日時: 2024/8/5(月曜) 15:00-16:00
会場: 京都大学 本部構内 総合研究9号館 北館 2F N2講義室
(以下のマップの63番の建物です)
https://www.kyoto-u.ac.jp/ja/access/campus/yoshida/map6r-y

Title: Quest for Scaling Earthly AI Holistically
- Advancing AI to help children with speech and language service needs

Speaker: Professor Dr. Jinjun Xiong (University at Buffalo)

Abstract:
AI has become an increasingly powerful technology force that promises
to impact all aspects of our society, from transportation to
healthcare and education to sustainability.  But the diverse layers of
software abstractions, hardware heterogeneity, and data privacy
concerns have made the development of optimized AI solutions extremely
challenging. This results in the business world’s expensive investment
only on a handful of selective and “profitable” AI solutions, leaving
many critical societal needs, such as equitable education and
sustainability, much less addressed than deserved. For example, nearly
3.4 million children in the U.S. require speech and language services
under the Individuals with Disabilities Education Act (IDEA) and are
at risk of falling behind in their academic and social-emotional
development without timely intervention by Speech and Language
Pathologists (SLPs). Unfortunately, there is a significant shortage of
SLPs and the COVID pandemic has further exacerbated this gap, making
it almost impossible for SLPs to provide individualized services for
children.

Through the recently established NSF National AI Institute for
Exceptional Education, we envision a transformative approach to
address this challenge. We aim to develop advanced AI technologies to
scale SLPs’ availability and services such that no child in need of
speech and language services is left behind. Towards this end, we
propose to develop two novel AI solutions: (1) AI Screener to enable
universal early screening for all children, and (2) AI Orchestrator to
work with SLPs and teachers to provide individualized interventions
for children with their formal Individualized Educational Plan (IEP).
In developing these solutions, we will advance foundational AI
technologies, enhance our understanding of children’s speech and
language development, serve as a nexus point for all special education
stakeholders, and represent a fundamental paradigm shift in how SLPs
serve children in need of ability-based speech and language services.

In this talk, I will discuss the rationale behind the Institute’s
vision, the technical approaches we are taking, and the corresponding
research challenges we must overcome. I will contextualize these
efforts in an ultimate research goal of transforming the current AI
innovation ecosystem to truly democratize AI for a better society.
This also opens up new opportunities for collaboration with a broad
research community in areas of foundational AI, workload acceleration,
system characterization and optimization, and AI automation.


Bio:
Dr. Jinjun Xiong is Empire Innovation Professor with the Department of
Computer Science and Engineering at University at Buffalo (UB). He
also serves as the Scientific Director for the $20 M National AI
Institute for Exceptional Education (http://ai4exceptionaled.org), and
Director for the SUNY-UB Institute for Artificial Intelligence and
Data Science (https://www.buffalo.edu/ai-data-science.html). Prior to
that, he was a Senior Researcher and Program Director for AI and
Hybrid Clouds Systems at the IBM Thomas J. Watson Research Center. He
was the former co-founder and co-director for the IBM-Illinois Center
for Cognitive Computing Systems Research (C3SR), the success of which
in 5 years has led to the 10-year $200M expansion of the center to the
IBM-Illinois Discovery Accelerator Institute. His research interests
are on across-stack AI systems research, including AI applications,
algorithms, tooling and computer architectures.  Many of his research
results have been adopted in IBM’s products and tools. He published
more than 160 peer-reviewed papers in top AI conferences and systems
conferences. His publication won 9 Best Paper Awards and 9 Nominations
for Best Paper Awards. He also won top awards from various
international competitions, including the championship award for the
IEEE GraphChallenge on accelerating sparse neural networks in 2020,
and the First Place Awards for the 2019 DAC Systems Design Contest on
designing an object detection neural network for edge FPGA and GPU
devices, respectively.

参加費:  無料
参加申し込み: 不要

主催 京都大学 情報学研究科 集積システム工学講座
共催 IEEE CAS 関西チャプター

問い合わせ先
橋本昌宜 (京都大学) hashimoto@i.kyoto-u.ac.jp