BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20220414T001924Z UID:EAD320CA-E338-4BA1-B52B-A8D965CAD434 DTSTART;TZID=US/Eastern:20220413T120000 DTEND;TZID=US/Eastern:20220413T130000 DESCRIPTION:Over the past decade\, the confluence of sophisticated algorith ms and tools\, computational infrastructure\, and data science has fueled a machine learning revolution across multiple fields\, including speech an d handwriting recognition\, natural language processing\, computer vision\ , social network filtering\, and machine translation. Ensuing advances are changing the way we interact with technology in our daily lives. This is particularly salient when it comes to user input on mobile devices\, be it speech\, handwriting\, touch\, keyboard\, or camera input. Increased inpu t intelligence boosts device responsiveness across languages\, improving n ot only basic abilities like tokenization\, named entity recognition and p art-of-speech tagging\, but also more advanced capabilities like statistic al language modeling and question answering. In this talk\, I will give se lected examples of what we are doing at Apple to impart input intelligence to mobile devices\, with two overarching themes as sub-text: (i) enhancin g interaction experience through machine learning\, and (ii) transforming users' digital lives without sacrificing their privacy.\n\nCo-sponsored by : North Jersey Section\n\nSpeaker(s): Dr. Jerome R. Bellegarda \, \n\nAgen da: \nOver the past decade\, the confluence of sophisticated algorithms an d tools\, computational infrastructure\, and data science has fueled a mac hine learning revolution across multiple fields\, including speech and han dwriting recognition\, natural language processing\, computer vision\, soc ial network filtering\, and machine translation. Ensuing advances are chan ging the way we interact with technology in our daily lives. This is parti cularly salient when it comes to user input on mobile devices\, be it spee ch\, handwriting\, touch\, keyboard\, or camera input. Increased input int elligence boosts device responsiveness across languages\, improving not on ly basic abilities like tokenization\, named entity recognition and part-o f-speech tagging\, but also more advanced capabilities like statistical la nguage modeling and question answering. In this talk\, I will give selecte d examples of what we are doing at Apple to impart input intelligence to m obile devices\, with two overarching themes as sub-text: (i) enhancing int eraction experience through machine learning\, and (ii) transforming users ' digital lives without sacrificing their privacy.\n\nRoom: M105\, Bldg: Muscarelle Center\, M105\, \, 1000 River Road \, Teaneck \, New Jersey\, U nited States\, 07666\, Virtual: https://events.vtools.ieee.org/m/306840 LOCATION:Room: M105\, Bldg: Muscarelle Center\, M105\, \, 1000 River Road \, Teaneck \, New Jersey\, United States\, 07666\, Virtual: https://events .vtools.ieee.org/m/306840 ORGANIZER:zhao@fdu.edu SEQUENCE:2 SUMMARY:Input Intelligence on Mobile Devices URL;VALUE=URI:https://events.vtools.ieee.org/m/306840 X-ALT-DESC:Description: <br /><p>Over the past decade\, the confluence of s ophisticated algorithms and tools\, computational infrastructure\, and dat a science has fueled a machine learning revolution across multiple fields\ , including&nbsp\;speech and handwriting recognition\, natural language pr ocessing\, computer vision\, social network filtering\, and machine transl ation. Ensuing advances are changing the way we&nbsp\;interact with techno logy in our daily lives. This is particularly salient when it comes to use r input on mobile&nbsp\;devices\, be it speech\,&nbsp\;handwriting\,&nbsp\ ;touch\,&nbsp\;keyboard\, or camera input.&nbsp\;Increased&nbsp\;input&nbs p\;intelligence boosts device responsiveness across languages\, improving not only basic&nbsp\;abilities like tokenization\, named entity recognitio n and part-of-speech tagging\, but also&nbsp\;more advanced&nbsp\;capabili ties&nbsp\;like&nbsp\;statistical language modeling and question answering .&nbsp\;In this talk\, I will give selected examples of what we are doing at Apple to impart input intelligence to&nbsp\;mobile devices\, with two o verarching themes as sub-text: (i) enhancing interaction experience throug h machine learning\, and (ii) transforming users' digital lives without sa crificing&nbsp\;their privacy.</p><br /><br />Agenda: <br /><p>Over the pa st decade\, the confluence of sophisticated algorithms and tools\, computa tional infrastructure\, and data science has fueled a machine learning rev olution across multiple fields\, including&nbsp\;speech and handwriting re cognition\, natural language processing\, computer vision\, social network filtering\, and machine translation. Ensuing advances are changing the wa y we&nbsp\;interact with technology in our daily lives. This is particular ly salient when it comes to user input on mobile&nbsp\;devices\, be it spe ech\,&nbsp\;handwriting\,&nbsp\;touch\,&nbsp\;keyboard\, or camera input.& nbsp\;Increased&nbsp\;input&nbsp\;intelligence boosts device responsivenes s across languages\, improving not only basic&nbsp\;abilities like tokeniz ation\, named entity recognition and part-of-speech tagging\, but also&nbs p\;more advanced&nbsp\;capabilities&nbsp\;like&nbsp\;statistical language modeling and question answering.&nbsp\;In this talk\, I will give selected examples of what we are doing at Apple to impart input intelligence to&nb sp\;mobile devices\, with two overarching themes as sub-text: (i) enhancin g interaction experience through machine learning\, and (ii) transforming users' digital lives without sacrificing&nbsp\;their privacy.</p> END:VEVENT END:VCALENDAR