In this week’s roundup, we look at how the accuracy of predictive models have taken a hit with the turbulent events of 2020 and how they can be improved. Working from home is another area that has been affected by the pandemic and is being transformed by artificial intelligence (AI) and machine learning (ML). IT leaders see great potential in AI and ML, especially in unstable economic conditions. Software developers may be taking the wrong approach with ML and need to rethink how it’s learned. Lastly, ML is being used to identify the traits that bring happiness to romantic relationships.
By Lisa Morgan, contributing writer for InformationWeek.com
The COVID-19 pandemic has turned the world upside down and has forced businesses to grapple with unprecedented changes. One of these changes happens to be compromised accuracy from predictive models. Now that we are gathering more information on 2020’s “new normal,” we know more about its effects on organizational and customer behavior. With a possible second round of shutdowns around the corner, increasing modeling agility through automation is key.
By Daglar Cizmeci, contributing writer for technative.io
AI’s potential can’t be underestimated when it comes to remote work. Even companies that were hesitant to embrace remote work were forced to do so during the COVID-19 pandemic. Now, technologies that allow employees to operate at similar levels of productivity at home as in the office have become increasingly commonplace. AI and MLI have made it easier for team leaders to monitor staff performance in a non-invasive manner and identify employee strengths and weaknesses.
By Eric Knorr, Editor in Chief at CIO
AI and ML have little to do with human intelligence; rather, they are about recognizing patterns and automating tasks. A recent CIO Tech poll reveals that today’s IT leaders see enormous potential and many are considering a greater investment, even as the economy becomes unstable.
By Dale Markowitz, Applied AI Engineer at Google and contributor to The Next Web
In software development, people are often taught core concepts and low-level details first and build from there. That approach is not always beneficial for those who want to learn machine learning. In the future, ML will be used as a tool that gives implementation details to highly focused experts. Software developers using ML may not even need to think about the low-level details, but instead concentrate on best practices in deploying these smart algorithms in the world.
By Elizabeth Fernandez, contributing writer for Forbes.com
Can ML predict what makes people happy in a romantic relationship? Over the years, researchers have analyzed massive amounts of data, but it is difficult and expensive to recruit couples for the studies. Now, ML is able to bypass these challenges and wade through all of the pre-existing data to identify variables that predict happiness.
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