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<p class="MsoNormal">-----Original Appointment-----<br>
<b>From:</b> Hunter, Tiffany <br>
<b>Sent:</b> Tuesday, August 23, 2022 3:39 PM<br>
<b>To:</b> Hunter, Tiffany; Abukamail, Nasseef; Chelberg, David; Juedes, David; Kelsey, Ralph; Liu, Chang; Liu, Jundong; Mourning, Chad; Ostermann, Shawn; Welch, Lonnie; Bartone, Chris; Braasch, Michael; Chenji, Harshavardhan; Irwin, Dennis; Jadwisienczak,
Wojciech; Kaya, Savas; Lawrence, Douglas; Rahman, Faiz; Ugazio, Sabrina; Van Graas, Frank; Vasiliadis, Konstantinos; Zhu, Jim; Allwine, Daniel; Goble, James; Kelley, Michael; Triplett, Joseph; Schlicher, Jared; Alder, Frank; DiBenedetto, Mike; Edwards, Jamie;
Hawes, Carl; Hopkins, Tammy; Nilles, Paul; Odunaiya, Simbo; Riggs, Cathy; Schultz, Adam; Thomas, Mary; Wilson, Aaron; Dolan, John; Karanth, Avinash; eecs_msee@listserv.ohio.edu; eecs_mscs@listserv.ohio.edu; 'eecs_phd@listserv.ohio.edu'; 'eecs_bscs@listserv.ohio.edu';
'eecs_bsee@listserv.ohio.edu'<br>
<b>Subject:</b> Research seminar-Professor Fujiyoshi (Chubu Glidden Visiting Professor)-Beyond Explainable AI: Co-evolution of AI and Human<br>
<b>When:</b> Friday, August 26, 2022 11:50 AM-12:45 PM (UTC-05:00) Eastern Time (US & Canada).<br>
<b>Where:</b> Stocker 103 and; Microsoft Teams Meeting<o:p></o:p></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#222222;background:white">Title:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#222222;background:white">Beyond Explainable AI: Co-evolution of AI and Human</span><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#222222"><br>
<br>
<span style="background:white">Abstract:<o:p></o:p></span></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#222222"><br>
<span style="background:white">Artificial intelligence systems using deep learning have achieved image and speech recognition at performance levels on par with human beings. However, an issue is not being able to understand the basis on which a deep learning
system determines its output. In this talk, we introduce Attention Branch Network (ABN), which outputs an attention map, an area that deep learning focuses on when determining inferential results. ABN is a deep learning network that can contribute to improving
recognition performance while acquiring the attention mechanism. As application examples of ABN, we introduce ABN’s visual explanation of medical diagnosis decisions. Visualization of attention here means viewing the AI’s direction of attention. This technology
holds great promise as an approach for interpreting the basis for decisions output by an AI system. We also discuss the following topic for AI, such as the co-evolution of AI and humans.</span></span><o:p></o:p></p>
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<p class="MsoNormal">Bio: <o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#222222;background:white">Dr.<span class="apple-converted-space"> Fujiyoshi
</span>received his Ph.D. in Electrical Engineering from Chubu<span class="apple-converted-space"> </span>University, Japan, in 1997. From 1997 to 2000 he was a post-doctoral fellow at the Robotics Institute of Carnegie Mellon University, Pittsburgh, PA, USA,
working on the DARPA Video Surveillance and Monitoring (VSAM) effort and the humanoid vision project for the HONDA Humanoid Robot. He is now a professor of the Department of Robotics, Chubu University, Japan. From 2005 to 2006, he was a visiting researcher
at Robotics Institute, Carnegie Mellon University. <br>
<br>
Homepage:<br>
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