Studies on the use o f AI tools in CS course s focus on prescriptive uses of AI tools that preemptively assign a use case to the tool. To our knowledge, this is the first user study that surveys CS students on how they use AI tools for their coding tasks by personal choice. We surveyed 26 CS students and practitioners with various programming experiences using AI tools for coding tasks (i.e., writ e, debug, etc.). When asked about the most common resources they have used to write a 300 - line program from scratch, blog entries ( e.g., Stack Exchange, etc.) were their top choice, followed by AI coding assistants (e.g., GitHub Copilot, etc.). When asked about resources to debug code, AI chatbots (e.g., ChatGPT, etc.) were the most common choice, followed by blog entries. AI coding assistants are used more for writing code, while AI chatbots are used for debugging tasks. Respondents with all programming experience prefer online resources for coding tasks - whether AI - powered or not - rather than direct human help from peers an d instructors.

Large Language Models (LLMs) like GPT and Bard are capable of producing code based on textual descriptions, with remarkable efficacy. Such technology will have profound implications for computing education, raising concerns about cheating, excessive dependence, and a decline in computational thinking skills, among others. There has been extensive research on how teachers should handle this challenge but it is also important to understand how students feel about this paradigm shift. In this research, 52 first-year CS students were surveyed in order to assess their views on technologies with code-generation capabilities, both from academic and professional perspectives. Our findings indicate that while students generally favor the academic use of GPT, they don't over rely on it, only mildly asking for its help. Although most students benefit from GPT, some struggle to use it effectively, urging the need for specific GPT training. Opinions on GPT's impact on their professional lives vary, but there is a consensus on its importance in academic practice.

Digital technologies for learning, health, politics, and commerce have enriched the world. Digital heroes like Sir Tim Berners-Lee, Batya Friedman, Alan Kay, JCR Licklider, and Joe Weizenbaum have blazed trails. We depend upon software that nobody totally understands. We are vulnerable to cyberterrorism. Privacy is overrun by surveillance capitalism.7 Totalitarian control advances. Daily Internet news matching our beliefs makes it difficult to tell true from false.

Federal administrative agencies across the United States employ machine learning and artificial intelligence to make decisions. But what happens when agencies can't explain how those algorithms work? Students in a policy lab at Stanford, Administering by Algorithm: Artificial Intelligence in the Regulatory State, are exploring this question and what it means for the future when law and computers intersect. Stanford co-instructors David Engstrom, Daniel Ho and California Supreme Court Justice Mariano-Florentino Cuéllar – along with Professor Catherine Sharkey of New York University School of Law – have brought together 25 burgeoning lawyers, computer scientists and engineers to probe the technologies government agencies develop and deploy. The lab culminates in a report that will be submitted to the Administrative Conference of the United States (ACUS), which puts forward guidelines outlining how such government agencies should operate.

The Robotics Project (the "Hand-Eye Project") evolved within the Stanford Artificial Intelligence Laboratory under the guidance of John McCarthy, Les Earnest, Jerry Feldman, and Tom Binford. Major efforts have been undertaken to isolate and solve fundamental problems in computer vision, manipulation, and autonomous vehicles. Generalized cones were introduced for modeling the geometry of 3-dimensional objects, and programs were constructed which learned structural descriptions of objects from laser-ranging data ("structured light"). Stereo vision and texture have been examined. Several generations of robot programming languages have resulted in AL, an intermediate-level language for commanding manipulation. A computer-controlled roving vehicle ("the cart") detected obstacles (using 9-eyed stereo) and planned paths to avoid them.