Buddhism & Technology: Historical Background and Contemporary Challenges – Abstracts

Click here to return to the main conference page.

  1. Marcus Bingenheimer (Temple University)

    Abstract forthcoming

  2. Justin Brody (Goucher College)

    Abstract forthcoming

  3. Douglas Samuel Duckworth (Temple University)
  4. Charles Goodman (Binghamton University)

    Abstract forthcoming

  5. Gregory Greive (University of North Carolina)
  6. Paul Hackett (Independent scholar)
  7. Natasha Heller (Virginia)

    Abstract forthcoming

  8. Peter Hershock (East West Center)

    Abstract forthcoming

  9. Matthew King (University of California, Riverside)
  10. Jeffrey Kotyk (McMaster University)
  11. Bill Magee (Maitripa Institute)
  12. Bill Mak (Kyoto University)
  13. Beverley McGuire (University of North Carolina Wilmington)

    Abstract forthcoming

  14. Sebastian Nehrdich (Hamburg University)
  15. Stuart Ray Sarbucker (Oregon State)
  16. Joshua Stoll (University of Hawai‘i West O‘ahu): Buddha in the Chinese Room: Empty Persons, Other Mindstreams, and the Strong AI Debate

    This paper will analyze the concept of strong AI and the debate surrounding John Searle’s Chinese Room argument from a Buddhist point of view. First, we will explore the debate on the Chinese Room argument’s implications for the possibility of strong AI. Then we will delve into Buddhist arguments for the selflessness of persons (pudgalanairātmya) as well as the status of other mindstreams (santānāntara) and Buddha’s omniscience (sarvajnātva) as it relates to the possibility of his cognition of other psychophysical continua (santānāntara). Finally, the implications of these Buddhist considerations will be introduced into the debate regarding Searle’s thought experiment in order to develop an understanding of strong AI from a Buddhist perspective.

  17. Panyadipa Tan (Shan State Buddhist University, Taunggyi, Myanmar): Rediscovering the Buddha’s wisdom in Scientific Age: Buddhist Meditation and Research on Psychosomatic Health and Longevity

    A major factor that makes Buddhism appeal to modern people contributing to its growth in the West is its empirical teachings which invite investigations of truths by reason and direct experience. This unassuming pragmatic spirit distinguishes Buddhism from other religions, making it particularly relevant in this era where evidence-based scientific inquiry becomes hegemonic to discovery of knowledge. In recent decades much collaborative effort in scientific research has been initiated between Buddhist practitioners and scientists. Accumulating evidence from such collaboration not only attests to the potential health benefits of Buddhist meditations, but has also greatly helped edifying Buddhism as a religion that confers demonstrable mental and physical wellbeing. Today, the Buddhist-inspired mindfulness meditation has increasingly been adopted into integrative and mainstream healthcare systems. Its possible roles in managing stress, affective mood disorders and various forms of pain and addiction have been considerably recognized. In addition to this, a plethora of psychosomatic effects potentially against chronic and degenerative diseases have also been reported for certain forms of Buddhist meditation. Based upon hundreds of peer-reviewed clinical studies on Buddhist meditation interventions in public science repositories, this paper aims to give an up-to-date, systematic appraisal on how Buddhist meditation affects psychosomatic and longevity indices. The possible biological mechanisms involved in such effects will be briefly discussed. Relevant excerpts and legends from ancient Buddhist texts on how mental cultivations might help ameliorate physical affliction and illnesses will also be referenced. Finally, the caveats and apparent shortcomings of current scientific findings in informing the ethical and spiritual goals of Buddhism will be addressed. I hope this paper will keep Buddhist scholars of non-medical background abreast with state-of-the-art medical research findings in Buddhist meditation. Through continual collaboration across disciplines, Buddhism and science are ready to continue work with each other toward a balanced and sustainable technological progress that weighs in fundamental ethical and humanistic values.

  18. Yu-chun Wang (Dharma Drum Institute for Liberal Arts)

    Abstract forthcoming

  19. Mei Wang (University of Science and Technology of China): “Study on Artificial-Intelligence-Based Buddhist Murals Restoration,” 基于人工智能技术的佛教壁画类文化遗产修复研究

    壁画作为佛教文化艺术遗产的缩影,是佛教数字化研究的重要样本。早在人工智能技术风靡之前,以敦煌莫高窟壁画为代表的佛教壁画类文化遗产就在数字化研究上经历了长期探索。进入人工智能时代之后,借助前沿科技,壁画数字化工作将打开新的契机。本文将以佛教壁画为例,探讨将以深度学习(Deep Learning,DL)为基础的人工智能技术用于佛教文化遗产保护的可能,包括:壁画的破损补全修复、断代、历史断层风格生成等。

    深度学习是人工智能研究领域下的一种机器学习方法,原理是对数据进行表征学习来模拟人脑神经结构,其广泛运用于图像处理研究,近两年亦逐步应用于文化遗产保护工作。其中生成式对抗网络(Generative Adversarial Networks,GAN)作为近年来流行的一种深度学习模型,主要用于图像的生成,在宗教艺术作品的数字化修复方面发挥了显著作用,对壁画乃至唐卡的补全、风格生成等都具有重要的参考价值。

    本文将针对目前敦煌壁画修复工作的主要关注点,基于人工智能的深度学习技术发展现状,结合相关算法的特点和应用范围,研究其辅助敦煌壁画修复工作的可行性和前景,推动佛教文化有效传播。

    This paper aims to promote the integration of latest technology and Buddhist art and culture, focusing on the feasibility and accessibility of Artificial Intelligence (AI) assisted Buddhist mural restoration in East Asia. Long before the diffusion of AI technology, Buddhist murals from Mogao Caves has undergone long-term exploration for its digitization. After entering the so-called AI era, study of the digitization of murals could be brought to a brand-new stage. Therefore, this paper takes Buddhist murals as a case to explore the possibility of using AI technology based on Deep Learning (DL) to preserve Buddhist arts. DL is known as a Machine Learning (ML) method under AI-related research, which is widely used in image recognition and generation. Generative Adversarial Networks (GAN), as a popular DL model, is widely used in image generation and could be used in restoring and generating broken murals and Thangka. Tasks of murals preservation using AI technology include broken image restoration, ancient paintings dating, lost historic painting style generation.

  20. Christian Wittern (Kyoto University)

    Abstract forthcoming