Scientists and businesses have agreed with the fact that perhaps we have entered the world of Artificial Intelligence. Now, before planning to take up a project on Machine Learning, it is very important to be prepared for various tough challenges.
Deep learning algorithms are getting evolved day by day and proving that machines are powerful enough to play any complex game. Automation is seen nowadays in every industry.
Like – video suggestions, product purchase suggestions, image identification, driverless cars, customer support tasks, disease detection, and many other places.
Companies in the tech industry are struggling to explain to their customers about the technology they are offering, but this is never an issue with the Machine Learning project professionals.
Engineers think and believe that machine learning can do a lot. The biggest challenge is to develop such algorithms that can understand the query and reflect the accurate or closest result.
Artificial Intelligence is a new buzzword in the market. People are taking machine learning projects is like magic that will help to resolve any sort of problem. But this is not true.
Calculating the prediction of risk before taking a loan or opening a handset using face recognition are not magical things. Behind the complete picture, complex coding and algorithms play a vital role. It looks easy, but it is not simple at all.
Deep learning is comparatively new and is getting developed frequently. This requires a wide range of data that is properly structured with all the questions and answers which customers may ask.
Implementing a machine learning application is not a quick process. An organization has to invest effort, resources, and time to be successful.
There may be millions of parameters in an artificial neural network. A training set has thousands of records. Indeed, a neural network can remember all the training sets and give answers with full accuracy but for any question. That is new or not part of the database. This network would not respond and behave uselessly for the query.
This is one of the major limitations of implementing a machine learning application. Let’s discuss some more challenges.
Earlier the methods used in machine learning were simple. For example, the way a supervisor taught a decision tree, it acted the same as per rule means if something is green and oval, its probability to be a cucumber is high.
Such models could not recognize a cucumber in the picture, but everyone knew the exact procedure behind it.
Deep learning algorithms are not like that: they behave differently. They arrange the whole data set in a properly structured format like a hierarchy so that they can clearly understand and connect each part of the data with one another.
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After going through the whole data, the neural networks can easily identify a cucumber and reflect results with complete accuracy. The only problem is that scientists and engineers cannot understand how the network reads the whole data and gives back an accurate result; called a black box.
Artificial Intelligence engineers and scientists understand the data & its structure that a model analyses, they also understand the output generated by the model and they somewhat understand how a prediction functions too but there is no clear picture to them and even hard to understand how a model performs.
Some AI researchers agree that the overall field is like a black box. This is the biggest challenge in the AI algorithm: implementation for various purposes such as driverless cars, medical applications, and auto credit rating assessments.
What if an algorithm doesn’t behave as expected? This means, how will a manufacturer of a car explain the reason if the car undergoes an accident? How will a bank be able to respond to a customer query?
The black box is the biggest challenge in all these cases. It would be great if a web user has some knowledge about how the auto-suggestion works. This is the only reason many big brands are nowadays sharing some hidden secrets so that users can understand if a system behaves unexpectedly sometimes. Artificial Intelligence has created fear in human brains. People are discussing these days that soon AI will take the place of humans as it almost behaves like a human.
Due to human tendency, we can accept machine only if it works as a machine, not as a human. We feel good only if human beings are laughing, talking, and smiling. But, since we have entered the age of digital marketing, it’s common for new generations to interact with robots or algorithms.
Machine learning is the hottest technology in the modern market, but only a few engineers have information on how to implement it. A data scientist having expert knowledge of the machine learning process knows very little about software engineering.
Research says that the number of people for AI & machine learning research is increasing, but only a few have the right set of skills to deal with the tough or challenging AI problems. Many top brands such as Facebook, Google, and Amazon are looking for machine learning project engineers and data scientists who can help them in AI jobs.
If I talk about the salary part of data scientists and machine learning scientists or engineers. That is way too much, but there are average people too in this line. It won’t be wrong to say that with machine learning, we make things more complicated. As per the report, some professionals are early quiet high while some are receiving great packages as superstars [limited].
As we have already discussed, a machine learning model is trained with an enormous set of data. However, creating this vast set of information is no longer a challenge now. Companies can easily afford people to develop such data sets.
Indeed, this won’t take much of your amount, but you have to invest a good amount of time to get this data ready. And buying a fully prepared data set is an expensive option.
There are other challenges, too. It’s difficult to prepare a structure of the data set required to train a machine learning model. You must know what type of problem the model or algorithm solves.
The basis for this information, you can create a correct structure of data creation. Generate and maintain a process of data collection and persistent formatting. After preparing the whole data, cut it and make it more crisp and structured. Classify the entire process into several minor tasks. The overall process requires so many skilled engineers and time. No matter if you have an infinite storage capacity disk, the process is expensive and time-consuming.
With using personal data, there are more difficulties. Nowadays privacy is everything, and no company around the world is ready to compromise with the privacy of data. They would never allow using personal data and if they find that personal information is being used. They will register a complaint regarding the same since using someone’s data with his/her consent is no less than a crime.
Earlier, the software development was very simple. The process was defining a business aim, all the functionalities, a technology that requires fulfilling the aim, and some amount of time to get it released!
There are various layers in machine learning development. Machine learning engineers write a code that generates a unique program and addresses those goals that you have decided on as your business objectives. In this way, adding two or more layers makes machine development tougher than traditional software development methods.
Those machine learning projects take more time. Garnering the preparation of an extensive set of data is a time-consuming procedure. There are other issues, too.
This is the primary reason that in the traditional development approach, an experienced team member would calculate the approx. Time of project development complete. But with machine learning projects, no one can ever give the exact figure of the time.
The only reason is that even machine learning experts do not know how the neural network and deep learning algorithms will perform in the training with additional data sets.
Conclusion
Machine learning is a new technology and there are so many challenges in the ML project too. Even machine learning experts do not know whether a neural network will behave as expected.
Also, there are only a few machine learning professionals who can deal with critical issues of artificial intelligence. Be ready to accept all these challenges before starting a machine learning project.
Meet The Author
Digvijay Upadhyay has over 5+ years of experience as a Data Scientist at JanBask Training. I am providing online training to professionals and writing technical and inspiring or helpful blogs related to Data Science, Business Analytics, Machine Learning, business intelligence, etc.
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