BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210408T164016Z UID:C8D8D4C9-F263-4600-88BF-09EBC223F9B8 DTSTART;TZID=America/Los_Angeles:20210408T080000 DTEND;TZID=America/Los_Angeles:20210408T093000 DESCRIPTION:Machine capability has reached an inflection point\, achieving human-level performance in tasks traditionally associated with cognition ( vision\, speech\, strategic gameplay). However\, efforts to move such capa bility into the real world\, where it can pervasively integrate in our liv es\, have in many cases fallen far short of the relatively constrained and isolated demonstrations of success. A major insight emerging is that stru cture in data can be substantially exploited to enhance machine learning. This talk explores how the statistically-complex processes of the real wor ld can be addressed by preforming sensing in ways that preserve the rich s tructure of the real world. This evokes questions like: what sorts of stru cture are useful\; what sorts of models can exploit such structure\; what sensing technologies enable such structure\; what computational architectu res are required to harness such structure? While the many eventual applic ations of embedded AI are difficult to define\, these foundational questio ns can help us prepare for providing the technology platform required in t hose applications nonetheless. This talk will investigate the algorithmic and technological implications\, spanning from machine-perception models f or sensor fusion\, to large-scale form-fitting embedded sensors\, to mixed -signal architectures for in-memory computing.\n\nSpeaker(s): Prof. Naveen Verma\, \n\nAgenda: \nEvent address for attendees: https://ieeemeetings.w ebex.com/ieeemeetings/onstage/g.php?MTID=ec74b3863d0ed1ec3ffab89714795c1b5 \n\nSan Diego\, California\, United States\, Virtual: https://events.vtool s.ieee.org/m/267016 LOCATION:San Diego\, California\, United States\, Virtual: https://events.v tools.ieee.org/m/267016 ORGANIZER:jfshi@ieee.org SEQUENCE:2 SUMMARY:Thinking About the Technology Platform for Next-generation AI URL;VALUE=URI:https://events.vtools.ieee.org/m/267016 X-ALT-DESC:Description: <br /><p>Machine capability has reached an inflecti on point\, achieving human-level performance in tasks traditionally associ ated with cognition (vision\, speech\, strategic gameplay).&nbsp\; However \, efforts to move such capability into the real world\, where it can perv asively integrate in our lives\, have in many cases fallen far short of th e relatively constrained and isolated demonstrations of success. A major i nsight emerging is that structure in data can be substantially exploited t o enhance machine learning. This talk explores how the statistically-compl ex processes of the real world can be addressed by preforming sensing in w ays that preserve the rich structure of the real world. This evokes questi ons like: what sorts of structure are useful\; what sorts of models can ex ploit such structure\; what sensing technologies enable such structure\; w hat computational architectures are required to harness such structure? Wh ile the many eventual applications of embedded AI are difficult to define\ , these foundational questions can help us prepare for providing the techn ology platform required in those applications nonetheless. This talk will investigate the algorithmic and technological implications\, spanning from machine-perception models for sensor fusion\, to large-scale form-fitting embedded sensors\, to mixed-signal architectures for in-memory computing. </p><br /><br />Agenda: <br /><table class="TblBgColor" border="0" width=" 100%" cellspacing="0" cellpadding="3">\n<tbody>\n<tr class="TblContentFont 3">\n<td valign="top" nowrap="nowrap"><strong>Event address for attendees: </strong></td>\n<td valign="top" nowrap="nowrap"><a id="attendeeURL" href= "https://ieeemeetings.webex.com/ieeemeetings/onstage/g.php?MTID=ec74b3863d 0ed1ec3ffab89714795c1b5">https://ieeemeetings.webex.com/ieeemeetings/onsta ge/g.php?MTID=ec74b3863d0ed1ec3ffab89714795c1b5</a></td>\n</tr>\n</tbody>\ n</table> END:VEVENT END:VCALENDAR