Saturday, October 23, 2021

g-f(2)592 THE BIG PICTURE OF THE DIGITAL AGE (10/23/2021), MIT SMR, The Real Deal About Synthetic Data




ULTRA-condensed knowledge


"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence, Synthetic Data (10/22/2021)  g-f(2)426 


Opportunity

Synthetic Data, MIT SMR 


    • Data is the essential fuel driving organizations’ advanced analytics and machine learning initiatives, but between privacy concerns and process issues, it’s not always easy for researchers to get their hands on what they need. 
    • A promising new avenue to explore is synthetic data, which can be shared and used in ways real-world data can’t. However, this emerging approach isn’t without risks or drawbacks, and it’s essential that organizations carefully explore where and how they invest their resources.
    • Synthetic data is artificially generated by an AI algorithm that has been trained on a real data set. It has the same predictive power as the original data but replaces it rather than disguising or modifying it. 
    • Synthetic data is a boon for researchers. 
    • The technology has potential across a range of industries.
    • Although synthetic data is still at the cutting edge of data science, more organizations are experimenting with how to get it out of the lab and apply it to real-world business challenges. How this evolution unfolds and the timeline it will follow remain to be seen. But leaders of data-driven organizations should have it on their radar and be ready to consider applying it when the time is right for them.


      Genioux knowledge fact condensed as an image



      References




      ABOUT THE AUTHORS


      Fernando Lucini (@fernandolucini) is global data science and machine learning engineering lead at Accenture Applied Intelligence.



      Extra-condensed knowledge


      Opportunity 

      The Value for Business: Security, Speed, and Scale, MIT SMR



      • Synthetic data’s most obvious benefit is that it eliminates the risk of exposing critical data and compromising the privacy and security of companies and customers. 
      • By eliminating the time-consuming roadblocks of privacy and security protocols, synthetic data also allows organizations to gain access to data more quickly.
      • With synthetic data, a company can quickly train machine learning models on large data sets, accelerating the processes of training, testing, and deploying an AI solution. 
      • Security and speed also enable scale, enlarging the amount of data available for analysis. 



        Alert 

        Why Isn’t Everybody Using It?, MIT SMR



        • While the benefits of synthetic data are compelling, realizing them can be difficult. Generating synthetic data is an extremely complex process, and to do it right, an organization needs to do more than just plug in an AI tool to analyze its data sets. The task requires people with specialized skills and truly advanced knowledge of AI. A company also needs very specific, sophisticated frameworks and metrics that enable it to validate that it created what it set out to create. This is where things become especially difficult.



        Condensed knowledge




        Opportunity 

        What It Takes to Move Forward, MIT SMR


        • With the relevant skills, frameworks, metrics, and technologies maturing, companies will be hearing a lot more about synthetic data in the coming years. As they weigh whether it makes sense for them, companies should consider the following four questions:
          1. Do the right people know what we’re getting into? Synthetic data is a new and complicated concept for most people.
          2. Do we have access to the necessary skills? Creating synthetic data is a very complex process, so organizations need to determine whether their data scientists and engineers are capable of learning how to do it.
          3. Do we have a clear purpose? Synthetic data must be generated with a particular purpose in mind, because the intended use affects how it’s generated and which of the original data’s properties are retained.
          4. What’s the scale of our ambitions? Creating synthetic data isn’t for the faint of heart. The sheer complexity associated with doing it right — and the potential pitfalls of doing it wrong — means organizations should be sure it will deliver sufficient value in return.



        Some relevant characteristics of this "genioux fact"

        • Category 2: The Big Picture of the Digital Age
        • [genioux fact deduced or extracted from MIT SMR]
        • This is a “genioux fact fast solution.”
        • Tag Opportunities those travelling at high speed on GKPath
        • 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.


        References


        “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)592, Fernando Machuca, October 23, 2021, blog.geniouxfacts.comgeniouxfacts.comGenioux.com Corporation.


        ABOUT THE AUTHORS


        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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