Over the Hype, Under the Hood: MIT’s Justin Solomon on Understanding AI’s Foundations
AI has quickly moved from novelty to necessity in many organizations. Yet while headlines focus on the newest generative tools, the deep question facing technology leaders is practical: how do you evaluate which systems actually work—and which are hyped?
That question is the focus of a new course led by MIT Associate Professor Justin Solomon, a member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who studies the mathematical foundations of machine learning.
In “Mathematics and Modeling for Modern AI,” a course offered through MIT Professional Education, Solomon helps technical leaders and engineering managers look beneath the surface of AI systems—examining the statistics, architectures, mathematical models, and data that determine how these tools behave in the real world.
At a moment when companies are racing to deploy AI across their organizations, Solomon argues that understanding how these systems work “under the hood” has become a strategic advantage. Leaders who can distinguish between genuine technical progress and repackaged ideas are positioned to deploy AI responsibly, evaluate vendor claims, and guide teams building tools.