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AI Validator – A Rising Role for Managing AI Risk in the Enterprise

by Svetlana Sicular  |  September 22, 2020

AI is a newcomer in the enterprise.  The current struggle in the space of AI is to meet the enterprise expectations by inventing and creating AI equivalents of the software lifecycle – DevOps and quality assurance (QA). MLOps is emerging as an equivalent of DevOps (see Use 3 MLOps Organizational Practices to Successfully Deliver Machine […]

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Generative AI in the Hype Cycle for Emerging Technologies – Appealing to the Enterprise!

by Svetlana Sicular  |  September 11, 2020

The Hype Cycle for Emerging Technologies is the most popular Gartner hype cycle. Read and listen about the trends in this hype cycle in just published 5 Emerging Technologies Explained by Gartner Experts.  The emerging trends are sure exciting for me as the author of the Hype Cycle for Artificial Intelligence, because the emerging technologies […]

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The AI Talent Shortage is a Myth

by Svetlana Sicular  |  August 28, 2020

Everybody loves the myth about AI talent and skill shortage. Why not? This myth helps explain why someone is not ready to start and why someone else have not yet finished those AI projects. This very same myth helps vendors sell their products and services. Media likes to speak about the lack of AI skills […]

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AI in the Enterprise: Vertical Solutions on the Horizon

by Svetlana Sicular  |  August 20, 2020

How can enterprise business leaders choose from a host of seemingly similar AI solutions under the time and budget crunch? What might catch their eye within their short attention span? The answer is a solution that solves exactly their business problem, because business people have neither background nor enthusiasm to figure out how to apply […]

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Bias Against… AI Bias

by Svetlana Sicular  |  August 7, 2020

Bias is one of the stereotypical dangers of AI. Those who refer to bias are often biased against… AI bias. We have seen enterprises spending unnecessary resources to eradicate AI bias, because they presume without due diligence that bias is always bad. Meanwhile, bias is a natural effect of learning. It cannot be completely eliminated, […]

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The Hype Cycle for Artificial Intelligence 2020 Reflects the State of AI in the Enterprise

by Svetlana Sicular  |  July 29, 2020

The annual Hype Cycle for Artificial Intelligence, 2020 is just published! Yay! AI as a concept is rolling off the top of the Peak of Inflated Expectations. (Yay!) It is time for production deployments, and that reveals new challenges. Some of these challenges are not really unheard of – they reflect a lack of capabilities […]

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How Enterprise Investments in AI Changed During the Pandemic

by Svetlana Sicular  |  July 22, 2020

Speculations about the state of artificial intelligence in the enterprise are plentiful and inconsistent.  The COVID-19 pandemic just aggravates those speculations.  Some people say that AI helps a lot – for modeling the economy reopening, tracing contacts, coming up with the treatment and much more. Some others are saying that AI failed, because when data […]

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The Big Data Obituary

by Svetlana Sicular  |  October 3, 2018

Today, the rivals and big data pioneers Cloudera and Hortonworks announced their merger. In our last year’s Hype Cycles, we marked “Big Data” and “Hadoop Distributions” as obsolete before plateau. They are off the hype cycle this year, but I want to preserve the obituary of big data that my colleague Mark Beyer wrote in 2017, […]

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And the 2017 Cool Vendors Are…

by Svetlana Sicular  |  June 1, 2017

This is the time of our Cool Vendors season finale — the reports showcasing fascinating technologies and breakthrough ideas are out. The season usually starts in January, when analysts submit the names of vendors that impressed them the most.  It is purely an analyst choice whether to submit and what. The criteria by which a […]

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Hadoop is dead, long live Hadoop!

by Svetlana Sicular  |  September 29, 2016

This year, we are celebrating the 10th anniversary of Hadoop. In 2006, we lived in the dark ages of data: Several databases were old news and a single Gartner analyst was covering them all. Today, a whole team of Gartner database analysts could barely handle all the new options. In summer 2009, baby-elephant Hadoop made its […]

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