This is our regular bi-weekly meeting open to anyone interested in Artificial Intelligence or Machine Learning, regardless of experience or background. The point is to meet others interested in the area and potentially form collaborations or study groups. For those new to AI/ML, please identify yourself so you can connect with others with similar interests.
We'll continue the format as the previous meetups - introductions, couple of short presentations, open discussions on topics of interest.
We'll have a projector, so if you have a presentation of what you're working on please bring your laptop. (The projector takes HDMI hookup.) Let me know if you are interested and please post presentation to meetup or slack channel prior to the meetup.
A couple of logistic items: (1) there is free parking in the building; (2) we'll meet in the EAST room (Room B) on 2nd floor; (3) there will be coffee too!
A big thank you to our sponsor, Miner and Kasch, for covering the costs of this event!
As always, suggestions are most welcome!
NLP on the edge - An example of Hybrid AI
by Antonio LINARI
I will show how it is possible to link together knowledge graphs' based pure disambiguation and deep learning ... and run this hybrid AI on a small device (a Jetson Nano)
Machine Learning from Healthcare Payers' Perspective: Key Use Cases, Challenges, Trends, Cool Techniques and Tremendous Opportunities
by Changrong Ji
Changrong Ji is an enterprise architect at CareFirst BlueCross BlueShield where she leads Machine Learning explorations, and collaborates with peers within the national BlueCross BlueShield network to further AI adoption in healthcare. She will give a brief overview of Machine Learning from Healthcare Payer's perspective:
1. Key use cases and activities in areas such as Care Management, Claims Processing, Actuary, Marketing and Customer Service
2. Challenges: data privacy, interoperability, etc.
3. Trends and opportunities, including a few cool and highly promising areas of early stage applied R+D: Deep Transfer Learning with claims and Electronic Medical Records, Federated Learning to alleviate data privacy concerns