This week in AI & Machine Learning: Computer vision for wildlife preservation, Amazon and USC create ML research center, optical flow estimation, how to build object detection models and datasets, and more.
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Sixgill Tip of the Week:
Collaborate on computer vision labeling using Sense Data Annotation. Easily invite colleagues to work together on the same dataset and review labels created by other team members. Sign-up now and get started today free of charge. No credit card required.
Artificial Intelligence News:
It seems like every week we see a new way machine learning is being used for animal preservation, and I’m loving it! Check out how the non-profit Wild Me, uses computer vision and citizen scientists to help identify, and monitor animals in the fight against extinction.
Amazon and the University of Southern California partner to create The Center for Secure and Trusted Machine Learning. This topic of research is something that may play a big role in creating artificial intelligence that is more secure and generally more trusted by the public to preserve privacy. It’s also neat to see big tech companies forming academic partnerships like this. I won’t be surprised if this type of partnership becomes more common.
Continuing with more AI and college news, Colby College, a libral arts school in Waterville, Maine announces the Davis Institute for Artificial Intelligence, the first cross-disciplinary institute for artificial intelligence at a liberal arts college.
Learn how a Korean folk singer’s voice is being recreated with AI for a new song 25 years after his death. It will be interesting to see how this trend of bringing people’s voices back will continue. Microsoft also recently filed for a patent for creating chatbots based on people’s history. Would you opt-in to something like this?
Developer Tools & Education:
Check out this recorded workshop to learn about computer vision applications, creating your own dataset for object detection, and how to train models in python.
Learn about deep learning based methods for calculating and predicting optical flow with RAFT.
The tutorial this week from pyimagesearch will teach you how to detect low contrast images with OpenCV, scitkit-image, and python. This can be a useful step when building an image pipeline for computer vision.
This release removes the AVX2 requirement from TF 2.4.0. I know some people had problems with this on some chipsets.
This release contains some updates and fixes to metrics and semi-supervised learning methods.
Upcoming Online AI & Data Events:
Learn about integrating AutoML, custom training for Tensorflow jobs, deployment to cloud instances, serving binaries, custom pre- and post- processing, auto-scaling, containers and debugging deployed models.
Pedro Domingos will show that deep networks learned by the standard gradient descent algorithm are in fact mathematically approximately equivalent to kernel machines.
Shifting patterns mean that some AI models, which were previously working fine, are now no longer predicting with the same accuracy. Learn how to tackle this problem automatically.
This week, we’re continuing chapter 8: Optimization for Training Deep Models starting at section 8.5 Algorithms with Adaptive Learning Rates with some planned demos from members.
Interesting Podcasts & Interviews:
Listen to Deblina Bhattacharjee discuss math for machine learning, approaches for unsupervised machine learning, productivity tips, and more.
Listen to Francisco Webber discuss approaches to natural language processing, including, GPT-3, semantic folding, semantic extraction, search use cases and more.
Gurdeep Pall talks about autonomous systems, machine learning, challenges of simulation, and more.
Notable Research Papers:
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