Tuesday, December 21, 2021

g-f(2)747 THE BIG PICTURE OF THE DIGITAL AGE (12/21/2021), MIT News, Characters for good, created by artificial intelligence




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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence, Personalized learning (12/21/2021)  g-f(2)426 


OPPORTUNITIES, MIT News

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Characters for good, created by artificial intelligence


  • As it becomes easier to create hyper-realistic digital characters using artificial intelligence, much of the conversation around these tools has centered on misleading and potentially dangerous deepfake content. But the technology can also be used for positive purposes — to revive Albert Einstein to teach a physics class, talk through a career change with your older self, or anonymize people while preserving facial communication.
  • To encourage the technology’s positive possibilities, MIT Media Lab researchers and their collaborators at the University of California at Santa Barbara and Osaka University have compiled an open-source, easy-to-use character generation pipeline that combines AI models for facial gestures, voice, and motion and can be used to create a variety of audio and video outputs.
    • By making this pipeline easily available, the researchers hope to inspire teachers, students, and health-care workers to explore how such tools can help them in their respective fields. If more students, educators, health-care workers, and therapists have a chance to build and use these characters, the results could improve health and well-being and contribute to personalized education, the researchers write in Nature Machine Intelligence.


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AI-generated characters for supporting personalized learning and well-being





References



g-f(2)740 THE BIG PICTURE OF THE DIGITAL AGE (12/18/2021), MIT Media Lab, AI-generated characters for supporting personalized learning and well-being



Extra-condensed knowledge



Lessons learned, MIT News 

Nature Machine Intelligence
AI-generated characters for supporting personalized learning and well-being


Abstract

  • Advancements in machine learning have recently enabled the hyper-realistic synthesis of prose, images, audio and video data, in what is referred to as artificial intelligence (AI)-generated media. These techniques offer novel opportunities for creating interactions with digital portrayals of individuals that can inspire and intrigue us. AI-generated portrayals of characters can feature synthesized faces, bodies and voices of anyone, from a fictional character to a historical figure, or even a deceased family member. Although negative use cases of this technology have dominated the conversation so far, in this Perspective we highlight emerging positive use cases of AI-generated characters, specifically in supporting learning and well-being. We demonstrate an easy-to-use AI character generation pipeline to enable such outcomes and discuss ethical implications as well as the need for including traceability to help maintain trust in the generated media. As we look towards the future, we foresee generative media as a crucial part of the ever growing landscape of human–AI interaction.



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Lessons learned, MIT News 


  • It will be a strange world indeed when AIs and humans begin to share identities. This paper does an incredible job of thought leadership, mapping out the space of what is possible with AI-generated characters in domains ranging from education to health to close relationships, while giving a tangible roadmap on how to avoid the ethical challenges around privacy and misrepresentation,” says Jeremy Bailenson, founding director of the Stanford Virtual Human Interaction Lab, who was not associated with the study.
  • Although the world mostly knows the technology from deepfakes, “we see its potential as a tool for creative expression,” says the paper’s first author Pat Pataranutaporn, a PhD student in professor of media technology Pattie Maes’ Fluid Interfaces research group.  
  • Other authors on the paper include Maes; Fluid Interfaces master’s student Valdemar Danry and PhD student Joanne Leong; Media Lab Research Scientist Dan Novy; Osaka University Assistant Professor Parinya Punpongsanon; and University of California at Santa Barbara Assistant Professor Misha Sra.
  • Deeper truths and deeper learning
    • Generative adversarial networks, or GANs, a combination of two neural networks that compete against each other, have made it easier to create photorealistic images, clone voices, and animate faces. 
    • Self-exploration is only one of the positive applications of AI-generated characters, the researchers say. 
    • Experiments show, for instance, that these characters can make students more enthusiastic about learning and improve cognitive task performance. The technology offers a way for instruction to be “personalized to your interest, your idols, your context, and can be changed over time,” Pataranutaporn explains, as a complement to traditional instruction.


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References


“genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)747, Fernando Machuca, December 21, 2021, blog.geniouxfacts.comgeniouxfacts.comGenioux.com Corporation.


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