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Understanding equations between humans and machines to master digital transformation

Not so long ago experts were proclaiming that the mobile is going to be the future. And, guess what? They were not wrong. Some of the extraordinarily successful applications and businesses are mobile-based. Just think of the names like Instagram, Uber, Swiggy. Didn’t they transform the thoughts of a human into exceptionally well-crafted businesses and apps? Yes! They did. And for all those successfully running transformations there has been a secret that we need to know. That is, how they bridged all those gaps between a human and a machine and made a remarkable equation. How did they master it? Rapid Changes need adaptability With evolving technology, humans started to find a solution to every problem they encountered and started converting that as key thought of a business. Now, devices and machines are understanding from a deeper level than that of a human. Just think about all the technologies used in health care? How we have transformed from checking pulse with a finger on the wrist to having an oximeter for accuracy? There was take-away of food while we were kids and now food to your doorstep with a click. From buying a ticket at the station to booking it on the app. With changing needs and adapting new trends there has been a good equation we need to learn between a human and a machine. How do machines learn? Artificial Intelligence and machine learning happen basically in four types. This is exactly how the data is processed, observed, corrected and output is obtained. The key techniques in which the ML or AI leveraged to learn are as follows • Active Learning: Generally the models incorporated in machine learning are to guess the solution, check if it correct and go ahead to solve a similar problem. Active learning is a similar kind of work that AI and ML do. They take prioritized mathematical techniques from the subject matter expert and follow them to derive results. This method improves a positive collaboration between a human and a machine. • Transfer Learning: In this technique, the neutral network is well trained to solve a particular type of problem. Then the knowledge acquired in solving that problem is now transferred to apply for another different model but a similar kind of issue. For example, a problem solved in recognizing a car is relatively transferred for similar issues related to identifying a truck • Reinforcement Learning: This technique is used as a trial and error method. Where the successful methods are rewarded as right moves and the error methods are considered to recheck the process. This is mostly done by AI agent and is taught successful strategies. Through the process, the AI understands how well it can make the right choice to avoid consequences without any explicit instructions from the programmer. • Meta-Learning: This technique is a subfield of machine learning. It is programmed with certain types of algorithms that can teach a machine how to absorb the data. Simply how to learn what to learn. This helps the machine to upgrade in the form of skills and environments without the necessity of loading huge datasets. How should a human learn? This question might surely sound surprising. But, to have a great equation with a machine, humans need to upgrade on few things and learn a few. • Team game: Forming a potential team that is skilful in upcoming trends helps to work as a better team with those having skills in the current trends. This collaboration can bring out results that are meeting the current needs of a business and are also braced to encounter the new demands. • Discover: There is never s straight line to great innovations. Sometimes the non-linear process of going two steps back can bring out great ideas that can create wonders. • Test: Learning is always a process that goes on forever. It becomes successful only when tested and then identified as the right way. So a successful project gives a lot of skill enough to embrace another one. • Fail: The most common of all. It helps in a way better than a successful project. It gives clear guidance on what not to do. And, when it happens, stand again to start again. • Lead: Ensure everyone on the team is coached and trained to take over the project at any given time and circumstance. Throughout the process of a project, situations keep on changing and one needs to be well informed to face anything. • Unlearn and upskill: Every time a new problem arrives, the same process that succeeded in the previous issue might be completely irrelevant to this one. So be open to unlearn outdated processes and upskill to the emerging solutions. Conclusion From observing new technologies and how they are transforming digitally, it is very much evident that not just machines, but humans have a lot to learn to form a great equation. A good partnership between humans and machines can only be achieved if machines are made with accurate tools and technologies, and humans who can embrace diversity, nurture it across the industries and their ecosystems.

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Not so long ago experts were proclaiming that the mobile is going to be the future. And, guess what? They were not wrong. Some of the extraordinarily successful applications and businesses are mobile-based. Just think of the names like Instagram, Uber. Didn’t they transform the thoughts of a human into exceptionally well-crafted businesses and apps? Yes! They did. And for all those successfully running transformations there has been a secret that we need to know. That is, how they bridged all those gaps between a human and a machine and made a remarkable equation. How did they master it?

Rapid Changes need adaptability

With evolving technology, humans started to find a solution to every problem they encountered and started converting that as key thought of a business. Now, devices and machines are understanding from a deeper level than that of a human. Just think about all the technologies used in health care? How we have transformed from checking pulse with a finger on the wrist to having an oximeter for accuracy? There was take-away of food while we were kids and now food to your doorstep with a click. From buying a ticket at the station to booking it on the app. With changing needs and adapting new trends there has been a good equation we need to learn between a human and a machine.  

How do machines learn?

Artificial Intelligence and machine learning happen basically in four types. This is exactly how the data is processed, observed, corrected and output is obtained. The key techniques in which the ML or AI leveraged to learn are as follows

  • Active Learning: Generally the models incorporated in machine learning are to guess the solution, check if it correct and go ahead to solve a similar problem. Active learning is a similar kind of work that AI and ML do. They take prioritized mathematical techniques from the subject matter expert and follow them to derive results. This method improves a positive collaboration between a human and a machine.
  • Transfer Learning: In this technique, the neutral network is well trained to solve a particular type of problem. Then the knowledge acquired in solving that problem is now transferred to apply for another different model but a similar kind of issue. For example, a problem solved in recognizing a car is relatively transferred for similar issues related to identifying a truck 
  • Reinforcement Learning: This technique is used as a trial and error method. Where the successful methods are rewarded as right moves and the error methods are considered to recheck the process. This is mostly done by AI agent and is taught successful strategies. Through the process, the AI understands how well it can make the right choice to avoid consequences without any explicit instructions from the programmer.
  • Meta-Learning:  This technique is a subfield of machine learning. It is programmed with certain types of algorithms that can teach a machine how to absorb the data. Simply how to learn what to learn. This helps the machine to upgrade in the form of skills and environments without the necessity of loading huge datasets. 

How should a human learn?

This question might surely sound surprising. But, to have a great equation with a machine, humans need to upgrade on few things and learn a few. 

  • Team game: Forming a potential team that is skilful in upcoming trends helps to work as a better team with those having skills in the current trends. This collaboration can bring out results that are meeting the current needs of a business and are also braced to encounter the new demands. 
  • Discover: There is never s straight line to great innovations. Sometimes the non-linear process of going two steps back can bring out great ideas that can create wonders.
  • Test: Learning is always a process that goes on forever. It becomes successful only when tested and then identified as the right way. So a successful project gives a lot of skill enough to embrace another one.
  • Fail:  The most common of all. It helps in a way better than a successful project. It gives clear guidance on what not to do. And, when it happens, stand again to start again. 
  • Lead: Ensure everyone on the team is coached and trained to take over the project at any given time and circumstance. Throughout the process of a project, situations keep on changing and one needs to be well informed to face anything.
  • Unlearn and upskill: Every time a new problem arrives, the same process that succeeded in the previous issue might be completely irrelevant to this one. So be open to unlearn outdated processes and upskill to the emerging solutions. 

From observing new technologies and how they are transforming digitally, it is very much evident that not just machines, but humans have a lot to learn to form a great equation. A good partnership between humans and machines can only be achieved if machines are made with accurate tools and technologies, and humans who can embrace diversity, nurture it across the industries and their ecosystems. 

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