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Struggling to use Flan-UL2? The flan templates from google show you exactly how to structure your prompts (based on how it was trained) Here are some examples you can follow. First up is generate a news article based on the title

ChatGPT has 100,000,000+ users. But 99% don't know its output formats beyond text. 11 Powerful output formats ChatGPT can produce ( with examples ) 🧵:

Excited to share what I’ve been working on at @openai! github.com/openai/evals is a framework for evaluating OpenAI models and an open-source eval registry. We will be granting GPT-4 access to those who submit high quality evals. Looking forward to your contributions!

Memorizing Transformers extends language models with the ability to memorize the internal representations of past inputs via knn-lookup into an external memory of recent (k, v) pairs, improving performance with memory size of up to 262K tokens arxiv: arxiv.org/abs/2203.08913

Adobe just launched the beta of Firefly, their AI image creator. And it's incredible! - text-to-image - AI 3D modelling - AI Video editing This is the first AI image generator built towards solving creator problems. Here's a breakdown.

2.5 months later...in collaboration with @openAI , ChatGPT gets its "Wolfram superpowers"! writings.stephenwolfram.com/2023/03/chatgp…

we are starting our rollout of ChatGPT plugins. you can install plugins to help with a wide variety of tasks. we are excited to see what developers create! openai.com/blog/chatgpt-p…

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ChatGPT Hackathon Demos w/ @trychoma, @replit: 🔩 ChatGPU - Connect to a GPU + NVIDIA SMI 🪄 PromptWizard - Access top prompts like for games 📊 MLFlow Assist - See/deploy ML Flow experiments 🇺🇸 Political Action - Send custom emails to legislators

do we want to know which variables are direct causes of a target outcome, or the full dependencies among all variables? gradually i started to think that it's probably neither, since the utility of each cause is not a function of the distance to the target outcome variable but

🔗 Thoughts on Research Impact in AI. Grad students often ask: how do I do research that makes a difference in the current, crowded AI space? This is a blogpost that summarizes my perspective in six guidelines for making research impact via open-source artifacts. Link below.

I ran #notebooklm with my recent "Tareted Cause Discovery" paper (arxiv.org/abs/2408.16218). The results are super impressive! It captured the main point of the paper and explained the idea with a nice metaphor.

In 2021, this man quit Google after a fight about his AI chatbot... And vanished overnight. But 3 years later, Google hunted him down and paid $2.7 BILLION to get him back. Here's what he knows that Google can't live without: 🧵

Andrej Karpathy explains what makes Elon Musk unique “I don’t think people appreciate how unique [Elon’s style] is. You read about it, but you don’t understand it—it’s hard to describe.” The first principle Karpathy — who led the computer vision team of Tesla Autopilot — has

BREAKING: MIT just completed the first brain scan study of ChatGPT users & the results are terrifying. Turns out, AI isn't making us more productive. It's making us cognitively bankrupt. Here's what 4 months of data revealed: (hint: we've been measuring productivity all wrong)