3 Questions: What is the best path forward for AI in academia?
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
Over two decades at MIT, Hamilton forged new paths in computer science and engineering. Her groundbreaking work included leading the team that programmed the first computers that took humans to the moon.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
The PKG Center for Social Impact expands Code.Tulsa experiential learning program.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
In their new book, “How AI Sees the City,” the leaders of MIT’s Senseable City Lab examine the technology’s implications for researching urban life.
Inaugural artist Amy Karle will join astrophysicists at MIT’s Kavli Institute to explore questions about the universe and translate them into an immersive multimedia experience.
Naoki Egami has become a standout in political methodology, helping refine tools that give scholars durable results.
The PhD student works to give young people an active role in shaping digital technologies that can support their own well-being.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
MIT historian Tristan Brown uncovers the true origins of an artifact once thought to be a relic of the Boston Tea Party.