Update README.md

README.md

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-# Multi-Domain Expert Layers (MDEL) Training:
+# Multi-Domain Expert Learning (MDEL)**:
 ## How to increase knowledge without breaking the bank?
 
 🍩 Ontocord.AI 🍩 and the open source community.
@@ -18,7 +18,7 @@ Bedrock AI, TurkuNLP, ETH, Redmond.AI, Incite, MICS CentraleSupelec, Centro de E
 
 Open sourcing AI models can lead to increased innovation, accessibility, transparency, and community building. However we need a mechanism to train more capable models in an efficient and modular way.
 
-The proposed method that we call Multi-Domain Expert Layers (MDEL) training for open source language models involves branching from a base model, training each branch independently on a specific domain for specific layers, and merging the trained models at the end. Additionally, the specific layers are kept as experts, with a classifier used as a router to activate the experts during inference. This approach makes it possible to easily increase expertise of a model, to independently train more "adapters", and to reuse previously trained experts and models without retraining, resulting in a modular and efficient system. 
+The proposed method that we call Multi-Domain Expert Learning (MDEL) for open source language models involves branching from a base model, training each branch independently on a specific domain for specific layers or other adapters, and merging the trained models at the end. Additionally, the specific layers or adapters are kept as experts, with a classifier used as a router to activate the experts during inference. This approach makes it possible to easily increase expertise of a model, to independently train more "adapters", and to reuse previously trained experts and models without retraining, resulting in a modular and efficient system. 
 
 In this effort, we seek international labs and open source aligned researchers and companies in various countries to each train a set of domain experts of their choosing, thereby enabling international participation and knowledge sharing. This will also result in lower costs for training and a lower environmental impact due to reuse and lower energy usage. Currently we have volunteers from four continents and are looking for more.
 
@@ -32,3 +32,4 @@ If you are interested in contributing to this project, please reach out to us an
 
 Let's work together to create open-source models that benefit everyone! 🤝 #AI #MDEL #Supercomputers #Summit #OpenSource #Innovation #VolunteersNeeded #OpenScience #DemocratizeAI
 
+** Why did we change the term Layer to Learning? Because we are exploring, in addition layerwise experts, also working with different architecture like Flamingo (https://arxiv.org/abs/2204.14198) and EMU (https://arxiv.org/abs/2307.05222) which will allow us to swap out different modal experts to improve the performance of the model.