Artificial Intelligence: Week #4 | 2021

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:

Ways Wild Me Uses Artificial Intelligence And Citizen Scientists To Help With Conservation

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 Will Create A Machine Learning Research Center

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. 

Colby College Announces Davis Institute for Artificial Intelligence

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.

South Korean AI technology Brings Back Folk Singer’s Voice

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:

Intro To Computer Vision: Building Object Detection Models and Datasets

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.  

RAFT: Optical Flow estimation using Deep Learning

Learn about deep learning based methods for calculating and predicting optical flow with RAFT.

Detecting Low Contrast Images With OpenCV, Scikit-Image, and Python

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. 

TensorFlow 2.4.1 has Been Released

This release removes the AVX2 requirement from TF 2.4.0. I know some people had problems with this on some chipsets.

Scikit-Learn Version 0.24.1 Released

This release contains some updates and fixes to metrics and semi-supervised learning methods. 

Upcoming Online AI & Data Events:

Workshop: Enterprise Production AI With GCP AutoML

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.

Deep Networks Are Kernel Machines

Pedro Domingos will show that deep networks learned by the standard gradient descent algorithm are in fact mathematically approximately equivalent to kernel machines.

Automating Model Monitoring and Drift Detection

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. 

Book Club: Deep Learning | Chapter 8 Continued | Optimization for Deep Learning

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:

Deep Learning for Machine Vision | Super Data Science

Listen to Deblina Bhattacharjee discuss math for machine learning, approaches for unsupervised machine learning, productivity tips, and more. 

Semantic Folding for Natural Language Understanding with Francisco Webber

Listen to Francisco Webber discuss approaches to natural language processing, including,  GPT-3, semantic folding, semantic extraction, search use cases and more.

The Future of Autonomous Systems with Gurdeep Pall

Gurdeep Pall talks about autonomous systems, machine learning, challenges of simulation, and more.

Notable Research Papers:

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About Sixgill:

Sixgill, LLC provides custom enterprise AI solutions, end-to-end machine learning lifecycle management, and fast data annotation for computer vision.

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Artificial Intelligence: Week #17 | 2021

Artificial Intelligence: Week #17 | 2021

This week in AI & Machine Learning: Lyft sells self-driving car division, robotaxis, AI’s carbon footprint, robot makes paella, self-supervised learning with transformers, RNA Sequencing, how drones are reshaping Africa, and more!

Artificial Intelligence: Week #16 | 2021

Artificial Intelligence: Week #16 | 2021

This week in AI & Machine Learning: The UN using AI for peace, using machine learning to rescue clinical trials, robots for hospitals, PyTorchVideo framework, training transformers at scale, and more!