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@unknown
Apr 13 โ€ข 9 tweets โ€ข 8 min read Twitter logo Read on Twitter
1/ ๐Ÿš€ #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. Thread๐Ÿ‘‡ #HypeVsReality
2/ ๐ŸŽฏ 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/ ๐ŸŒŸ 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!
@yoheinakajima @SigGravitas @asimdotshrestha 4/ ๐Ÿ› ๏ธ AutoGPTs offer 4 primary features: assigning goals/tasks to work on autonomously, chaining multiple GPT-4s together, internet access for reading/writing files, and memory to track progress. These features make them valuable for businesses & devs.
@yoheinakajima @SigGravitas @asimdotshrestha 5/ ๐Ÿšง Despite their potential, AutoGPTs face limitations, like confusion with ambiguous instructions and distractions when tackling new tasks. But devs are working hard to improve these models daily.
@yoheinakajima @SigGravitas @asimdotshrestha 6/ ๐ŸŒ AutoGPTs could revolutionize industries like customer support, automating tasks and allowing employees to focus on higher-order jobs. Just imagine 2.8 million customer service jobs disrupted by AI agents like AutoGPTs, as @gregisenberg suggests! #FutureOfWork
@yoheinakajima @SigGravitas @asimdotshrestha @gregisenberg 7/ I personally believe that autonomous AI agents will be a complete game changer for the future of work. That's why decided to build an Upwork for AI agents.
@yoheinakajima @SigGravitas @asimdotshrestha @gregisenberg 7/ ๐Ÿ’ก The unexpected emergence of AutoGPTs underscores the importance of research for identifying innovative solutions. As AI models continue to evolve, devs are more hopeful than ever for game-changing breakthroughs.
@yoheinakajima @SigGravitas @asimdotshrestha @gregisenberg 8/ ๐Ÿ“š Continuous learning is crucial to staying on top of AI advancements like AutoGPTs. As AI models develop and change, we must adapt our understanding of their capabilities, limitations, and potential use cases. Follow me for more AI updates.

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Twitter author Profile picture
Apr 13
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! ๐Ÿงต
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?
Read 6 tweets
Twitter author Profile picture
Apr 12
๐Ÿš€ 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! ๐Ÿงต dataleap.substack.com/p/ai-workforceโ€ฆ
@dataleapHQ ๐Ÿ™ 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.
@dataleapHQ @hwchase17 @LangChainAI @gpt_index @yoheinakajima @ShunyuYao12 @SigGravitas ๐Ÿค– 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.
Read 9 tweets
Twitter author Profile picture
Apr 12
๐Ÿงต1/ Just attended a ๐Ÿ”ฅ @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 ๐Ÿง 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?
@LangChainAI @charles_irl @ShunyuYao12 @mbusigin @yoheinakajima @hwchase17 ๐Ÿค–3/ Yohei built babyAGI, inspired by #HustleGPT. He used GPT-4 to create an autonomous Founder Bot, even penning a "scientific paper" about it. BabyAGI focuses on completing, generating, and prioritizing tasks. #BabyAGI
Read 16 tweets
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Apr 12
๐Ÿงต 1/ ๐Ÿค–๐Ÿง  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. ๐Ÿ‘‡
2/ ๐ŸŽฎ 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/ ๐Ÿ“Š 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. ๐Ÿ˜ฎ
Read 10 tweets
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Apr 12
๐Ÿงต 1/ ๐Ÿ“ˆ 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! ๐Ÿ‘‡
@pinecone @weaviate_io @trychroma 2/ ๐Ÿค– 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. ๐Ÿง 
@pinecone @weaviate_io @trychroma 3/ ๐Ÿ” 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.
Read 8 tweets
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Apr 12
๐Ÿงต 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! ๐Ÿฉ๐Ÿ“บ Shout out to @bfirsh for this! Image
@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. ๐Ÿ“š
@bfirsh 3/ To make LLaMA speak like a character, the dataset is parsed into scenes and prompts are generated. This is done by taking the previous lines in the scene, the character with the next line, and that line. The model is then prompted to complete the line in context. ๐ŸŽญ
Read 7 tweets

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