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DTSTART;TZID=America/Los_Angeles:20251009T173000
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DESCRIPTION:Abstract: As artificial intelligence (AI) continues to scale in
  power and influence\, modern models increasingly mirror the complexities 
 of human cognition. Yet\, a widening gap exists between the principles of 
 biological memory and the constraints of contemporary computing systems. T
 his talk\, How Memory Powers Intelligence: From Cognition to AI Computing 
 Systems\, explores this intersection through the lens of memory. We presen
 t research that reimagines intelligent systems not just as data processors
 \, but as experience-driven learners. Key questions include: How can syste
 ms retain and learn from past interactions? How can we prioritize what inf
 ormation is worth encoding? What strategies enable efficient consolidation
  of long-term knowledge? And how can we accelerate memory retrieval to sup
 port real-time reasoning? By drawing parallels between cognitive science a
 nd AI architecture\, we highlight novel memory-centric approaches to unloc
 king more intelligent\, scalable\, and adaptive computing systems.\n\nSpea
 ker(s): Prof. Hai (Helen) Li\n\nAgenda: \n5:30PM-6:15PM Presentation\n\n6:
 15PM-6:30PM Q&A\n\nVirtual: https://events.vtools.ieee.org/m/499690
LOCATION:Virtual: https://events.vtools.ieee.org/m/499690
ORGANIZER:appleloving@gmail.com
SEQUENCE:4
SUMMARY:How Memory Powers Intelligence: From Cognition to AI Computing Syst
 ems
URL;VALUE=URI:https://events.vtools.ieee.org/m/499690
X-ALT-DESC:Description: <br /><p class="xmsonormal"><strong><span style="fo
 nt-size: 11.0pt\;">Abstract: </span></strong><span style="font-family: 'Ti
 mes New Roman'\,serif\;">As artificial intelligence (AI) continues to scal
 e in power and influence\, modern models increasingly mirror the complexit
 ies of human cognition. Yet\, a widening gap exists between the principles
  of biological memory and the constraints of contemporary computing system
 s. This talk\, How Memory Powers Intelligence: From Cognition to AI Comput
 ing Systems\, explores this intersection through the lens of memory. We pr
 esent research that reimagines intelligent systems not just as data proces
 sors\, but as experience-driven learners. Key questions include: How can s
 ystems retain and learn from past interactions? How can we prioritize what
  information is worth encoding? What strategies enable efficient consolida
 tion of long-term knowledge? And how can we accelerate memory retrieval to
  support real-time reasoning? By drawing parallels between cognitive scien
 ce and AI architecture\, we highlight novel memory-centric approaches to u
 nlocking more intelligent\, scalable\, and adaptive computing systems.</sp
 an></p><br /><br />Agenda: <br /><p>5:30PM-6:15PM Presentation</p>\n<p>6:1
 5PM-6:30PM Q&amp\;A</p>
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