deaf child ren
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The Download: OpenAI's top scientist on AGI, and gene therapy to restore hearing
Ilya Sutskever, OpenAI's cofounder and chief scientist, is no longer focusing on building the next generation of his company's flagship generative AI models. Instead his new priority is to figure out how to stop an artificial superintelligence (a hypothetical future technology he sees coming with the foresight of a true believer) from going rogue. A lot of what Sutskever says is wild. But not nearly as wild as it would have sounded just one or two years ago. He thinks ChatGPT just might be conscious (if you squint).
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Language Is the Scaffold of the Mind - Issue 76: Language
Can you imagine a mind without language? More specifically, can you imagine your mind without language? Can you think, plan, or relate to other people if you lack words to help structure your experiences? Many great thinkers have drawn a strong connection between language and the mind. Oscar Wilde called language "the parent, and not the child, of thought"; Ludwig Wittgenstein claimed that "the limits of my language mean the limits of my world"; and Bertrand Russell stated that the role of language is "to make possible thoughts which could not exist without it."
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5 Ways In Which AI Is Improving Accessibility For The Hearing Impaired
With the potential of AI permeating all aspects of our lives, the scope of the technology to help people with hearing disability has increased. Multiple wearable devices with artificial intelligence AI, ML and NLP embedded in it are available in the market, making the lives of people with hearing disability easier. Language translation and captioning: Tech giants are already working in the field as part of its larger corporate social responsibility programme. Microsoft, as part of its inclusive mission, has developed headsets with its embedded AI-powered communication technology, Microsoft Translator for hearing impaired. The system uses automatic speech recognition to convert raw spoken language – ums, stutters and all – into fluent, punctuated text.
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Machine learning algorithm uses brain scans to predict language ability in deaf children
In a new international collaborative study between The Chinese University of Hong Kong and Ann & Robert H. Lurie Children's Hospital of Chicago, researchers created a machine learning algorithm that uses brain scans to predict language ability in deaf children after they receive a cochlear implant. This study's novel use of artificial intelligence to understand brain structure underlying language development has broad reaching implications for children with developmental challenges. It was published in the Proceedings of the National Academy of Sciences of the United States of America. "The ability to predict language development is important because it allows clinicians and educators to intervene with therapy to maximize language learning for the child," said co-senior author Patrick C. M. Wong, PhD, a cognitive neuroscientist, professor and director of the Brain and Mind Institute at The Chinese University of Hong Kong. "Since the brain underlies all human ability, the methods we have applied to children with hearing loss could have widespread use in predicting function and improving the lives of children with a broad range of disabilities" said Wong.
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Evaluations of the LODE Temporal Reasoning Tool with Hearing and Deaf Children
Arfé, Barbara (University of Verona) | Gennari, Rosella (Free University of Bozen-Bolzano) | Mich, Ornella (Free University of Bozen-Bolzano and FBK-irst)
LODE is a web tool for children that are novice readers, and is primarily meant for deaf children. It proposes written stories and interactive games for reasoning, globally, on the stories. In this paper, first, we motivate the rationale of LODE, and explain its reasoning games. Then we briefly describe the design of the web client-server architecture of LODE; the server employs a constraint programming system for creating and solving the LODE games in real time. Finally, we concentrate on two evaluations of the latest prototype of LODE: one with hearing novice readers; another one with deaf readers. We conclude by discussing the results of the evaluations, and their implications for LODE.
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