💡 **The Future of AI and Content Creation!** 🤖✨

With tools like **NotebookLM** making it SO easy to create content, the amount of info online is exploding! 🌐🎙️ But keeping up with it all is becoming impossible...

To stay ahead, we'll need **AI agents** to consume content FOR us! 🔍 These agents could filter out the junk, fact-check the rest, and format it in a way that fits our own learning styles. 📚🎧

🎥 **Video podcasts** will be the next big thing, and having an AI to sift through it all will be a game-changer! 🚀

#AI #ContentCreation #LearningAgents #NotebookLM #FutureTech #PersonalizedLearning #AIRevolution

Through scaling #DeepNeuralNetworks we have found in two different domains, #ReinforcementLearning and #LanguageModels, that these models learn to learn (#MetaLearning).

They spontaneously learn internal models with memory and learning capability which are able to exhibit #InContextLearning much faster and much more effectively than any of our standard #backpropagation based deep neural networks can.

These rather alien #LearningModels embedded inside the deep learning models are emulated by #neuron layers, but aren't necessarily deep learning models themselves.

I believe it is possible to extract these internal models which have learned to learn, out of the scaled up #DeepLearning #substrate they run on, and run them natively and directly on #hardware.

This allows those much more efficient learning models to be used either as #LearningAgents themselves, or as a further substrate for further meta-learning.

I have an #embodiment #research on-going but with a related goal and focus specifically in extracting (or distilling) the models out of the meta-models here:
https://github.com/keskival/embodied-emulated-personas

It is of course an open research problem how to do this, but I have a lot of ideas!

If you're inspired by this, or if you think the same, let's chat!