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
3/ Bloomberg has amassed a goldmine of financial data over the years. And what better way to utilize this data than to train a large language model to run generative AI chatbots? That's how BloombergGPT, trained on 50 billion parameters, came into existence.
4/ So, what magic can BloombergGPT create? Here are its claimed capabilities:
Sentiment Analysis
Named Entity Recognition
News Classification
Question Answering
These features could revolutionize trading and finance, empowering traders and facilitating learning.
5/ Before you get too excited, remember that BloombergGPT's capabilities are still claims by Bloomberg. The real-world application is yet to be tested, but the potential is immense. #FutureofFinance
6/ One thing is certain: Niche AIs are on the rise. From digital art to finance and beyond, AI is growing rapidly, expanding into various domains. The age of specialized AI agents is here, and BloombergGPT is a prime example of this exciting trend. #NicheAI
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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?
3/ Business-wise, data may not always make your business more defensible. Market leaders might spend resources gathering data, but if their product's data makes it easier for competitors to catch up, is that initial effort a strong defense?
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
3/ 🌟 Two main models dominate the AutoGPT landscape: BabyAGI by @yoheinakajima & AutoGPT by @SigGravitas. They're trending on GitHub and attracting devs worldwide. Don't forget @asimdotshrestha's AgentGPT, which runs in-browser!
🚀 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@hwchase17@LangChainAI@gpt_index@yoheinakajima@ShunyuYao12@SigGravitas 🤖 3/ The gig economy has traditionally been the domain of human freelancers, but at Dataleap we are looking to create a new era where AI agents and humans coexist, each leveraging their unique strengths. Sometimes they'll complement each other; other times, it's all AI.
🧵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.
@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!
3/ 📊 Here's the kicker: actual humans role-playing the same 25 agents generated responses that were rated as less human-like than the chatbot-powered agents by an evaluation panel. Generative Agents are getting closer and closer to authentic human behavior. 😮
🧵 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. 🧠
@pinecone@weaviate_io@trychroma 3/ 🔍 Vector databases excel at similarity search (vector search), allowing users to find related results without knowing specific keywords or metadata classifications. This provides accurate results while eliminating irrelevant ones that traditional search tech might return.