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DESCRIPTION:Enterprise Artificial Intelligence (AI) and the Future of Work
 \n\nAbstract\n\nIn the decade and a half since 2000 we have witnessed the 
 emergence of the Intelligent Web wherein diverse threads of AI research ha
 ve come together in the continuing effort to better target advertisements 
 at us as we use the Web. It was inevitable that these technologies\, which
  to some extent enable machines to undertake tasks requiring purposeful in
 telligence\, would become pervasive across industry and society. Today\, e
 very industry is both impacted by and also seeking to exploit AI\, not onl
 y by adopting AI technologies in current operations\, but by changing the 
 way they do business: Banks are seeking to go beyond being mere accountant
 s of money and facilitators of payments: by exploiting the data they are p
 rivy to\, i.e.\, who pays whom for what\, they hope to assist merchants in
  their advertising and pricing strategies. Insurance companies can move fr
 om provision to prevention\, for example via mobile apps that track the dr
 iving behavior of customers and reward those that drive more safely\, and 
 similar tools to encourage fitter and healthier lifestyles. Likewise\, car
  manufacturers are beginning to use data from the hundreds of sensors that
  populate each modern vehicle to better predict failures and prevent costl
 y recalls. The list goes on\, and similar trends are observed for telecom 
 operators\, utility companies\, retail chains\, hospitals\, even governmen
 ts. In this talk I will describe in detail a few cross-industry examples o
 f AI technologies being applied in ‘traditional’ (i.e.\, non-web) enterpri
 ses: In particular applications of deep learning and deep reinforcement le
 arning\, with the latter’s effectiveness at ‘playing games’ coming in hand
 y once we begin viewing much of routine operations as games themselves. Wh
 ile in many cases\, operations traditionally involving humans expertise ca
 n be increasingly automated using AI technologies\; we also see equally ma
 ny scenarios where knowledge workers’ effectiveness can\, if properly done
 \, be significantly amplified via appropriate contextually intelligent ass
 istants. Last but not least I will also illustrate examples of the use of 
 crowdsourcing to train machines\, which together with active learning can 
 keep machines and humans working together in a synergistic loop.\n\nDr. Ga
 utam Shroff\n\nVice President & Chief Scientist\n\nTCS Research\, Tata Con
 sultancy Services\n
DTSTART:20170517T150000Z
DTEND:20170517T163000Z
SUMMARY:BRIDGE Webinar: Gautam Shroff\, TCS Research\, Tata Consultancy Ser
 vices
URL:https://www.mcgill.ca/desautels/channels/event/bridge-webinar-gautam-sh
 roff-tcs-research-tata-consultancy-services-267822
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