Remember how kids are taught good behaviour? A child is given candy after dental checkups, chocolates for good grades and so on. This is called positive reinforcement. If you got an amazing gift from your parents after high school grades or a bonus for being an employee of the year that’s positive reinforcement right there. The objective of such micro rewards is to enforce good behavior and it works exceptionally well.
This also works the other way around. Every time you were scolded for bad performance at work or given low grades in your college research, that was negative reinforcement and it works just like positive reinforcement. When we use a similar approach on machines it’s called reinforcement machine learning. Now that you understand the concept it’s time that we look at from a technical perspective. If any point you feel overwhelming come back to the first 5 lines. That’s everything it really is.
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Reinforcement Machine Learning (RML) – What it means?
Reinforced machine learning is one of the most talked-about aspects of machine learning and artificial intelligence that is paving its way to bring a dramatic change in multiple industries. The learning is based on the combination of action and outcome (win or loss) system which is set in a controlled environment. Similar to humans learning a new skill which involves multiple trials and errors, the machine performs the task several times and uses the feedback of its actions and learns from the mistakes and experiences.
How Does Reinforced Machine Learning Work?
Reinforced machine learning involves three basic steps to perform a particular task:
- The first one is the state that describes the situation in which the activity has to be performed. For instance, a robot learning to place cubes in a box will have the state involving a number of cubes and a box where all the cubes have to be placed.
- The second concept of learning is an action that involves the machine or robot to perform the task. In the set example, the action would be to place the cubes one by one in the box.
- And the third part of the learning process is outcome analysis, which is the feedback from the environment. The reward can be either positive or negative depending upon whether the goal of the action is achieved or not respectively.
There are multiple tools and solutions that are using advanced reinforced machine learning to increase the efficiency of the tasks and they use the same mechanism to achieve a standard goal.
How We Leveraged Document Data Extraction Machine Learning Reinforcement?
KlearStack is an advanced platform that uses reinforcement machine learning to carry out the data extraction process from documents like invoices, purchase orders, and receipts. It can be employed for template-less data extraction from the unstructured documents which helps in increasing the operational efficiency of the departments.
The proprietary technology that enables such precision is called Intelligent Data Extraction (IDE). Using a combination of Optical Character Recognition, Machine Learning and Natural Language Processing we are able to contextually understand the meaning and placement of different elements of a document.
The system then allows a user to confirm the quality of output of the task. This works as a feedback mechanism for the solution and then it automatically performs better in future data extraction requisitions.
Our solution is pioneering the art of extraction and helping industries in managing their physical documents. The solution has shown to increase the efficiency of extraction to 90% in just 90 days and the reinforced machine learning ability embedded in the system helps in making that happen.
It’s high time that organizations across all domains and irrespective of the organization size should adopt such AI based tools to reduce the wastage of resources and leakages in the cash-flow.