Nina is a data scientist at AMT – Association for Manufacturing Technology, where she is an active practitioner and speaks about data science principles. Her work at AMT supports holistically understanding data and collaborating on its business value. Topics she enjoys discussing include statistical engineering, mathematical statistics, and coffee.
Nina is a data scientist at AMT – Association for Manufacturing Technology, where she is an active practitioner and speaks about data science principles. Her work at AMT supports holistically understanding data and collaborating on its business value. Topics she enjoys discussing include statistical engineering, mathematical statistics, and coffee.
Trends in Utilizing AI To Complement Integrated Force Control
AI and advanced sensors transform robotics, enabling direct force control with feedback. This article explores AI's role in integrated force control, predictive modeling, and their impact on robotic performance and end effectors across applications.
Derivative, Meet Gradient
Does a year-over-year percentage growth truly reflect industry change? Do increased robot installations in sectors with less robot density look the same in ones with more density? Derivatives help expose how total change is affected when variables change.
If Something Is True, Does It Mean It's Important? Understanding Statistical Significance
The statistical perspective of significance should not be confused with the practical sense of significance. Consider the difference between something having strategic importance versus something being statistically significant.
How Do You Make Sense of Data That You’ve Never Seen?
How do you make sense of data that you’ve never seen? This article provides a first-line approach on garnering data insights after the data has been initially retrieved.