This week in AI & Machine Learning: You can help train a NASA rover, detecting hazardous objects, data annotation for skin cancer, AI-powered drive-thrus, robot firefighters, deep learning accelerates archaeology, and more!
Authors note:
If you missed my workshop on building your own object detection models and datasets for computer vision you can check out the recording here!
My Top AI Highlight:
You Can Help Train NASA’s Rovers to Better Explore Mars
You can help make our Mars robots even smarter. Members of the public can now help teach an artificial intelligence algorithm to recognize scientific features in images taken by the @NASAPersevere rover. Find out how to get involved: https://t.co/5gbOXJq020 pic.twitter.com/xys0xlc7HZ
— NASA Mars (@NASAMars) October 26, 2021
“Members of the public can now help teach an artificial intelligence algorithm to recognize scientific features in images taken by NASA’s Perseverance rover.”
You can help NASA make a more robust machine learning model for future space missions! Just like most supervised computer vision solutions, NASA requires humans to perform data annotations on images to improve accuracy. Learn how you can help on NASA’s official blog.
🤖 Artificial Intelligence News:
- Detecting Unwanted & Hazardous Objects with Vision AI
- Deep Learning Architecture for Skin Cancer Segmentation
- FDA releases ‘guiding principles’ for AI/ML device development
- Making this album with AI ‘felt like wandering in an enormous labyrinth’
- GitHub sees uptick in coders using AI assistant
- ‘It means everything’: Duke professor receives $1 million dollar artificial intelligence award
- McDonald’s, IBM Drive-Thru Partnership Shows How A.I. is a Big Deal
- You Can Help Train NASA’s Rovers to Better Explore Mars
- ‘Small Data’ Are Also Crucial for Machine Learning
- Firefighting Robots Go Autonomous
- Busy Beaver gets Badger robots
- Amazon launches AWS instances powered by Habana’s AI accelerator chip
🛠️ Developer Tools & Education:
- Introducing PyTorch-DirectML: Train your machine learning models on any GPU
- GoEmotions: A Dataset for Fine-Grained Emotion Classification
- Training a DCGAN in PyTorch
- Deciding Which Tasks Should Train Together in Multi-Task Neural Networks
- Feature Extraction in TorchVision using Torch FX
- Accelerating PyTorch with CUDA Graphs
- Predicting Spreadsheet Formulas from Semi-structured Contexts
📅 Upcoming Online AI & Data Science Events:
- Workshop: Build Trustworthy AI System (Nov 8–10:00am PDT)
- Semantic Search and Neural Information Retrieval (Nov 9–12:00 pm PDT)
- TensorFlow MLCommunity Day (Nov 9–9:00 am PT)
🎤 Interesting Podcasts & Interviews:
- Researchers Chris Downum and Leszek Pawlowicz Use Deep Learning to Accelerate Archaeology
- Multi-task Learning for Melanoma Detection with Julianna Ianni
- House Hunters: Machine Learning at Redfin with Akshat Kaul
- Courses in Data Science and Machine Learning
- Eureka moments with natural language processing
📄 Notable Research Papers:
- A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
- Safely Bridging Offline and Online Reinforcement Learning
- STransGAN: An Empirical Study on Transformer in GANs
- MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
🐘 About the Author & Plainsight:
- Sage Elliott is a Developer Evangelist at Plainsight & passionate about making AI more approachable. Connect with Sage on Twitter or LinkedIn.
- Plainsight’s vision AI platform streamlines the end-to-end machine learning process. From data annotation through deployment, customers quickly create and successfully operationalize their own vision AI applications to solve highly diverse business challenges. Join the conversation on Slack.
