AI & Machine Learning
How to Succeed With Predictive AI
To succeed with machine learning, manage projects as business initiatives, not technology projects.
To succeed with machine learning, manage projects as business initiatives, not technology projects.
On the Me, Myself, and AI podcast, Airbnb’s Naba Banerjee explains how machine learning helps protect hosts and guests.
UC Berkeley’s Ziad Obermeyer discusses how machine learning and AI are being used for medical research and diagnoses.
A Q&A with AWS’s Michelle K. Lee on the challenges and advantages of adopting machine learning.
Swarm systems draw input from individuals and use algorithms to optimize system performance in real time.
Counterpoints looks at predicting the college football playoffs with UW-Madison’s Laura Albert.
AI offers a helping digital hand to overburdened B2B marketing departments.
People are complex. We need a more nuanced approach to predicting job performance.
MIT professor Munther Dahleh proposes a marketplace for data that bases the cost of data on the financial value it generates
Health care companies are using digital tools to put doctors’ focus back on patients.
To fully benefit from supply chain analytics, companies need to be able to act on insights quickly.
Managers already struggle to put data to intelligent use; AI may add to their difficulty.
The 2017 Data & Analytics Report by MIT Sloan Management Review finds that companies that embraced analytics have begun to find new ways to derive strategic benefit from analytics.
For the IoT revolution to progress, data collection and data quality both must be greatly improved.
AI’s value for managers lies in its ability to predict equipment failures and assess human emotions.
What’s happening this week at the intersection of management and technology.
Industry expert Gerhard Kress discusses how the transportation industry is capitalizing on the opportunities that Internet of Things data offers.
What’s happening this week at the intersection of management and technology.
The success or failure of bringing the IoT into an organization depends on the commitment of its leadership.
What will happen to predictive analytics once everything is connected?