CS132
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AI as Technology Accelerator
Course Description
How is AI accelerating breakthroughs in fields like biotechnology, neuroscience, and cybersecurity? How are other fields (semiconductors, material sciences advances) influencing the development of AI? What opportunities and challenges arise as these domains converge? These are some of the key questions this course addresses. This course brings the Stanford Emerging Technology Review, a pioneering partnership between the School of Engineering, Institute for Human-Centered AI, and the Hoover Institution, into the classroom - offering a technically grounded, human-centered and interdisciplinary examination of key emerging technology areas like cybersecurity, biotechnology, neuroscience, space technologies, and material sciences, and their intersection with AI advancements that shape the future of science, engineering, and society. Each week, a leading Stanford faculty or fellow will present a seminar on one or multiple domains, emphasizing foundational concepts and computational challenges, current technical advances with AI, and over-the-horizon implications for individual users, communities, and broader society. The course is structured to balance technical breadth and depth, offering students an integrated view of technical innovation and its real-world consequences. Emphasis is placed on understanding both the mechanics of each technology and the broader factors that influence its development and adoption, such as economic incentives, security risks, and ethical considerations. This course is ideal for upper-level undergraduate and graduate students who want to engage with cutting-edge research, think critically about the future of technology and their research, and interact directly with experts at the intersection of science, society, and policy.
Cross Listed Courses
Grading Basis
ROP - Letter or Credit/No Credit
Min
2
Max
2
Course Repeatable for Degree Credit?
No
Course Component
Seminar
Enrollment Optional?
No
Programs
CS132
is a
completion requirement
for:
- (from the following course set: )
- (from the following course set: )