This week in AI & Machine Learning: AlphaFold, AI balloons, transformers for computer vision, Yann LeCun’s deep learning course, Amazon’s ML compute chip, AI happy hour, and more!
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Artificial Intelligence News:
By far, the biggest news this week comes from the Google DeepMind team. They published the results from AlphaFold 2, their AI system, that solves the protein folding problem. Understanding protein shapes can potentially unlock treatments for diseases or development of other industry solutions that require the understanding of enzymes.
There is a lot of debate and potential hype over how impactful this breakthrough could be. Understanding the protein folding problem and its impact is complicated. So if you’re interested, I recommend watching the video below or reading this article. Both do a good job of breaking it down.
Before you get too excited, these are balloons running experiments or capturing data. It might be a while before someone creates a self driving hot air balloon taxi! But still, this application that uses reinforcement learning to make weather balloon actions autonomous is very cool. It could really help important equipment carrying balloons, like Google’s “project loon” overcome challenging weather conditions.
Google recently released a paper mapping out some areas of concern for bias in machine learning when deployed to real world applications. If you’ve been working in the field for a while many of the areas may not be “news” to you. But, I think it’s important that companies continue researching areas of weakness in AI models and formalize those findings from which everyone can learn.
Developer Tools & Education:
It’s likely you have at least heard of transformers by now and how they have greatly improved Natural Language Processing applications. Read how Google is using Visual Transformers to replace Convolutional Neural Networks (CNNs) with transformers for efficient computer vision applications.
Check out this new course on deep learning and PyTorch by none other than Yann Lecun and NYU. The format and material looks really good–a great blend of theory and application with PyTorch code.
This week’s PyImageSearch tutorial continues with the Siamese network theme showing how to implement them in Keras and Tensorflow.
Every major tech company is getting into the machine learning computing chip game. Amazon launched its own chip designed to train and run machine learning models.
Learn how Google uses TFX to put ML models in production and what’s new this fall.
Upcoming Online AI & Data Events:
This is a casual networking style event. Come by and introduce yourself and share a little about what you’re working on or learning.
Various industries such as Manufacturing, Energy and Utilities, Automotive, Aerospace and Defense, Logistics and Transportation, and Building Management have proposed the use of Digital Twins to aid the Design, Analysis, Build, Manufacturing, and Operations phases of asset-intensive industries.
Get started with computer vision and object detection. I, Sage Elliott, will walk through how to build your own object detector to locate objects in images and videos with Facebook detectron2 and cover practical data labeling methods for you to use in your own projects.
This introductory workshop will get you started with computer vision and walk you through how to build your own object detection system to locate objects in images and videos.
Learn about using Azure’s Machine Learning notebooks. This is a pretty cool online environment for running ML. Especially for experimentation.
Don’t forget AWS reInvent is online this year, and still happening this week! Starting today, you can sign up to see a bunch of really neat talks. A 2nd round of events is also happening in January.
Pycascades is a ways out, but I wanted to make sure I posted here since tickets recently went on sale! Like many conferences, the event will be held online this year.
Interesting Podcasts & Interviews:
Listen to this panel discussion to learn how to decrease time to market with MLOps, feature stores, and more.
Explore the current state of NLP, like BERT, HuggingFace, and much more.
Manolis Kellis is a computational biologist at MIT. Much of this conversation is more philosophical than technical, but there may be some parts you find interesting in relation to the field of AI.
Notable Research Papers:
Some of the interesting machine learning papers published this week.
- 6.7ms on Mobile with over 78% ImageNet Accuracy: Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration