The charm of AI may have been kept alive by both the science community and Hollywood, but Ash Dhupar knows its real-world application can have a huge impact on the bottom line. As Chief Analytics Officer at Publishers Clearing House, he and his team utilize 180 algorithms to predict LTV with a 2-3% accuracy. By adding the laser focus of CLV as an objective, the application of machine learning and AI techniques help to point to the most powerful marketing channels by attribution, better optimizing marketing dollars.
10-year-old grading software leverages deep learning algorithms to “compare notes” with human teachers’ scores, suggestions, and comments. An engineer involved in the project compared its capabilities to those of AlphaGo, the record-breaking AI Go player developed by Google subsidiary DeepMind.
Avi Goldfarb, a professor at the University of Toronto’s Rotman School of Management, explains the economics of machine learning, a branch of artificial intelligence that makes predictions. He says as prediction gets cheaper and better, machines are going to be doing more of it. That means businesses — and individual workers — need to figure out how to take advantage of the technology to stay competitive. Goldfarb is the coauthor of the book “Prediction Machines: The Simple Economics of Artificial Intelligence.”
Very thought-provoking (if not sobering) article…if your Analytics program is not heading in the direction you hope, this article provides very clear places to start hunting for improvement. Many thanks to Walt Frick for this find!
from Digi*Pub…Conversations from LearnLaunch 2018: edTech Start-ups AdmitHub, Authess, and Choosito
Pressures to bring affordability and relevance to education are driving fascinating innovation within educational publishing, and these trends are made possible by developments in AI. How exactly does this technology underpin these important developments in education?
