Artificial Intelligence: Week #2 | 2022

This week in AI & Machine Learning: Improving disability employment, wisdom from The women leading the AI industry, Nvidia’s dynamic super resolution image scaling, and more!

Authors Note:

This week I hosted a workshop on creating your own object detection models and datasets for computer vision. You can check out the recording on our YouTube channel. Join our Meetup group to get notified for new events (we have some cool ones planned for this year)!

Zammo and Microsoft are using AI to Improve Disability Employment

If you don’t have a disability it can be easy to take online navigation and communication for granted. Zammo is already changing information accessibility for airports and OurAbility, but is looking to scale it’s AI powered voice technology to any company with a job board. Read how Microsoft and Zammo are increasing job accessibility here.

Wisdom From The Women Leading The AI Industry, With Elizabeth Spears of Plainsight

Plainsight’s Co-Founder & CPO, Elizabeth Spears met with Authority Magazine, to share her experiences, insights, and practical use cases for computer vision in industries.

Our first computer vision project as a company took a very practical task — counting cattle as they passed a camera — and turned that into a 40 million dollar yearly savings for our customer, just by doing that one task extremely accurately.”

Read the full interview with Elizabeth Spears here.

Nvidia’s AI-Powered Dynamic Super Resolution Image Scaling Technology (DLDSR)

NVIDIA announced it’s new AI-powered graphics rendering technology called DLDSR (Deep Learning Dynamic Super Resolution), which allows for a more detailed resolution using the Tensor cores of an RTX GPU. It’s always exciting to see more details come to our virtual worlds! Read more about the DLDSR announcement here.

No-Code and Low-Code Machine Learning Platforms Still Require People

With the rise in no-code and low-code machine learning platforms, such as Plainsight making machine learning more accessible, it’s important to remember that you should understand the problem you’re solving, creating unbiased datasets, and continue to monitor the results. Read about why no-code machine learning platforms still require people here.

 

Interpretable Machine Learning — with Serg Masís

Serg Masís, a data scientist Syngenta joins the Super Data Science Podcast to discuss interpretable machine learning, how to avoid interpreting models incorrectly, and why it’s important. See the full show notes here.

I’ll see you next week! You can join the discussion on the AI for ALL Slack group.

Published by Sage Elliott.

More Plainsight Blog Posts:

5 Ways Agribusinesses Can Prevent Recalls, Shutdowns, and Delays with Vision AI

5 Ways Agribusinesses Can Prevent Recalls, Shutdowns, and Delays with Vision AI

Investments in computer vision technology can help agribusinesses and food manufacturers of all types spot signs of trouble early and stop costly, potentially deadly recalls before they happen. Deployed across the production and manufacturing cycles, these models can detect hazards ranging from contaminants and foreign objects to defective equipment and non-compliant behavior. Organizations capture hundreds of thousands of hours of visual data in the form of video footage and imagery every day, and computer vision allows these businesses to put this data to work for process transformation.