This week in AI & Machine Learning: AI policies for US government, FDA AI grant, Fully Driverless Autonomous Vehicles, how to build a dataset for computer vision, pytorch lighting, Tensorflow, fastai, and more.
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Sixgill Tip of the week: Sign up for a 30-day free trial to test out our new online data annotation tool with smart polygon selection. Use promo code: HOLIDAY2020 to get an additional 60 days free.
Artificial Intelligence News:
As far as I know, this is the first official artificial intelligence post on the USDA.gov website, which is very exciting! Hopefully it’s the first of many! Scientists, funded by a $20 million grant from the USDA’s National Institute of Food and Agriculture, are working to create the next generation of food systems using artificial intelligence.
Although several companies have been operating fully autonomous vehicles (usually with a safety driver still behind the wheel) for a while now, this week we’ve seen just how close we may be to having self driving cars in many more places. Read why Oliver Cameron thinks this has been a game-changing several weeks for not only his company (Voyage), but the entire self-driving industry.
🚖 Join me for a ride in a fully driverless @voyage robotaxi, with no Safety Driver!— Oliver Cameron (@olivercameron) December 10, 2020
This is a meaningful moment for @voyage, and a glimpse of an exciting future not too distant.
🎥 The full video is unedited, with commentary on how our technology works: https://t.co/L0bDgpLmRa pic.twitter.com/Y4L8BlEFTG
The U.S. government has set guidelines for artificial intelligence adoption and establishing trustworthy AI within Federal Agencies. This contains outlines for: principles for the use of AI in government, common policy for implementing P\principles, catalogue of agency use cases of AI, and enhanced AI implementation expertise.
If you’re interested in reading more about AI development and government involvement, I suggest checking out the book “AI Super Powers” by Kai-Fu Lee. He offers some really interesting opinions on the AI ecosystems of the world.
Developer Tools & Education:
This workshop covers high-level intuition + applications of computer vision, practical examples how to create your own computer vision datasets, and how to train your own object detection machine learning mode.
Pytorch Lighting 1.1 is out with some major updates! The biggest of which is probably Model Parallelism Training.
This update features experimental support for asynchronous training of Keras models, moves mixed precision out of experimental phase, and more.
Another update to a great deep learning library–what a week! This update mostly contains minor bug fixes.
With AWS re:invent happening this week, we’ve seen quite a few machine learning tool releases from Amazon.
Upcoming Online AI & Data Events:
It looks like ticket sales have reopened! NeurIPS is still underway tomorrow. You can expect some really interesting presentations, talks, and papers.
A casual virtual happy / networking hour in a brain computing interface group.
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.
Interesting Podcasts & Interviews:
Listen to this conversation with Rama Akkiraju to learn about IBM, AI services, ROI on AI development, AI ethics and more.
Catch up with Anima Anandkumar and learn about her team at NVIDIA getting seven research papers published this week at NeurIPS!
Join Peter Mattson to learn about MLCommons, MLPerf, best practices of getting a machine learning model into production and more.
Explore MLOps with Daan Odijk and overcome challenges of scaling video AI services.
Notable Research Papers:
Some of the interesting machine learning papers published this week.
- Fact-Enhanced Synthetic News Generation
- Few-shot Image Generation with Elastic Weight Consolidation
- Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods
- The Lottery Ticket Hypothesis for Object Recognition
- Semantic Image Synthesis via Efficient Class-Adaptive Normalization
- Spatial Language Understanding for Object Search in Partially Observed Cityscale Environments
- You Only Need Adversarial Supervision for Semantic Image Synthesis
- Super-Selfish: Self-Supervised Learning on Images with PyTorch
- Understanding Attention: In Minds and Machines
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