AI Across Industries
The Demand Forecasting Challenge: Taking a Bite Out of the Supply Chain with AI
September 23, 2020
· 2 min read

Staying in tune with consumer demands can be challenging, especially when these demands change almost daily. Tracking consumption patterns to predict demand is a monumental task. Previously, solving this problem involved spreadsheets or legacy statistical methods. But with automation and machine learning, artificial intelligence can help organizations slice through mounds of data to get more accurate and timely insights on consumer demand . Machine learning can build forecasts for hundreds of thousands of items and consider all of the minute details for each one of them, from seasonality to sales history. For example, automated machine learning can easily create separate models on clusters of products, using unsupervised ML techniques, to make predictions even more granular such as by store, week, and SKU.

 
September 23, 2020
· 2 min read
AI in the News
Let’s Take It from the Top: DataRobot’s Predictions for 2020 Primetime Emmy Awards
September 15, 2020
· 4 min read

This year's 72nd Primetime Emmy Awards will take place on Sunday, September 20th. Looking back at last year's post where we used DataRobot to predict the winners for the 2019 Primetime Emmy Awards for the "Outstanding Drama Series" and "Outstanding Comedy Series" categories, we correctly predicted Game Of Thrones winning Outstanding Drama Series but missed the mark on Outstanding Comedy Series (Fleabag was the winner, whereas we predicted Veep would take the top prize). Since Game Of Thrones is absent from this year's 2020 Emmy nominations, let's see if the data says anything about potential underdog winners!

 
September 15, 2020
· 4 min read
AI Thought Leadership
How Do We Make Machine Learning More Aligned with Human Values?
September 3, 2020
· 3 min read

Cutting-edge algorithms and new research will continue to drive the advancement of machine learning. However, there’s a more straightforward way of resolving many challenges in machine learning today, especially when it comes to ethics and better alignment with human values. And it is not surprisingly, not focused on the technology, but rather on the people using it. For advanced AI and machine learning systems already in production, the focus is on delivering the intended value of the system, which is no longer a question of leveling up the technology or mathematical techniques behind it. Value-oriented approaches can be supported holistically by both the human expertise involved in creating AI and by the technology itself.

 
September 3, 2020
· 3 min read