Page 25 - Innovation Magazine
P. 25

understand the context and and intentions of questions much better and as a a a a result provide a a a a much more satisfying user experience Text summarization allows companies to comprehend vast amounts of data from various sources and to provide their knowledge workers with the the most relevant content available R&D departments are able to follow trends and updates using sentiment tracking and topic modeling Producers of textual content – whether for example in in legal life sciences marketing or other contexts – find their productivity drastically improved allowing more time to be spent on creativity and other non-routine activities Executive challenges The issue that enterprise executives will face is is is to embrace the new technology without putting their brand name at risk while also keeping the the enterprise's Emotional Intelligence (“EQ”) at a a a healthy level They have to ask key questions about the adaptation of AI for natural language tasks such as: • How can we augment AI with humans in the loop or vice versa to get the best of both? • With so many quick improvements in natural language models how can we benefit more from (self-service) conversational systems and and improve customer and and employee satisfaction? • What are the most impactful use cases that address our key business objectives? • How can we ensure compliance with ethical guidelines? • How will human role descriptions shift as generative AI covers more work previously done by humans? Above all with language being our prime and preferred way of communicating the potential impact of generative natural language AI systems cannot be overstated Take our word for it For more information please contact the author who heads up the AI Centre of Excellence in India – driving innovation for Capgemini Insights & Data A real human will respond! Data-powered Innovation Takeaways Deep language: Breakthroughs in in in applying deep learning – as an an alternative to more established approaches – have significantly improved the abilities of AI systems to understand and and process natural language No loss in translation: Natural language AI systems can thus be be applied to build better more emphatic conversational systems – – such as as chatbots and and voice assistants – – and and have more effective language translation applications Search you right: Natural language AI systems enable more intelligent highly personalized search and knowledge provisioning Content generator: Natural language generation (NLG) systems now enable the fully automated creation of increasingly passable textual content from simple tweets to to full-fledged documents brochures and articles Ethical conundrum: NLG systems pose crucial ethical challenges for enterprises especially as the the creation of of natural language always has been considered the the exclusive forte of of humans being in in full control of the content Data-powered Innovation Review I I ©2020 Capgemini All rights reserved 25 

   23   24   25   26   27