What we're about

Visit the group: http://nyhais.org

Join our Slack channel: https://bit.ly/NYHAIS-Slack

Join our Facebook group: https://www.facebook.com/groups/NYHAIS

Join us on LinkedIn: https://www.linkedin.com/company/nyhais/

Members are encouraged to abide by the Asilomar principles of artificial intelligence: https://futureoflife.org/ai-principles/

Interested in AI? Join our group! This group is dedicated to the application of neuroscience, artificial intelligence, medicine, and computer science towards the further understanding and development of artificial intelligence. We also focus on developing the New York MedTech industry and fostering open source projects that support it. Contact the organizers about it.

The goal is to create a laid-back place where the spirit of the coffeehouse culture of the Enlightenment meets the modern hackerspace; where people from different disciplines who wouldn't normally meet can exchange ideas, experiment with hands-on systems, and have a good time.

We hope this can be the melting pot of neuroscientists, hackers, programmers, doctors, mathematicians, artists, lawyers, post docs in any science, bio-staticians, entrepreneurs, and the average person who is interested in learning about advances in artificial intelligence applications in health (and adjacent data-intensive fields).

Check out our new List of All NYC AI Health Companies:

Feel free to add in your company or ones that you see fit!
https://docs.google.com/spreadsheets/d/1xjz... (https://docs.google.com/spreadsheets/d/1xjz3j2ypHGLS6cj6EDmMmGmecUqUgCRAi5d6PONN5p0/edit#gid=0)

Legal disclaimer: By joining the New York Health Artificial Intelligence Society (NYHAIS) Meetup group you confirm that you are not a person residing in any country embargoed by the United States and/or subject to U.S. export controls or sanctions (including without limitation Iran, Cuba, Sudan, Syria and North Korea), or any other jurisdiction where the sharing of these technologies are otherwise prohibited, licensed, restricted or taxed by applicable federal, state, territorial, provincial or local laws, rules or regulations and any other country designated by the United States Treasury's Office of Foreign Assets Control. The members of New York Health Artificial Intelligence Society (NYHAIS) agree to abide by any United States export control laws regarding the technologies and strategies described and discussed in this group. The organizers of New York Health Artificial Intelligence Society (NYHAIS) are not liable for any attempts by the members to circumvent those laws.

Upcoming events (5+)

AI & Society

Think Coffee

We will be talking about AI and society, open source software, as well the current events in the field of AI. The Run down Introductions: 20 mins – Introduce yourself with the formal pleasantries of who are you and what do you do. Discussion on: [TBD] 90 Mins – Networking: 10 mins – Schmooze it up you guys. Exchange business cards, ideas, or jokes.

[Remote event] Machine vision using deep-learning -- Introduction and basics

• Join remotely here: https://meet.google.com/qij-pjqj-acr • What we'll do: This is a hands-on workshop on machine vision. During this workshop, attendees​ will learn: 1) What is a convolutional neural network by building one, 2) Transfer learning, 3) Object prediction and building object prediction pipelines. We will build a neural network from scratch and train it to recognize images. Once we create a network, we will improve this model using a strategy called transfer learning. Once we understand the concepts of transfer learning, we will train a deeper network called Inception version 3 to create a state of the art image classifier. The projects will use OpenCV, Tensorflow and Keras; three very popular machine vision and deep-learning tools. We will also cover the basics of deploying scalable python applications in the cloud. This week, we will be going step-by-step to deploy the deep Bayesian image classifier notebook in Google CoLab: https://github.com/rahulremanan/python_tutorial/blob/master/Machine_Vision/01_Transfer_Learning/notebook/Dogs_vs_Cats_Bayesian_classifier.ipynb The course is hosted using either our own cloud platform: Jomiraki, a cloud connected AI developer environment or Google CoLab, a GPU powered Jupyter compatible deep-learning instance. Either of these environments will be set-up ahead of time, with zero end-user dependencies. This will ensure that each participant will spent more time testing and running the code, instead of trying to figure out the set-up process itself. • What to bring: This a bring your own device (BYOD) event. For optimal experience, Moad machine vision team recommends Chrome >=72, to access the course contents. • Pre-requisites: Please set-up a Kaggle and GitHub account ahead of time. Both accounts are needed to get the full benefit of the code examples. • Important to know: This is an introductory workshop on machine vision. This course is part of the FutureReady boot-camp by Moad Computer. If you are interested in participating in the boot-camp, please fill-out this form: https://goo.gl/forms/TzClAtTqOLwHcudv1 • Additional materials: https://arxiv.org/pdf/1506.02142.pdf https://jacobgil.github.io/deeplearning/class-activation-maps

AI & Society

Think Coffee

We will be talking about AI and society, open source software, as well the current events in the field of AI. The Run down Introductions: 20 mins – Introduce yourself with the formal pleasantries of who are you and what do you do. Discussion on: [TBD] 90 Mins – Networking: 10 mins – Schmooze it up you guys. Exchange business cards, ideas, or jokes.

[Remote event] Machine vision using deep-learning -- Introduction and basics

• Join remotely for free here: https://meet.google.com/qij-pjqj-acr • What we'll do: This is a hands-on workshop on machine vision. During this workshop, attendees​ will learn: 1) What is a convolutional neural network by building one, 2) Transfer learning, 3) Object prediction and building object prediction pipelines. We will build a neural network from scratch and train it to recognize images. Once we create a network, we will improve this model using a strategy called transfer learning. Once we understand the concepts of transfer learning, we will train a deeper network called Inception version 3 to create a state of the art image classifier. The projects will use OpenCV, Tensorflow and Keras; three very popular machine vision and deep-learning tools. We will also cover the basics of deploying scalable python applications in the cloud. The topic for this week is explainable deep learning in the form of visualizing the predictions. To achieve this goal, we will be using a technique called class activation maps. The course is hosted using either our own cloud platform: Jomiraki, a cloud connected AI developer environment or Google CoLab, a GPU powered Jupyter compatible deep-learning instance. Either of these environments will be set-up ahead of time, with zero end-user dependencies. This will ensure that each participant will spent more time testing and running the code, instead of trying to figure out the set-up process itself. • What to bring: This a bring your own device (BYOD) event. For optimal experience, Moad machine vision team recommends Chrome >=72, to access the course contents. • Pre-requisites: Please set-up a Kaggle and GitHub account ahead of time. Both accounts are needed to get the full benefit of the code examples. This is an introductory workshop on machine vision. This course is part of the FutureReady boot-camp by Moad Computer. If you are interested in participating in the boot-camp, please fill-out this form: https://goo.gl/forms/TzClAtTqOLwHcudv1 • Additional materials: http://cnnlocalization.csail.mit.edu/ https://jacobgil.github.io/deeplearning/class-activation-maps RSVPs on this page without a tickets 24 hours before the event will be moved to Not Going automatically. They will be moved back if they purchase a ticket.

Past events (321)

Photos (185)

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