Machine Learning: Advantages and Disadvantages

“A baby learns to crawl, walk and then run.  We are in the crawling stage when it comes to applying machine learning.” 

~Dave Waters

 

 

Concept of Machine Learning

 

Machine learning comprises of three components:

 

1. Algorithms are one main component of machine learning. It is the core of everything in machine learning.

 

2. The next component are variables that make up the decision.

 

3. A base data or knowledge on which the machine trains and learns.

 

Whatever the machine we are taking, the first thing is to feed data to it to set the parameters. Then the algorithm is tested and the result is checked with the correct answer. This comparison helps to undermine the discrepancies in the results. 

 

Then more input data is fed to the machine to see if it is able to judge and perform. It is done to check the computational decisions of the machines. All those businesses that are data-driven have to implement some automated set of machines. 

 

Machine learning helps companies to make customer data more organized, systematic, and even more useful. In short, we can say that machine learning is the science of teaching machines how to become self-reliant.

 

 

Uses of Machine Learning

 

There are so many sectors in which Machine learning is being used and helping all the industries by improving their customer service, bringing automation, and helping in innovating the products. Given below are some major industry types and how machine learning is helping them:

 

1. Manufacturing Industry

 

Monitoring the condition and predictive maintenance are two areas in which machine learning has helped the manufacturing industry.

 

2. Retail Sector

 

In the retail sector, the marketing channels are quite streamlined and machine learning has helped in logistics as well as making the channels automated.

 

3. Healthcare Sector

 

The Healthcare sector is the most technology-driven sector. With the help of machine learning, disease identification is quite easy as machines are very well equipped. Apart from it, risk satisfaction is also a benefit of machine learning.

 

4. Hospitality Sector

 

The hospitality sector is related to travel and tourism and in this segment, prices fluctuate. Machine learning also helps in dynamic pricing and improving customer experiences.

 

5. Financial Sector

 

The financial sector is related to stock marketing. It is the most fluctuating sector because of the volatility of the stock prices. In this machine learning helps in risk analytics as well as forecasting the prices along with regulatory compliances.

 

6. Energy

 

Energy-related sectors like electricity generation, solar power, kinetic energy etc also have installed machine learning in their machines to forecast demand and optimize the supply of energy.

 

Disadvantages of Machine Learning

 

“Machine intelligence is the last invention that humanity will ever need to make.” 

~Nick Bostrom.

 

Nothing is absolutely perfect. Likewise, even though Machine learning has a wide range of applications there are some flaws and disadvantages of it as well.  Machine learning has some serious errors in it which are worse than human errors. 

 

What will happen if you train the machine wrong? Or if the algorithm you entered is incorrect. What will be the outcome in that case? The results will be wrong or they will be biased. 

 

Moreover, some mistakes can go unnoticed for such a long time that all your results since that initial point will be wrong. Imagine the time it will take to correct all those flaws. Let us look at some of the disadvantages of machine learning.

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Disadvantages of Machine Learning

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1. Data Acquisition

 

Machine learning is based on data. Data is acquired from multiple sources. If the data source is not reliable then we cannot expect the correct outcome. Most of the time the external sources can be wrong. 

 

The quality of the data is also important. If you do not have data then you have to wait for the data. It will cause time delays. A machine needs to process a large amount of data to learn how to become self-reliant. So, machine learning depends on data.

 

2. Time and Resources

 

Since the data that the machine processes is huge in volume, the time is taken to do it also varies quite a lot. Basically, the machines need sufficient time so that they can adapt to the algorithm and learn it. A machine has to develop so that it can function as per the algorithm. 

 

There are certain trial also runs to check the reliability and accuracy of the machine. It is not only about the time but also the resources. The resources are both monetary and the set up or the infrastructure. Electricity is also needed to power the computer. 

 

So all the resources required for the machines will lead to high costs and the time wasted for the algorithm to set is also a huge waste of resources. Because during that time no outcome is given. Trial runs are also costly. So money is a very big disadvantage in machine learning.

