Tuesday, September 28, 2021

g-f(2)521 THE BIG PICTURE OF THE DIGITAL AGE (9/28/2021), ZDNet, Preparing for the 'golden age' of artificial intelligence and machine learning

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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence (9/28/2021)  g-f(2)426 


The "golden age" of AI and ML, ZDNet 

  • Swami Sivasubramanian, VP of machine learning at Amazon Web Services, calls this the "golden age" of AI and machine learning. That's because this technology "is becoming a core part of businesses around the world." 
  • The latest ZDNet survey on AI actionability and accountability finds that IT teams are taking a direct lead, with most companies building in-house systems. However, oversight of AI-generated decisions is lagging.
  • The survey confirmed that AI and ML initiatives are front and center at most enterprises.
  • There are a number of ways IT leaders and AI proponents can help address issues with AI actionability and accountability. 70% are pursuing continuous integration/continuous deployment (CI/CD) approaches to their AI and ML work to assure constant checks on the composition of algorithms, associated applications, and the data going through them. 
  • Survey respondents included executives, CIOs, CTOs, analysts/systems analysts, enterprise architects, developers, and project managers. Industries represented included technology, services, retail, and financial services. Company sizes varied. 


        • The survey of IT executives conducted by ZDNet found that companies need more data engineers, data scientists, and developers to deliver on the goals. 
        •  "creating AI solutions that work for humans also requires understanding how humans work," says Kimberly Nevala, AI strategic advisor at SAS . "How can humans engaging with AI systems influence their behavior and performance? And vice versa. Critical thinking, navigating uncertainty and collaborating productively are also underrated yet key skills."
        • How far along are corporate efforts to achieve fairness and eliminate bias in AI results? The data shows that they're not that far, with 41% of respondents indicating there are few, if any, checks on their AI output or they simply aren't aware if such checks take place. 

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        Opportunity, Some key statistics, ZDNet 
          • As of August, when ZDNet conducted the survey, close to half of the represented enterprises (44%) had AI-based technology actively being built or deployed. Another 22% had projects under development. Efforts in this space are still new and emerging — 59% of surveyed enterprises have been working with AI for less than three years.
          • IT teams are taking a direct lead, with most companies building their systems in-house. Close to two-thirds of respondents, 63%, report that their AI systems are built and maintained by in-house IT staff. Almost half, 45%, also subscribe to AI-related services through Software as a Service (SaaS) providers. Another 30% use Platform as a Service (PaaS), and 28% turn to outside consultants or service firms.
          • Chief digital officers, chief data officers or chief analytics officers usually take the lead with AI and ML-driven output, with 50% of respondents identifying these executives as primary decision-makers. Another 42% say individual department heads play a role in the process, and 33% of surveyed organizations have corporate committees that exercise AI oversight. One-third of these organizations assign AI and ML responsibilities to data scientists and analysts. Interestingly, CIOs and CTOs only weigh in at 25% of the respondents' companies.

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          Alert, Implementation is hard, ZDNet 
            • "Implementing an AI solution is not easy, and there are many examples of where AI has gone wrong in production," says Tripti Sethi, senior director at Avanade. "The companies we have seen benefit from AI the most understand that AI is not a plug-and-play tool, but rather a capability that needs to be fostered and matured. These companies are asking 'what business value can I drive with data?' rather than 'what can my data do?'" 
            • Skills availability is one of the leading issues that enterprises face in building and maintaining AI-driven systems. Close to two-thirds of surveyed enterprises, 62%, indicated that they couldn't find talent on par with the skills requirements needed in efforts to move to AI. More than half, 54%, say that it's been difficult to deploy AI within their existing organizational cultures, and 46% point to difficulties in finding funding for the programs they want to implement.
            • Data engineering is the most in-demand skill to support AI and ML initiatives, and it was cited by 69% of respondents. 

            Lessons learned, ZDNet
            • Almost half of enterprises, 47%, purchase extending processing capacity through a third-party or cloud provider. This  is the leading hardware area purchased in the realm of technologies. 
            • Only 11% of enterprises purchase hardware or systems for on-site implementations. 
            • At least 42% are working with Internet of Things (IoT) devices and networks to support their AI efforts. 
            • In terms of AI-related software, 47% are working with analytics engines such as Apache Spark. Another 42% are working with big data clustering platforms such as Hadoop, as well as 42% deploying advanced databases. 

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            • Category 2: The Big Picture of the Digital Age
            • [genioux fact deduced or extracted from geniouxfacts]
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            “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)521, Fernando Machuca, September 28, 2021, blog.geniouxfacts.comgeniouxfacts.comGenioux.com Corporation.


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