AI & ML interests

AGI and ML Pipelines, Ambient IoT AI, Behavior Cognitive and Memory AI, Clinical Medical and Nursing AI, Genomics AI, GAN Gaming GAIL AR VR XR and Simulation AI, Graph Ontology KR KE AI, Languages and NLP AI, Quantum Compute GPU TPU NPU AI, Vision Image Document and Audio/Video AI


Pre-requisites

One of the best platforms in 2022 for open source AI development and demonstration is "HuggingFace Spaces".

Spaces supports a model hub, an inference API, github and container turn key integration, and an ability to create and freely host new programs for world wide communities reducing the pain and difficulty in setting up environments for AI.

HuggingFace is an open source implementation of an AI platform which supports three main SDK's used within AI and NLP apps which are HTML5, Gradio, and Streamlit.  

As a pre-requisite you will need to create an account for yourself at HuggingFace (https://huggingface.co/). Next join the classroom organization called "AINLPRoundTable".

Intended audience:

This AI NLP round table class is for anyone with basic computing skills of all ages and backgrounds to be able to set up a space for themselves where they can create, test and demonstrate AI and NLP programs to anyone on the internet as open source.  Prior knowledge and interest of development of AI programs is recommended but not required so this audience can include people interested and new to AI.

** AI and NLP Products **

This classroom follows three product design tenets:

  1. Describe the "Pain" customer is facing with problem you plan to solve.
  2. Describe the "Joy" of what changes for the customer because of your product. And finally,
  3. If we exceed all expectations, Describe how we give the customer a new "Superpower".

As a "press release" for products be able to answer these to describe your goals to document product delivery.

Intent/Outcome of the Classroom: The intent of this HF Organization and this Classroom session is to enable all attendees to create AI and NLP programs in record time using Spaces, HTML5, Gradio, Streamlit, and Open Source.  

By the end of this session attendees will be able to easily create new AI and NLP demos of their own to host and share including UI, ML models, user input and interaction, dataset load, save, transform and search. The goal is to achieve proficience in using AI and NLP software development kits and libraries by sharing in an open source environment.

Pre-requisites: The preferred platform in 2022 for open source community AI development and demonstration is "HuggingFace Spaces". Spaces supports a model hub, an inference API, github action integration, and ability to create and freely host new programs for world wide communities. HuggingFace is an open source implementation of an AI platform which supports three main SDK's used within AI and NLP apps which are HTML5, Gradio, and Streamlit.  As a pre-requisite you will need to create an account for yourself at HuggingFace (https://huggingface.co/). Next join the classroom organization called "AINLPRoundTable".  

Intended audience: This AI NLP round table class is for anyone with basic computing skills of all ages and backgrounds to be able to set up a space for themselves where they can create, test and demonstrate AI and NLP programs to anyone on the internet as open source.  Prior knowledge and interest of development of AI programs is recommended but not required so this audience can include people interested and new to AI.

Democratize AI and NLP to Give Customers Superpowers This classroom follows three easy to remember customer focused product design tenets:

  1. Be able to describe easily the "Pain" customer is facing with problem you plan to solve.
  2. Be able to describe the "Joy" of what has changed for the customer because of your product. And finally,
  3. If we exceeded all expectations, we gave the customer a new "Superpower".

As a "press release" for your product be able to answer these and discuss your product ideas for AI and NLP and how we can help. We do these press releases informally in a trusted space using short form video to document product delivery.

models

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datasets

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