This week in AI & Machine Learning: Cruise acquires Voyage, computer vision to combat wildfires, empathetic robots, SpeechBrain toolkit, 3D PyTorch debugger, and more!
🤖 Artificial Intelligence News:
- Self-Driving Car Company Voyage Acquired by Cruise ← I’ve been keeping up with Oliver Cameron and his leadership of Voyage since its launch. I’m excited to see how the Voyage team can help accelerate Cruise in the autonomous vehicle space!
- A California County Tests Computer Vision for Wildfire Detection ← This type of project has been on a lot of people’s minds in the past couple of years.
- Empathetic Robots Are Killing Off the World’s Call-Center Industry
- New Machine Learning Tool can Accelerate Drug Discovery
- Artificial Intelligence Supply Chain Management Industry Will Reach $15.5B
- How and When Quantum Computers will Improve Machine Learning?
- OrionStar’s 5G Robots Target Service, Convenience, and Coffee
🛠️ Developer Tools & Education:
- SpeechBrain Toolkit is Out “SpeechBrain is an open source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. State-of-the-art performance is obtained in various domains.”
- Mixing normal images and adversarial images when training CNNs ← Learn how to defend from adversarial attacks on CNNs.
- Gradient Descent Optimization With Nadam From Scratch
- Efemarai — Announces Free 3D PyTorch Debugger ← This looks really fun to play around with.
- Leveraging Machine Learning for Game Development
- Sixgill Sense: AI-Powered Update ← Check out our new AI-Powered data annotation features!
🎤 Interesting Podcasts & Interviews:
- Author Cade Metz Talks About His New Book “Genius Makers” | NVIDIA AI
- Big Global Problems Worth Solving with Machine Learning | SDS
- Interview with Luis Serrano | Views of Pol Fañanás and Gerard Garcia
- Complexity and Intelligence with Melanie Mitchell
- Accelerating Innovation with AI at Scale with David Carmona
📄 Notable Research Papers:
- Is it Enough to Optimize CNN Architectures on ImageNet?
- TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
- PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss
- Approximating How Single Head Attention Learns
- Revisiting ResNets: Improved Training and Scaling Strategies
- Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks
