321 - 330 of 366 results for: CS

Course Description:Large Language Models (LLMs) have revolutionized AI through remarkable pattern matching capabilities. However, the path to Artificial General Intelligence (AGI) requires advancing beyond unconscious (System 1) to conscious (System 2) processing. This research-oriented course explores fundamental approaches to elevate LLMs toward AGI capabilities through conscious reasoning, planning, and decision-making. Core Research Questions: 1. How can we enable LLMs to transition from pattern matching to conscious deliberation? 2. What frameworks support robust reasoning and verifiable decisions? 3. How do we implement planning and temporal awareness in LLM systems? 4. What role does multi-LLM agent collaboration play in advancing toward AGI capabilities? The course examines: 1. Theoretical foundations of consciousness in AI 2. Multi-LLM Agent Collaborative Intelligence (MACI) frameworks 3. Entropy-guided information exchange 4. Constitutional AI principles 5. Temporal reasoning and planning architectures. Through lectures, discussions, and hands-on projects, students will explore practical implementations across various domains. While healthcare provides immediate applications (diagnosis, treatment planning), the principles apply broadly to any field requiring AGI-level reasoning capabilities. Prerequisites: Machine Learning, Deep Learning

The last ten years has seen a watershed in the development of large-scale neural networks in artificial intelligence. At the same time, computational neuroscientists have discovered a surprisingly robust mapping between the internal components of these networks and real neural structures in the human brain. In this class we will discuss a panoply of examples of such "convergent man-machine evolution", including: feedforward models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics and motor control; integrated models of attention, memory, and navigation; transformer models of language areas; self-supervised models of learning; and deep RL models of decision and planning. We will also delve into the methods and metrics for comparing such models to real-world neural data, and address how unsolved open problems in AI (that you can work on!) will drive forward novel neural models. Some meaningful background in modern neural networks is highly advised (e.g. CS229, CS230, CS231n, CS234, CS236, CS 330), but formal preparation in cognitive science or neuroscience is not needed (we will provide this).

In this course we creatively apply information technologies to collectively attack Global Grand Challenges (e.g., global warming, rising healthcare costs and declining access, and ensuring quality education for all). Interdisciplinary student teams will carry out need-finding within a target domain, followed by brainstorming to propose a quarter long project. Teams will spend the rest of the quarter applying user-centered design methods to rapidly iterate through design, prototyping, and testing of their solutions. This course will interweave a weekly lecture with a weekly studio session where students apply the techniques hands-on in a small-scale, supportive environment. Note: Cardinal Course certified by the Haas Center for Public Service

In recent years, serious games have become both a booming industry and a powerful tool for education, helping to illuminate complex social and ecological challenges and inspiring meaningful engagement with their solutions. This project-based course introduces core principles of game design, with a focus on creating games that teach. Run as a hands-on studio, students will design and and prototype games aimed at social change and civic engagement. Through lectures and extensive readings, we will explore the fundamentals of game design in order to develop effective games that address pressing societal issues.

The Read, Write, Play class is an interdisciplinary exploration into games as both interactive experiences and cultural artifacts. By analyzing both classic and contemporary games alongside diverse readings - from practical design to cultural critiques - students will develop a deep understanding of game design, programming and play, and games' impacts on players and society. The course encourages critical thinking and creative reflection by combining play with academic inquiry, structured through blog posts and discussions. Students will read, write, and engage in dynamic discussions that explore different perspectives, from game development to cultural impact. The course aims to empower students with the analytical and expressive skills to critically evaluate games as a medium and articulate their insights through writing.

Designing for accessibility is a valuable and important skill in the UX community. As businesses are becomeing more aware of the needs and scope of people with some form of disability, the benefits of universal design, where designing for accessibility ends up benefiting everyone, are becoming more apparent. This class introduces fundamental Human Computer Interaction (HCI) concepts and skills in designing for accessibility through individual assignments. Student projects will identify an accessibility need, prototype a design solution, and conduct a user study with a person with a disability. This class focuses on the accessibility of UX with computers, mobile phones, VR, and has a design class prerequisite (e.g., CS147, ME115A).

This project-based class focuses on understanding the use of technology in the world. Students will learn generative and evaluative research methods to explore how systems are appropriated into everyday life in a quarter-long project where they design, implement and evaluate a novel mobile application. Quantitative (e.g. A/B testing, instrumentation, analytics, surveys) and qualitative (e.g. diary studies, contextual inquiry, ethnography) methods and their combination will be covered along with practical experience applying these methods in their project. Prerequisites: CS 147, 193A/193P (or equivalent mobile programming experience).

This class focuses on building agents that achieve human-level performance in specialized technical domains and are adept at collaborating with humans using natural language. We draw upon research in cognitive and systems neuroscience to take advantage of what is known about how humans communicate and solve problems in order to design advanced artificial neural network architectures. For more detail on invited speakers, schedule of talks and project milestones, see here:   https://web.stanford.edu/class/cs379c/class_messages_listing/curriculum/

This is an advanced course that will focus on modern machine learning algorithms for autonomous robots as an embodied intelligent agent. It covers advanced topics that center around 1. what is embodied AI and how it differs from internet AI, 2. how embodied agents perceive their environment from raw sensory data and make decisions, and 3. continually adapt to the physical world through both hardware and software improvements. By the end of the course, we hope to prepare you for conducting research in this area, knowing how to formulate the problem, design the algorithm, critically validate the idea through experimental designs and finally clearly present and communicate the findings. Students are expected to read, present, and debate the latest research papers on embodied AI, as well as obtain hands-on experience through the course projects.