Wednesday, March 3, 2021

g-f(2)153 The Big Picture of Business Artificial Intelligence (3/3/2021) in a Single “g-f KBP” Chart




Extra-condensed knowledge


  • g-f(2)82 THE BIG PICTURE OF THE DIGITAL AGE, MIT TASK FORCE ON THE WORK OF THE FUTURE, AI Today, and the General Intelligence of Work.
    • Most of the AI deployed today, while novel and impressive, still falls under a category of “specialized AI.”
    • Artificial general intelligence (AGI), the idea of a truly artificial human-like brain, remains a topic of deep research interest but a goal that experts agree is far in the future.
    • The ability to adapt to entirely novel situations is still an enormous challenge for AI and robotics, a key reason for companies’ continued reliance on human workers for a variety of tasks.
    • From a work perspective, these technologies tend to be task oriented, that is they execute limited sets of tasks, more than the full set of activities comprising an occupation.
  • g-f(2)111 THE BIG PICTURE OF THE DIGITAL AGE, HBR, Why Is It So Hard to Become a Data-Driven Company?
    • Findings from this year’s survey suggest that even with record levels of committed investment, firms are continuing to struggle to derive value from their Big Data and AI investments and to become data-driven organizations.


Genioux knowledge fact condensed as an image


The “genioux facts” Knowledge Big Picture (g-f KBP) charts



The “genioux facts” Knowledge Big Picture (g-f KBP) standard chart


The “genioux facts” Knowledge Big Picture (g-f KBP) simplified chart


The “genioux facts” Knowledge Big Picture (g-f KBP) block chart


The “genioux facts” Knowledge Big Picture (g-f KBP) graphic


Condensed knowledge  


  1. g-f(2)82 THE BIG PICTURE OF THE DIGITAL AGE, MIT TASK FORCE ON THE WORK OF THE FUTURE, AI Today, and the General Intelligence of Work.
    • Most of the AI deployed today, while novel and impressive, still falls under a category of “specialized AI.” 
    • Artificial general intelligence (AGI), the idea of a truly artificial human-like brain, remains a topic of deep research interest but a goal that experts agree is far in the future.
    • The ability to adapt to entirely novel situations is still an enormous challenge for AI and robotics, a key reason for companies’ continued reliance on human workers for a variety of tasks.
    • From a work perspective, these technologies tend to be task oriented, that is they execute limited sets of tasks, more than the full set of activities comprising an occupation.
  2. g-f(2)65 THE BIG PICTURE OF THE DIGITAL AGE: IDC, Worldwide Spending on Artificial Intelligence Is Expected to Double in Four Years, Reaching $110 Billion in 2024
    • Global spending on artificial intelligence (AI) is forecast to double over the next four years, growing from $50.1 billion in 2020 to more than $110 billion in 2024. 
    • According to IDC Worldwide Artificial Intelligence Spending Guide, spending on AI systems will accelerate over the next several years as organizations deploy artificial intelligence as part of their digital transformation efforts and to remain competitive in the digital economy.
  3. g-f(1)10 Companies obtain significant financial benefits from AI when the strategic focus is organizational learning, not just machine learning 
    • Only 10% of companies obtain significant financial benefits from artificial intelligence technologies. 
    • Research shows that these companies intentionally change processes, broadly and deeply, to facilitate organizational learning with AI. 
    • Better organizational learning enables them to act precisely when sensing opportunity and to adapt quickly when conditions change. 
    • Their strategic focus is organizational learning, not just machine learning.
  4. g-f(1)21 Successful Digital Transformation Projects at Repsol: Continuous Learning with AI 
    • Repsol, a global energy and utility company based in Spain has successfully implemented more than 190 digital transformation projects. 
    • Seventy percent of them incorporate artificial intelligence in some way. Repsol does more than teach machines how to drill, blend, and serve. 
    • In effect, Repsol changed its processes to continuously learn with AI. 
    • Process improvements beget new behaviors and new human knowledge, which is then fed back to machines. 
  5. g-f(2)111 THE BIG PICTURE OF THE DIGITAL AGE, HBR, Why Is It So Hard to Become a Data-Driven Company? 
    • For the third consecutive year, investment in data and AI initiatives has been nearly universal, with 99.0% of firms reporting investment in data and AI according to findings from a newly released executive survey from NewVantage Partners. 
    • Findings from this year’s survey suggest that even with record levels of committed investment, firms are continuing to struggle to derive value from their Big Data and AI investments and to become data-driven organizations.
  6. g-f(2)132 THE BIG PICTURE OF THE DIGITAL AGE, MIT SMR, Progress Toward the Intelligent Enterprise.
    • To deliver on the promise of AI, leaders must focus their efforts on improving their organization’s culture and decision-making capabilities.
    • It’s easy to do AI wrong, and it’s very hard to do it right.
    • The organizations that are making the most significant progress on the path toward the intelligent enterprise are the ones that treat the project as a complex systems engineering problem — one that focuses more on adapting the corporate culture than it does on the technology itself.
  7. g-f(2)93 THE BIG PICTURE OF THE DIGITAL AGE, Forbes, Understanding The Value Of Artificial Intelligence Solutions In Your Business.
    • AI should be viewed as part of the digital transformation journey of your organization and implemented only when it makes sense for the business goals that you are trying to achieve.
  8. g-f(2)34 MIT SMR: Why Chief Data Officers (CDO) Must Assume Leadership for Data Success 
    • Companies must ensure that the chief data officer (CDO) has the necessary tools and support for executing on his or her data vision. 
  9. g-f(2)4 Companies need to identify the type of talent they need in order to become data-driven 
    • Whether people are called data scientists, quantitative analysts, or something else, these titles are still relatively new to companies. 
    • It’s not surprising that there is little consensus on their meaning. Over time, new titles will emerge that better describe the particular activities involved in a job; data engineer, for example, has already arisen to describe people who spend most of their time wrangling data. 
    • Until more consensus is reached across our society, however, it’s important for companies to emulate TD Bank and clearly identify the types of talent required to become data-driven and then acquire it, nurture it, and unleash it.
  10. g-f(2)136 THE BIG PICTURE OF THE DIGITAL AGE, Forbes, 10 Ways AI And Machine Learning Are Improving Marketing In 2021.
    • 70% of high-performance marketing teams claim they have a fully defined AI strategy versus 35% of their under-performing peer marketing team counterparts. 
    • CMOs who lead high-performance marketing teams place a high value on continually learning and embracing a growth mindset.


Category 2: The Big Picture of the Digital Age

[genioux fact produced, deduced or extracted from geniouxfacts]

Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).

Type of validity of the "genioux fact". 

  • Inherited from sources + Supported by the knowledge of one or more experts + Supported by research.


Authors of the genioux fact

Fernando Machuca


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ABOUT THE AUTHORS


CEO ‎Genioux.com Corporation
PhD with awarded honors in computer science in France

Fernando is the director of "genioux facts". He is the entrepreneur, researcher and professor who has a disruptive proposal in The Digital Age to improve the world and reduce poverty + ignorance + violence. A critical piece of the solution puzzle is "genioux facts". The Innovation Value of "genioux facts" is exceptional for individuals, companies and any kind of organization.


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