BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260124T050259Z UID:5DFD513C-7EB9-4824-9A9D-6645FB4378B9 DTSTART;TZID=Etc/UTC:20250717T120000 DTEND;TZID=Etc/UTC:20250717T130000 DESCRIPTION:[Infrastructure for Physical AI]\n\nSpecial Presentation by Bil l McFarland (Ramen Inc.\, USA)\n\nHosted by the Future Networks AI/ML and Apps & Services working groups\n\nDate/Time: Thursday\, 17 July 2025 @ 12: 00 UTC (12 PM GMT)\n\nTopic:\n\nInfrastructure Enabling the Physical AI Re volution\n\nAbstract:\n\nWhile generative AI’s automation of office work has captured the headlines recently\, AI is just as dramatically transfor ming physical work. This “Physical AI” is being deployed in factories\ , agricultural facilities\, warehouses\, health care facilities\, and even tually will be deployed in nursing homes and private residences. This pape r describes the unique distributed AI architectures required in these envi ronments\, and the attendant networking infrastructure to support them. Th e environments themselves are large and often outdoors\, including warehou ses\, factory floors\, storage facilities\, lumber yards\, ports\, and far ms. Increasingly the work is performed by intelligent and flexible robotic s. The paper demonstrates the need for edge-based AI to meet the required reliability\, latency\, cost\, security\, and trust for these applications . Networking topologies typical of these environments are presented\, high lighting their uniquely complicated arrangement of hierarchical and parall el systems with periodic touch points\, together with cross network utiliz ation models. Evidence is presented that demonstrates that distributed loc al AI processing\, together with hybrid WiFi and cellular networking\, are the best solutions to providing the required performance. A case study of an agricultural processing facility is presented to make the concepts con crete.\n\nSpeaker:\n\n[]\nBill McFarland is an experienced engineering exe cutive\, currently acting as an advisor to several companies in the commun ications space. Bill was previously the CTO of Plume Design. At Plume he l ed projects in data science\, optimization\, standards\, intellectual prop erty\, and regulatory matters. Bill also served as Vice President of Techn ology at Qualcomm\, and CTO of Atheros Communications. Bill received a Bac helor’s in Electrical Engineering from Stanford University\, and a Maste r’s in Electrical Engineering from the University of California\, Berkel ey. Bill holds over 100 patents\, and has published over 35 technical pape rs. In 2014\, Bill was named a Fellow of the IEEE.\n\nCo-sponsored by: Fut ure Networks AI/ML and Apps & Services working groups\n\nVirtual: https:// events.vtools.ieee.org/m/489020 LOCATION:Virtual: https://events.vtools.ieee.org/m/489020 ORGANIZER:baw@ieee.org SEQUENCE:18 SUMMARY:Infrastructure Enabling the Physical AI Revolution URL;VALUE=URI:https://events.vtools.ieee.org/m/489020 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-top: .25in \;"><img src="https://events.vtools.ieee.org/vtools_ui/media/display/f7bc4 d8b-ba50-4dfa-8ee5-943b47fa7753" alt="Infrastructure for Physical AI" widt h="750" height="197"></p>\n<p class="MsoNormal" style="margin-top: 12.0pt\ ;">Special Presentation by<strong> Bill McFarland (Ramen Inc.\, USA)</stro ng></p>\n<p class="MsoNormal" style="margin-top: 12.0pt\;">Hosted by the F uture Networks <strong>AI/ML </strong>and <strong>Apps &amp\; Services</st rong> working groups</p>\n<p class="MsoNormal" style="margin-top: 12.0pt\; "><strong><span style="font-size: 14.0pt\; font-family: Copperplate\; mso- fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-fareast\; ms o-bidi-font-family: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ansi-lan guage: EN-US\; mso-fareast-language: ZH-TW\; mso-bidi-language: AR-SA\;">D ate/Time</span></strong><span style="font-size: 12.0pt\; font-family: 'Cal ibri'\,sans-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-f amily: PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme- font: minor-latin\; mso-bidi-font-family: Arial\; mso-bidi-theme-font: min or-bidi\; mso-ansi-language: EN-US\; mso-fareast-language: ZH-TW\; mso-bid i-language: AR-SA\;">: <strong>Thursday\, 17 July 2025</strong><strong> @ 12:00 UTC (12 PM GMT)</strong></span></p>\n<p class="MsoNormal" style="mar gin-top: .25in\;"><strong><u><span style="font-size: 16.0pt\; font-family: Copperplate\;">Topic</span></u></strong><strong><span style="font-size: 1 6.0pt\; font-family: Copperplate\;">:</span></strong></p>\n<p class="MsoNo rmal"><strong><span style="font-size: 16pt\;">Infrastructure Enabling the Physical AI Revolution&nbsp\;</span></strong></p>\n<p class="MsoNormal" st yle="margin-top: .25in\;"><strong><u><span style="font-size: 16.0pt\; font -family: Copperplate\;">Abstract</span></u></strong><strong><span style="f ont-size: 16.0pt\; font-family: Copperplate\;">:</span></strong></p>\n<p c lass="MsoNormal">While generative AI&rsquo\;s automation of office work ha s captured the headlines recently\, AI is just as dramatically transformin g physical work. This &ldquo\;Physical AI&rdquo\; is being deployed in fac tories\, agricultural facilities\, warehouses\, health care facilities\, a nd eventually will be deployed in nursing homes and private residences. Th is paper describes the unique distributed AI architectures required in the se environments\, and the attendant networking infrastructure to support t hem. The environments themselves are large and often outdoors\, including warehouses\, factory floors\, storage facilities\, lumber yards\, ports\, and farms. Increasingly the work is performed by intelligent and flexible robotics. The paper demonstrates the need for edge-based AI to meet the re quired reliability\, latency\, cost\, security\, and trust for these appli cations. Networking topologies typical of these environments are presented \, highlighting their uniquely complicated arrangement of hierarchical and parallel systems with periodic touch points\, together with cross network utilization models. Evidence is presented that demonstrates that distribu ted local AI processing\, together with hybrid WiFi and cellular networkin g\, are the best solutions to providing the required performance. A case s tudy of an agricultural processing facility is presented to make the conce pts concrete.</p>\n<p><strong><span style="font-size: 16.0pt\; font-family : Copperplate\;"><u>Speaker</u>:</span></strong></p>\n<table style="border -collapse: collapse\; width: 100%\;" border="1"><colgroup><col style="widt h: 21.017274%\;"><col style="width: 78.886756%\;"></colgroup>\n<tbody>\n<t r>\n<td><img src="https://events.vtools.ieee.org/vtools_ui/media/display/a 8648b2d-594d-431d-ba72-4dd9249700ed" alt="" width="240" height="303"></td> \n<td>\n<p class="MsoNormal" style="margin-top: 6.0pt\;"><strong>Bill McFa rland</strong> is an experienced engineering executive\, currently acting as an advisor to several companies in the communications space. &nbsp\;Bil l was previously the CTO of Plume Design. &nbsp\;At Plume he led projects in data science\, optimization\, standards\, intellectual property\, and r egulatory matters. &nbsp\;Bill also served as Vice President of Technology at Qualcomm\, and CTO of Atheros Communications. &nbsp\;Bill received a B achelor&rsquo\;s in Electrical Engineering from Stanford University\, and a Master&rsquo\;s in Electrical Engineering from the University of Califor nia\, Berkeley. &nbsp\;Bill holds over 100 patents\, and has published ove r 35 technical papers. &nbsp\;In 2014\, Bill was named a Fellow of the IEE E.</p>\n</td>\n</tr>\n</tbody>\n</table> END:VEVENT END:VCALENDAR