigGravitas @LangChainAI 2/ The Super AI Agent Hypothesis posits a single, all-powerful AI capable of handling any task. Its primary advantage is simplicity, as users only need to interact with one AI agent, avoiding the need to manage multiple tools or interfaces.