The Earth Challenge 2020 initiative overcomes AI model training challenges using citizen data scientists to collect data for environment and healthcare
Topic: Machine learning
The latest approaches using machine learning and artificial intelligence in business, Internet of Things, and the medical industry.
In the news this week: New low code application for creating digital twins, AIOps for Dummies for DevOps and SRE teams, and
The findings will help researchers design and manufacture optimized electrodes for improved cell
AutoML makes AI more accessible by automating complex manual data science processes. But there are caveats to its use. Here are the top 5 myths and realities …
Interest in using AI to manage buildings is on the rise because it can help lower costs during an economic downturn and reduce carbon emissions.
The three key goals for ML deployment include improved customer experience, increased profitability, and revenue.
The lack of defined, repeatable process for ML model operations may be part of the reason so many do not reach production.
Building on a Jupyter Notebooks foundation, the new toolkit is designed to help reduce model development tasks
As the volumes of data used in businesses grows, getting data suitably annotated and tagged to train machine learning models is an enormous challenge.
This foundational research will help keep the United States in the forefront as applications for ML and AI rapidly