Showing results for "Driven" in Computers & Technology

The difference between growing your business and going out of business is your ability to think strategically. The problem is, most leaders are stuck in the operational weeds, struggling to find the time to make better strategic decisions. This challenge is only heightened by the rise of AI. While you know AI is the future, current demands leave you with no time to explore its benefits. Meanwhile, your competition is gaining an edge by integrating AI. The time to act is now.

Columbia Business School professor Sandra Matz reveals in fascinating detail how big data offers insights into the most intimate aspects of our psyches and how these insights empower an external influence over the choices we make. This can be creepy, manipulative, and downright harmful, with scandals like that of British consulting firm Cambridge Analytica being merely the tip of the iceberg.

Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems.

From technology, media, and retail to sport teams, government agencies, and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety, and so much more. Organized for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved, and the processes put in place to make it practical, as well as the technical details, challenges, and lessons learned from each unique scenario.

As humans compete for work against faster, cheaper, and smarter technology, the flood of AI-generated information undermines traditional expertise, eroding essential skills like writing, analysis, and decision-making. Despite these challenges, AI presents immense opportunities for those who master its intricacies, such as driving innovation, improving efficiency, and creating new markets. Can we thread the needle balancing the immeasurable benefits with the profound risks that AI presents to our future?

Cyber Intelligence-Driven Risk provides a solution to one of the most pressing issues that executives and risk managers face: How can we weave information security into our business decisions to minimize overall business risk?