Irrelevant to the background or aspects of lives you are from, you must have heard about machine learning or have at least heard the word being used in the context of a conversation, a book, magazine, or a newspaper. So many of you might be eager to understand what it is, although the people from the engineering background will have an understanding of it or at least will have known about its functionality and what it means to the future.
Although Machine learning is a sector that is always developing and there is no fixed end to it and a limit in which it can be put on hold. Therefore the fact that more than 75 percent of the devices we currently use have an element of machine learning in them, from Youtube video preferences for you and the Next Netflix platforms they are even present in our day to day electronic gadgets. Moreover, there is an eminent presence of machine learning software in Amazon's Alexa, Google Assistant, and many other smart devices which have become the popular component of every household.
Today machine learning is at its prime and the year 2021 can even expect more from the industry. Moreover, the previous year 2020 yeah! a dreadful year for many due to the impact of Covid 19 has shown promising advancements in the machine learning industry. Moreover, the focus was on the predictive interface aspects of the machine learning curriculum. In the earlier days, the predictive algorithms posted the solution or used the best-mentioned operation providing the user with no idea on what the methodology or way of operations was chosen.
However, during the latter years, there had been more research was conducted into the field allowing users to not trust the machine learning algorithms blindly. Rather they are provided with answers on why the methodology or way is applied and quantitative analysis is also provided. The aspect dealing with providing answers to you on why the ML software chooses a method or operation is tagged as interpretable machine learning or explainable AI.
So what is interpretable machine learning and how can play a key role in the advancements of machine learning tools?
In a normal machine learning set up after the algorithm is completed the learning of a process and the input data is provided a predictive interface starts to function. Thereby proving the apt solutions as well as the reasons and quantitative aspects to choose it. This can be one of the simplified explanations on how interpretable machine learning or explainable AI might function. Moreover, as it's a growing context considering machine-learning the further developments in the field can be seen in this year as it was in the previous years. Furthermore, there are various reasons why the machine learning software companies are sticking to it, and here are some of them:
As you know a major portion of business establishments are functioning in a non-digitized way today and the transition to digitalization in a business is gradually progressing. One of the key facts about machine learning software that holds the establishments to further proceed with them would be the disbelief in their capabilities. Therefore, if you are one of the people of the above-mentioned category you will choose a machine learning software that has predictive functionality. Most of you will give a thumbs up to it. This will provide the software with much more popularity and a larger customer base.
Another part is that there is a right to explanation rule in practice which is to regulate the automated decision-making process of a company. However, there is a predictive algorithm whose context has major applicability considering the financial sector of establishments as most of them rely on it. Therefore their institutions have to explain the models and algorithms which they put into practice to have a transparent nature to the functioning.
The medical sector is another field in which machine learning is widely used nowadays and is the one where we would require advanced algorithms and solutions which matter in human life. Moreover, the impact of AI solutions providing an answer along with the explanations and reasons for it will be much appreciated by the concerned medical professionals.
There are furthermore examples of the importance of interpretable machine learning and how the future will have more advancements in it. Moreover, it's a sector of study which requires the highest quality working professionals and experts. Furthermore, today various career paths you can choose based on machine learning.