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Update README.md

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@@ -4,40 +4,46 @@ emoji: 🐠🔬🧠
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  colorFrom: gray
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  colorTo: blue
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  sdk: streamlit
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- sdk_version: 1.34.0
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  app_file: app.py
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  pinned: false
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  license: mit
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  ---
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- GPT-4o Documentation: https://cookbook.openai.com/examples/gpt4o/introduction_to_gpt4o
 
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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-
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- This experimental multi agent mixture of expert system uses a variety of techniques and models to create different combinatorial AI solutions.
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  Models Used:
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- Mistral-7B-Instruct
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- Llama2-7B
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- Mixtral-8x7B-Instruct
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- Google Gemma-7B
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- OpenAI Whisper Small En
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- OpenAI GPT-4o, Whisper-1
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- ArXiV Embeddings
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- The techniques below which are not ML models but AI include:
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- Speech Synthesis using browser technology
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- Memory for semantic facts, and episodic emotional and event time series memories
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- Web integration using the q= standard for search linking allowing comparison of tech giant AI implementations:
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- Bing then Bing copilot with click 2
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- Google which does an AI search now
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- Twitter, the new home for technology discoveries, AI Output and Grok
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- Wikipedia for fact checking
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- YouTube
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- File and metadata integration combining text, audio, image, and video
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- This app also merges common theories in cognitive AI, AI with python libraries (e.g. NLTK, SKLearn).
 
 
 
 
 
 
 
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  The intent is to demonstrate SOTA AI/ML and combinations of Function-Input-Output for interoperability and knowledge management.
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  st.rerun()
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  if __name__ == "__main__":
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- main()
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  colorFrom: gray
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  colorTo: blue
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  sdk: streamlit
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+ sdk_version: 1.38.0
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  app_file: app.py
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  pinned: false
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  license: mit
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  ---
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+ # GPT-4o Documentation:
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+ https://cookbook.openai.com/examples/gpt4o/introduction_to_gpt4o
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+ This multi-agent mixture of expert system uses a variety of techniques and multiple model flows to create combinatorial AI solutions.
 
 
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  Models Used:
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+ 1. Mistral-7B-Instruct
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+ 2. Llama2-7B
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+ 3. Mixtral-8x7B-Instruct
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+ 4. Google Gemma-7B
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+ 5. OpenAI Whisper Small En
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+ 6. OpenAI GPT-4o, Whisper-1
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+ 7. ArXiV Embeddings
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+
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+
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+ The AI techniques used aare below:
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+
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+ 1. Speech Synthesis using browser technology
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+ 2. Memory for semantic facts, and episodic emotional and event time series memories
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+ 3. Web integration using the q= standard for search linking allowing comparison of tech giant AI implementations:
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+ 4. Bing then Bing copilot with click 2
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+ 5. Google which does an AI search now
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+ 6. Twitter, the new home for technology discoveries, AI Output and Grok
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+ 7. Wikipedia for fact checking
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+ 8. YouTube
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+
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+ Features:
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+
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+ 1. The app manages these as direct web links to the search and AI engines using q= request parameters like most search engines.
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+ 2. This allows deep link bookmarks that bring you back to inputs and outputs by type of model.
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+ 3. File and metadata integration combining text, audio, image, and video
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+ 4. This app also merges common theories in cognitive AI, AI with python libraries (e.g. NLTK, SKLearn).
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  The intent is to demonstrate SOTA AI/ML and combinations of Function-Input-Output for interoperability and knowledge management.
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  st.rerun()
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  if __name__ == "__main__":
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+ main()