This week in AI & Machine Learning: AI models for improving solar cell production, chatbots, and more.
Author’s Note
Whatever the state of mask mandates in your town or state, personal protective equipment is an everyday concern for businesses in industries like construction. Learn more about how Plainsight streamlines the process for creating PPE-detection models.
Artificial Intelligence News
More Sophisticated Chatbots with Task-Oriented Dialogue
Researchers at Google have developed two new sequence-to-sequence approaches for dialogue modelings to improve chatbots. Task-oriented dialogue has historically presented a challenge for AI conversational agents. Google’s new approaches, however, eliminate fixed ontologies to boost the quality and efficiency of chatbots. One innovation, Description-Driven Dialogue State Tracking, improves AI’s ability to discern the context of prompts from users. Check out the detailed summary of Google’s research.
A Computer Vision Demo from Meta
Meta has released its first external-facing demo related to the company’s research on self-supervised learning. The demo focuses on DINO, a method for training Vision Transformers that the tech giant introduced last year. Developed through a partnership with Inria, DINO is capable of detecting and labeling objects with no human supervision. Watch the demo to learn more about Meta’s ongoing efforts to advance computer vision technology and visit Plainsight’s website for more information on our work to make cutting-edge vision AI model building, training, and deployment more accessible.
Optimizing Solar Cell Production with ML
Perovskites, a family of layered, crystalline compounds, promise to replace silicon-based elements in the production of solar cells. They hold the key to a future of lighter, thinner, more easily produced and transported cells. Unfortunately, producing them at scale has proven challenging – until now. A paper published in Joule by researchers from Stanford University and MIT proposes a new approach to the machine learning processes necessary for developing perovskites. Learn more about how researchers are integrating data from past experiments into ML processes and making it easier and more affordable to support renewable energy.
Join our Community
See you next week! Until then, keep the conversation going on Plainsight’s AI for All Slack Channel.
About the Author & Plainsight
Bennett Glace is a B2B technology content writer and cinephile from Philadelphia. He helps Plainsight in its mission to make vision AI accessible to entire enterprise teams–the ML power users, as well as the subject matter experts and non-technical users.
Plainsight’s vision AI platform streamlines and optimizes the full computer vision lifecycle. From data annotation through deployment, customers can quickly create and successfully operationalize their own vision AI applications to solve highly diverse business challenges.
