Discover and read the best of Twitter Threads about #AIResearch
Most recents (5)
🔍📚 luci e ombre nell'uso attuale delle #AI nella ricerca bibliografica. Proviamo a leggere insieme i risultati ottenuti con #autoGPT, #GPT4 con plugin browsing attivo e #Bing A seguire i dettagli! 🤖💡 1/n #AIResearch #Bibliography 
Proviamo a leggere la risposta di #AutoGPT a questa richiesta: 🔍📚"Find evidence about the use of bubble test combined with transtemporal ecodoppler in the diagnosis of patent foramen ovale (PFO) and summarize your results in a table" 2/n #AIResearch
dropbox.com/s/znf0f2nfhnve…
dropbox.com/s/znf0f2nfhnve…
Proviamo a leggere i risultati di #Bing alla stessa ricerca, con una avvertenza preliminare: il salvataggio è più laborioso, Bing fornisce link attivi soltanto su microsoft_edge e questi link sono di durata incerta, è necessario sempre salvare i risultati anche in altro modo. 3/n
If you think ChatGPT is revolutionary, brace yourself for AutoGPT.
This groundbreaking AI technology is taking artificial intelligence to new heights with real-life examples and scenarios.
Follow along for mind-blowing insights! #AutoGPT #AIRevolution
This groundbreaking AI technology is taking artificial intelligence to new heights with real-life examples and scenarios.
Follow along for mind-blowing insights! #AutoGPT #AIRevolution
1. AutoGPT, aka BabyAGI, is a powerful AI language model built on the GPT-4 architecture.
It's designed to perform a wide range of tasks with human-like understanding, enabling it to adapt and generalize across various domains. #NextGenAI #ArtificialGeneralIntelligence
It's designed to perform a wide range of tasks with human-like understanding, enabling it to adapt and generalize across various domains. #NextGenAI #ArtificialGeneralIntelligence
2. One-Shot Learning: AutoGPT can quickly learn new concepts and tasks with minimal input.
For example, it can understand a user's request to create a personalized workout plan after analyzing just a few key parameters like fitness level, goals, and available equipment.
For example, it can understand a user's request to create a personalized workout plan after analyzing just a few key parameters like fitness level, goals, and available equipment.
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Introducing "🤖 Task-driven Autonomous Agent"
An agent that leverages @OpenAI's GPT-4, @pinecone vector search, and @LangChainAI framework to autonomously create and perform tasks based on an objective.
"Paper": yoheinakajima.com/task-driven-au…
[More 🔽]
Introducing "🤖 Task-driven Autonomous Agent"
An agent that leverages @OpenAI's GPT-4, @pinecone vector search, and @LangChainAI framework to autonomously create and perform tasks based on an objective.
"Paper": yoheinakajima.com/task-driven-au…
[More 🔽]
🚀2/8 The system can complete tasks, generate new tasks based on results, and prioritize tasks in real-time. It demonstrates the potential of AI-powered language models to autonomously perform tasks within various constraints and contexts.
💡3/8 The autonomous agent uses GPT-4 for task completion, Pinecone for efficient search and storage of task-related data, and the LangChain framework to enhance decision-making processes. #GPT4 #Pinecone #LangChain
🧩Three intriguing concepts in the world of #DeepLearning: #BackwardCompatibility, #StationaryRepresentation, and #NeuralCollapse. Their connection may hold the key to better deep learning models with possibly novel ways to use them
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🤝Backward Compatibility enables matching internal feature representations from different neural networks. Stationary Representation maintains feature spatial configurations fixed during learning. How does Neural Collapse fit in?
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💥Neural Collapse is when feature representations converge towards a simpler structure during training. This fascinating phenomenon is strongly related to compatibility and stationary representation.
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The size of #NLP models have increased enormously, growing to millions, or even billions, of parameters, along with a significant increase in the financial cost and carbon emissions.
The cost associated with training large models limits the #AIresearch community's ability to innovate, because a research project often needs a lot of experimentation.
Consider training a top-performing #LanguageModel on the Billion Word benchmark. A single experiment would take 384 GPU days (6 days*64 V100 #GPUs, or as much as $36,000 using AWS on-demand instances)
