This week in AI & Machine Learning: AI research to help predict COVID-19 resource needs, the White House Launches the National Artificial Intelligence Initiative Office, Amazon opens Alexa AI Tech, Implementing a Transformer with PyTorch, On-Device ML Study Jam, and more.
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Sixgill Tip of the Week:
Save time labeling complex objects by using the track forward and smart polygon selection features in Sense Data Annotation. Label in one frame and have it automatically tracked and labeled on the next! Sign-up now and get started with a free 30-day trial.
Artificial Intelligence News:
Facebook AI has collaborated with New York University (NYU) Langone Health’s Predictive Analytics Unit and Department of Radiology to help predict how much care and resources a patient may need for COVID-19 treatment.
It’s challenging for doctors to predict the course of #COVID19 in a patient. In partnership with @nyulangone we’re open-sourcing AI models to help hospitals predict whether a patient’s condition will deteriorate and to help plan for resource allocation. https://t.co/vf3IvRt2bh pic.twitter.com/0cOymNaPsp— Facebook AI (@facebookai) January 15, 2021
We have been hearing increasing talk about how the U.S government should approach and support artificial intelligence (A.I.) development. This week, the White House Office of Science and Technology Policy (OSTP) has officially created the National Artificial Intelligence Initiative Office to oversee nationwide AI strategy, research collaboration, and policymaking.
Amazon opens its Alexa AI technology to third-party companies for the first time, allowing customized Alexa assistants to be embedded in virtually any platform, starting with automobile manufacturers. This is a huge move from Amazon and may spur an AI voice assistant boom across many industries.
Developer Tools & Education:
In the latest pyimagesearch post, learn how to perform real-time augmented reality in video streams using OpenCV. This continues the computer vision + augmented reality series that has been going on for the past several weeks.
Check out this interesting thread and Google Colab about using transformers and how to implement them in pytorch.
Google AI shows off its new algorithm, “View-Invariant Probabilistic Embedding for Human Pose” (Pr-VIPE) to recognize pose similarity with computer vision in images and videos.
Pr-VIPE is a new approach to pose perception that uses probabilistic embeddings that are view-invariant to avoid the ambiguity arising from the 2D projection of 3D poses. The model is simple and compact, and can be trained in ~1 day on CPUs. Learn more at https://t.co/OEQx5VAPAk pic.twitter.com/TIoE7cs8X6— Google AI (@GoogleAI) January 14, 2021
Upcoming Online AI & Data Events:
This week we’re continuing chapter 8: Optimization for Training Deep Models starting at section 8.3 Basic Algorithms with some planned demos from members.
Come join us to learn on-device ML with TensorFlow Lite! There will be talks first then we switch to hands-on lab: Intro to TFLite & Mobile ML, Coral Edge TPU + demo, and hands-on lab of TensorFlow Lite.
Being able to classify and localize where an object is in an image is foundational to many computer vision applications. Join this hands on workshop to get started with Computer Vision & Object Detection.
Join IBM space industry specialists to learn about the space industry, various space tech initiatives and the AI/ML that powers today’s space age.
PyCascades is just around the corner and since they’re virtual this year they added more tickets after selling out. You can find a list of speakers and talks here.
Interesting Podcasts & Interviews:
In the latest episode of AI rewind TWiML’s guest, Michael Bronstein will give his thoughts and predictions on the graph machine learning field.
Join Erica Greene on the super data science podcast to learn how to scale up machine learning, avoid model drift, set priorities for large ML teams, and more.
Saiph gave a talk at NeurIPS 2020 called “A Future of Work for the Invisible Workers in A.I.”. This conversation dives deeper into the roles and potential issues around people doing much of the labeling for machine learning.
Notable Research Papers:
- Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
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