When: Mondays and Wednesday from 2:00 to 3:30
Where: Soda 310
Instructors: Joseph Gonzalez and Matei Zaharia
Enrollment: Priority enrollment for CS PhD students; others should fill out this form.
Office Hours: By appointment (Email Matei, Joseph’s Calendly Link).
Communication: Slack (Invite Only)
The recent success of AI has been at least partly driven by advances in hardware and software systems. These systems have enabled training increasingly complex models on ever larger datasets. In the process, these systems have also simplified model development, enabling the rapid growth in the machine learning community. These new hardware and software systems include a new generation of GPUs and hardware accelerators (e.g., TPU) as well as open source frameworks such as TensorFlow and PyTorch (and many others) and have shaped AI research and practice.
A fundamental hypothesis in AI-Systems research is that advances in systems will enable the continued scaling of models and data to unlock new AI capabilities. In the past few years, we have started to see significant evidence of this hypothesis with very large models unlock new AI capabilities especially in the context of text and image generation.
In this course, we will study the latest trends in systems designs to better support the next generation of AI applications. We will focus on advances in generative AI and specifically LLMs and how they have mirrored advances in computer systems for AI. We will cover the key pieces of work from the system and AI literature that have driven these advances and may reveal where research is headed next.
This course has been offered several times in the past including Spring 2019, Fall 2019, and Spring 2022. Each time we have tried different variations of the course format with a mix of faculty presentations, student presentations, and external speakers. This semester we will focus largely on student presentations and external speakers on key topics.
Additional Policies (Health, Conduct, Collaboration, …)