1 - 10 of 82 results for: artificial intelligence

From self-driving cars to bots that alter democratic elections, artificial intelligence is growing increasingly powerful and prevalent in our everyday lives. Literature has long been speculating about the techno-utopia¿and catastrophe¿that A.I. could usher in. Indeed, literature itself presents us with a kind of A.I. in the many characters that speak and think in its pages. But how do we classify an intelligence as ¿artificial¿ or not? Is there a clear boundary that demarcates bodies from machines? What, if anything, separates the ¿genre¿ of technology from that of literature? What classifies literature as ¿science fiction,¿ ¿scientific,¿ ¿futuristic,¿ ¿psychological,¿ or ¿dystopian¿? And can technology or literature ever overcome the ultimate division between all intelligences¿the problem of other minds? This course consists in curated multi-genre combinations of literature, philosophy, film, and television that explore what makes someone¿or something¿a person in our world today. Special events will include celebrating the current bicentennial of Mary Shelley¿s Frankenstein (1818) in Stanford Special Collections; a possible visit to Stanford¿s A.I. Laboratory; and chatting with the ELIZA chatbot.

From Snapchat to artificial intelligence, digital systems are reshaping our jobs, our democracies, our love lives, and even what it means to be human. But where did these media come from? And what kind of culture are they creating? To answer these questions, this course explores the entwined development of digital technologies and post-industrial ways of living and working from the Cold War to the present. Topics will include the historical origins of digital media, cultural contexts of their deployment and use, and the influence of digital media on conceptions of self, community, and state. Priority to juniors, seniors, and graduate students.

This course seeks to divert attention away from bleak fantasies of an impending AI apocalypse that would be unleashed by ¿the blind and irresponsible advent of oppressively dehumanizing technology¿ and instead highlight the oppressive ¿human¿ elements that structure how AI is imagined, researched, designed, produced and utilized. The aim of the course is to analyze how culture at large influences the development of AI and how, or to what extent, AI reproduces political and moral structures of human societies.nnWhat makes us, and even Silicon Valley tycoons, become afraid of science-fictional fantasies of nonhuman villains to wipe the human race, while we easily shrug off rampant racism or sexism that is reproduced and reinforced by ¿algorithms of oppression¿? What kind of political and cultural elements influence the mostly invisible political economy of how AI, machine learning and deep learning is designed, produced and utilized as a commodity by some of the most powerful corporations in contemporary global economy? In short, how does human culture at large configure within the scientific and technological research into and development of non-human intelligence?nnAnthropology has a long history of researching about human-technology interaction and often joins forces with History of Science and Science and Technology Studies. In that spirit, we will cover a wide array of literature on the historical development of academic research on cognitive science, philosophy of mind, consciousness, machine learning, deep learning, cybernetics and robotics. However, the primary aim of the course is to offer a meta-perspective on the ¿cultural aspects¿ of how these topics have been studied and practiced by entrepreneurs, research scientists, engineers, philosophers and futurists, and not the disciplinary knowledge generated by research on these topics.nnApart from ethnographic and historic researches about how AI is studied and produced, we will utilize works by theoretical cultural critics, historians and philosophers, like Bruno Latour, Donna Haraway, Michel Foucault, as well as Gilbert Ryle, Daniel Dennett and David Chalmers. Furthermore, we will heavily rely on cultural images, fantasies and narratives about artificial intelligence in literature, arts and cinema. To that effect, we will watch a wide array of movies and will interactively analyze these cultural works in class, asking to what extent they represent actual research into and development of AI.

This course will explore possibilities for student research projects based on presentations of faculty research. We will cover a broad range of topics within the general area of collective behavior, both natural and artificial. Students will build on faculty presentations to develop proposals for future projects.

Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare.

Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare.

Through weekly lectures given by prominent researchers, practicing professionals, and entrepreneurs, this class will examine important industry problems and critically assess corresponding AI directions in both academia and industry. Students will gain an understanding of how AI can be used to provide solutions in the architecture, engineering, and construction industry and asses the technology, feasibility, and corresponding implementation effort. Students are expected to participate actively in the lectures and discussions, submit triweekly reflection writings, and present their own evaluation of existing solutions. Enrollment limited to 12 students.

Through weekly lectures given by prominent researchers, practicing professionals, and entrepreneurs, this class will examine important industry problems and critically assess corresponding AI directions in both academia and industry. Students will gain an understanding of how AI can be used to provide solutions in the architecture, engineering, and construction industry and asses the technology, feasibility, and corresponding implementation effort. Students are expected to actively prepare for and participate in all lectures and corresponding discussions.

Can artificial intelligence (AI) reduce pollution? alleviate poverty? predict natural disasters? cure diseases? The AI for Good seminar series will explore ways that artificial intelligence can benefit society and our planet while examining the unintended consequences of these new technologies. This seminar series by academic, industry, government, and NGO leaders, who are involved in AI for social impact and responsible AI, aims to inspire undergraduate and graduate students to incorporate AI and data science into their learning. Experts will delve into AI applications and opportunities in fields ranging from healthcare, law, the environment, education, technology, government and more; will highlight challenges regarding fairness, bias, privacy, ethics, etc., and how these issues are beginning to be addressed. Interested students will have access to supplemental materials such as mini case studies and Jupyter notebooks. Speakers and more at https://icme.stanford.edu/ai4good.