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ChatGPT via AR and sleep rhythms in neurodegenerative disease

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“RizzGPT: Enhancing charisma with augmented reality and GPT-4”

Speakers: Bryan Chiang, Undergraduate Student in Computer Science, Stanford University, and Alix Cui, Undergraduate Student in Computer Science, Stanford University

Abstract: We will be sharing our project, RizzGPT, that recently blew up on social media. RizzGPT harnesses the capabilities of Brilliant Labs' cutting-edge AR device, Monocle, and OpenAI's GPT to create novel interfaces that immerse users in a more engaging and informative manner. With Monocle, users can experience an augmented reality environment that overlays digital information onto their physical world. The device is a compact and portable tool that empowers innovative hacking approaches, facilitating creative ventures that transform user experience. On the other hand, OpenAI's GPT, a state-of-the-art language model that integrates deep learning, enhances information processing and natural language understanding. By combining the capabilities of Monocle and GPT, RizzGPT has the potential to craft interfaces that captivate and engage users in a truly novel way.


“Characterizing sleep-wake rhythms in neurodegenerative disease”

Speakers: Jospeh Winer, Postdoctoral Research Fellow, Neurology, Stanford University

Abstract: Disrupted sleep and fragmentation of sleep-wake rhythms are common in the context of healthy aging as well as in neurodegenerative disease, resulting from a combination of physiological, societal, and behavioral factors. As wearable devices become increasingly popular among older adults, there is a need to understand how the measurement of sleep-wake behavior might be able to identify neurodegenerative disease and differentiate disease etiology and severity. This talk, focusing on healthy aging, Alzheimer’s disease, and Parkinson’s disease, will review actigraphy-estimated 24-hour rhythm and sleep impairment in these populations, both in a large epidemiological sample and our own deeply phenotyped Stanford cohort, in order to illustrate how wearables offer the opportunity to utilize sleep-wake impairment as a tool for early detection of disease and a potential treatment target.

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