{"id":4654,"date":"2020-05-13T11:27:52","date_gmt":"2020-05-13T09:27:52","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=4654"},"modified":"2021-12-23T12:54:37","modified_gmt":"2021-12-23T11:54:37","slug":"ibm-ai-for-enterprise","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/ai-ml\/ibm-ai-for-enterprise\/","title":{"rendered":"IBM Think Digital 2020: AI for Enterprise and the Value of Language"},"content":{"rendered":"\t\t\t\t
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  1. AI needs a good beginning<\/a>
  2. The IBM approach to AI for enterprise<\/a>
  3. Do we need DataOps?<\/a>
  4. Watson Assistant: The smarter AI assistant for business<\/a>
  5. Watson Discovery: AI for enterprise search technology\u00a0<\/a><\/ol>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\n\n\n

    IBM<\/span> recently hosted<\/span> Think Digital 2020<\/a>, a series of fascinating talks, presentations and interviews all facilitated remotely during the COVID-19 stay at home order. The two-day event was an opportunity to discuss the state of business<\/span> and the role of critical technologies<\/span>, such as AI<\/span> for enterprise and Cloud<\/span>, as clients accelerate recovery and digital transformation in the wake of COVID-19<\/span>. Codemotion<\/span> attended the event, and we’re sharing some insights from some of the fantastic sessions.<\/p>\n\n\n\n

    Sriram Raghavan is Vice President, IBM<\/span> Research<\/span> AI<\/span>, with overall responsibility for AI<\/span> research<\/span> across all of the IBM<\/span> Research<\/span> labs. He was interviewed by theCube about “The future of AI<\/span> for the enterprise”.<\/em><\/p>\n\n\n\n

    IBM<\/span> Research<\/span> has been exploring artificial intelligence and machine learning<\/span><\/a> technologies and techniques for decades. IBM<\/span> is advancing the field through their portfolio of research<\/span> focused on three areas: towards human-level intelligence, platform for business<\/span>, and hardware and the physics of AI<\/span>.S<\/p>\n\n\n\n

    Sriram shared several interesting insights into AI<\/span>:<\/p>\n\n\n\n

    AI needs a good beginning<\/h2>\n\n\n\n

    Sriram notes that to get the value of AI<\/span>, you need to start at the beginning. Making your data<\/span> ready for AI<\/span> leads to better adoption. Historically one of the challenges of AI<\/span> has been that inadequate or poor quality data<\/span> cannot provide satisfactory results that can identify and drive business<\/span> cases. Therefore the groundwork in early stages is critical. <\/p>\n\n\n\n

    “You can’t take months to build a model and be unable can’t operationalize it, but this is what has been happening traditionally in AI<\/span> for the enterprise.”<\/p>\n\n\n\n

    The IBM approach to AI for enterprise<\/h2>\n\n\n\n

     IBM Research<\/span> AI<\/span> is focused on the evolution of the science and techniques behind AI<\/span> and” “how do you have the techniques and innovations in AI<\/span> to learn to be less data<\/span> and resource-hungry?” This includes:<\/p>\n\n\n\n