<\/p>\n
<\/p>\n
Demystify the role and potential of Artificial Intelligence<\/strong> It\u2019s hard to believe that the now ubiquitous terms of AI and ML date back to the 1950’s<\/em><\/strong><\/p>\n Since then, public interest in AI and ML has waxed Gartner\u00ae\u2019s Hype Cycle\u2122 for Emerging Gartner.<\/strong><\/em> Demystify the role and potential of Artificial Intelligence (AI) and Machine Learning (ML) in powering positive outcomes for […]<\/p>\n","protected":false},"author":30,"featured_media":557,"comment_status":"open","ping_status":"closed","template":"","course_category":[],"course_tag":[],"class_list":["post-474","lp_course","type-lp_course","status-publish","has-post-thumbnail","hentry","course"],"_links":{"self":[{"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/lp_course\/474","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/lp_course"}],"about":[{"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/types\/lp_course"}],"author":[{"embeddable":true,"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/users\/30"}],"replies":[{"embeddable":true,"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/comments?post=474"}],"version-history":[{"count":4,"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/lp_course\/474\/revisions"}],"predecessor-version":[{"id":558,"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/lp_course\/474\/revisions\/558"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/media\/557"}],"wp:attachment":[{"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/media?parent=474"}],"wp:term":[{"taxonomy":"course_category","embeddable":true,"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/course_category?post=474"},{"taxonomy":"course_tag","embeddable":true,"href":"https:\/\/jiku.shop\/courses\/wp-json\/wp\/v2\/course_tag?post=474"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n(AI) and Machine Learning (ML)<\/strong> in powering positive
\noutcomes for the enterprise.<\/p>\n
\nand waned. But the release of OpenAI\u2019s ChatGPT<\/strong>
\nin late 2022, and its competitors that followed,
\nbrought generative AI into the mainstream.
\nNow it\u2019s more powerful and easier to use than ever before, with use
\ncases that span across both consumer and enterprise. Generative AI can
\nplan your next vacation, write poetry in the style of William Shatner or
\npolish the speech you need to write for your best friend\u2019s wedding. It can
\nalso write policies for a cyber insurance application, generate code or
\nexplain the meaning of a high error in your API service.
\nOver the next decade, relying on AI in business will be as quotidian
\nas flipping on a light switch.<\/p>\n
\nTechnologies 20231
\nclaims that generative AI will reach the peak of
\ninflated expectations in 2023. However, AI\u2019s long-term potential is
\nunderestimated, says Hao Yang, Splunk\u2019s VP of AI. Generative AI alone
\nrepresents an annual $2.6 to $4.4 trillion in opportunity across 63 use
\ncases, according to a McKinsey study. Spending will only continue to
\ngrow: IDC predicts that worldwide spending on AI will surpass $300
\nbillion in 2026.
\nNow the ROI of those investments \u2014 a historically painful metric to
\nquantify for AI projects \u2014 is starting to crystallize as organizations
\nexpand to more sophisticated use cases. In a PwC survey, 72% of AImature organizations surveyed (and 59% of all other respondents) are
\nconfident in their abilities to assess the ROI of their current AI initiatives,
\nwith the ability to capture both hard and soft returns and costs.
\nDespite the promise of AI, an overall mistrust lingers. Fifty-two percent
\nof organizations say that risk factors are a critical consideration when
\nevaluating new AI use cases, according to<\/p>\n
\n2<\/em>
\nThe old adage \u201cyou <\/em>
\ncan\u2019t protect what you don\u2019t know\u201d rings true for AI and ML. Generative <\/em>
\nAI in particular raises deep-seated data privacy and security concerns. <\/em>
\nThese AI anxieties are only natural as organizations navigate this new <\/em>
\nera of technology. The Biden Administration\u2019s executive order on AI<\/em>
\nprovides a few answers, but substantive regulatory changes are likely <\/em>
\nstill a ways out. <\/em>
\nIn the meantime, the AI train has left the station, with positive outcomes <\/em>
\nthat are too difficult to ignore. Usage is already widespread, with 55% <\/em>
\nof respondents in the McKinsey study reporting that their organizations <\/em>
\nhave adopted AI. And organizations are realizing the value of this <\/em>
\nadoption through improvements in productivity, decision-making, <\/em>
\ncustomer experience, innovation and beyond. <\/em>
\nTo be sure, some of AI\u2019s most far-reaching concepts (computers that <\/em>
\ncan replicate the human brain entirely, fully autonomous robots and <\/em>
\nprograms that design, code and upgrade themselves) are years away <\/em>
\nfrom reality; they\u2019re still moonshots that represent the eventual apex <\/em>
\nof AI\u2019s capabilities. But considering that AI tools can already win at <\/em>
\nJeopardy!, are able to detect breast cancer and are logging tens of <\/em>
\nthousands of miles behind the wheel of self-driving vehicles every <\/em>
\nday, the prospect of even those moonshot concepts really doesn\u2019t <\/em>
\nseem so far-fetched.<\/em>
\nIn other words, now is the time to learn about AI and ML. Before you can <\/em>
\ndevelop a thoughtful strategy that considers the risks and benefits, it\u2019s <\/em>
\nimportant to clarify these common misconceptions. <\/em>
\n1\u2002Gartner, Hype Cycle for Emerging Technologies, Arun Chandrasekaran, Melissa Davis <\/em>
\n2 August 2023.<\/em>
\n2\u2002Gartner Press Release, Gartner Survey Finds 55% of Organizations That Have Deployed AI <\/em>
\nTake an AI-First Strategy with New Use Cases, July 27, 2023.<\/em>
\nGARTNER is a registered trademark and service mark of Gartner, Inc. and\/or its affiliates in the U.S. <\/em>
\nand internationally, and HYPE CYCLE is a registered trademark of Gartner, Inc. and\/or its affiliates <\/em>
\nand are used herein with permission. All rights reserved.<\/em>
\n5 Big Myths of AI and Machine Learning Debunked | Splunk 3<\/em>
\nAI, ML and <\/em>
\ngenerative <\/em>
\nAI are all <\/em>
\nthe same.<\/em>
\nAs with many new technologies, artificial <\/em>
\nintelligence has created a gold rush effect <\/em>
\nacross many industries. <\/em><\/p>\n","protected":false},"excerpt":{"rendered":"