May 18, 2022

6 Posts

Blue globe and several pins around it on a pink background
May 18, 2022

The Batch: One Model For Hundreds of Tasks, Recognizing Workplace Hazards, When Data Means Danger, Vision Transformer Upgrade

One of the challenges of building an AI startup is setting customer expectations. Machine learning is a highly experiment-driven field. Until you’ve built something, it’s hard to predict how well it will work. This creates a unique
Gato’s performance on simulated control tasks | Image captions generated by Gato
May 18, 2022

One Model, Hundreds of Tasks: Multimodal Transformer Performs Over 600 Different Tasks

Researchers took a step toward achieving a longstanding goal: One model that performs a whole lot of very different tasks. Scott Reed, Konrad Żołna, Emilio Parisotto and a team at DeepMind announced Gato.
Factory workers getting ready to work
May 18, 2022

Recognizing Workplace Hazards: AI Device Helps Warehouses Avoid Workplace Injuries

A wearable device may help warehouse workers avoid injuries. Modjoul, maker of a system that evaluates risks to people engaged in physical labor, received an undisclosed sum from Amazon as part of a $1 billion investment in technologies that might enhance the retailer giant’s operations.
US map with locations of Planned Parenthood
May 18, 2022

When Data = Danger: Consumer Behavior App Removes Planned Parenthood Data

Amid rising social tension in the United States over reproductive freedom, a company that analyzes location data on abortion clinics stopped distributing its findings after a critical press report.
Architecture of CXV
May 18, 2022

Upgrade for Vision Transformers: Improved Efficiency for Vision Transformers

Vision Transformer and models like it use a lot of computation and memory when processing images. New work modifies these architectures to run more efficiently while adopting helpful properties from convolutions.
Blue balloon on nails in a light pink background
May 18, 2022

How to Build AI Startups Part 3: Set Customer Expectations!

One of the challenges of building an AI startup is setting customer expectations. Machine learning is a highly experiment-driven field. Until you’ve built something, it’s hard to predict how well it will work.

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