 

3. Interpretation of Results

 

One of the biggest disadvantages of Machine learning is that it might not interpret the results accurately. Why? The algorithm set is the basis of interpretation and if that is faulty then the results will be interpreted wrong. 

 

The algorithm must be accurate and developed to such a state that the results are analyzed and shown reliably. For all these resources will be required in a huge quantity. Now the only solution for this is that the algorithms must be designed with extreme care and then entered accurately so that the results are perfectly clear.

 

4. High-Error Susceptibility

 

The errors seen in the results of machines are so massive that if not corrected at the initial stages it will create a blunder. Biasness is one thing but wrong results are another issue. Machine learning relies on only two things. 

 

One is data and another is the algorithm. All the issues in this technology are based on errors in these two variables. Any defect in one will lead to erroneous conclusions.

 

Also Read | Extraneous Variables

 

Advantages of Machine Learning

 

“Machine learning will automate jobs that most people thought could only be done by people.” 

~Dave Waters

 

Machine learning is a very wide concept. Often people get confused between deep learning and machine learning. The difference is pretty simple. Machine learning is all about making the machine self-reliant and workable with the data. 

 

It is about developing a thinking capacity in the machines so that they can analyze the data and make all decisions quickly.  Let us study some advantages of machine learning :

 

1. Automation

 

Machine learning has made everything more self-reliant and more self-driven. There are so many machines that work independently without human intervention. In many cases, you can teach them and train them how to work. 

 

Artificial intelligence has done wonders when integrated with machine learning. Machines like washing machines are now equipped with machine learning technology wherein they adapt easily to their surroundings and then work even if the owner is not at home.

 

2. Patterns of Analysis

 

Machine learning is all about patterns. It means once the machine has been provided with data it automatically starts reading patterns in it and checks how the data flows. 

 

By analyzing all the data it is able to generate the best results and with a lot of accuracy. For individuals it can be complicated but for machines it is much easier.

 

3. Applications are varied

 

Machine learning has numerous applications now. We are in 2022 and now we can see so much of technology, AI, and machine learning. The Internet has literally become one of the crucial components of living today.

 

Some of the applications of machine learning are- stock forecasting, sales prediction, operations management, analytics of products, fraud detection, and many others.

 

4. Handling Data

 

You have no idea about the data that is generated each year. It is humanly impossible to process it or even organize half of it. But now we have automated tools and softwares that help us sort, organize, analyze and interpret all that data. 

 

Machine learning has literally made our lives a lot easier than we thought. Try removing all automated and machine-learning tools away for 1 day. You will realize what you are missing.

 

5. Execution

 

Execution is the most important aspect of any plan. With the help of machine learning, you can not only execute the task that you are doing but also do other tasks simultaneously. 

 

Moreover, no human interference is required when machine learning is present. In the case of a single task, you get the option of multi-dimensional utilities for doing a job.

 

6. Improvement

 

Both AI and machine learning are ever-evolving concepts. There is no stop as to where they will grow. Constant improvements are being done by experts in these fields. We can never reach the end level in machine learning. 

 

Machine learning in itself is such a wide concept that every device will automatically learn multiple new things when exposed to new situations. The programming is done in such a way that based on past data or actions it will itself develop into something better. So the path of improvement is always ongoing.

 

7. Opportunities

 

Because of machine learning, human intervention has decreased but that does not mean that job opportunities for human resources have completely vanished from the market. The market always needs talented, skillful, and knowledgeable people. 

 

Moreover, because of machine learning so, many new fields and techniques have been discovered that people are needed for research in those areas. So, yes machine learning has opened up new opportunities for people.

 

8. Reduction of time and Complexity

 

The time taken to do a task or job has reduced significantly as everything has become automated and streamlined due to machine learning. Human errors are also reduced and every task becomes biddable. 

 

Also Read | Uses of Machine Learning in Healthcare

 

Machine learning has replaced humans so much that the future might be in danger. The job opportunities will be present but not sufficient for everyone. There is no field in which machine learning is not extending itself. Machine learning is definitely a boon for the people but if not used properly it might become a bane too.