Machine learning is a special type of algorithm which can learn from data and make predictions. As we collect and get more data from various sources, the predictions can be made more appropriately. However, on the other hand, there are pitfalls also which needs to be understood carefully. In this article, I will talk about the promises and pitfalls that machine learning brings to the table.
What is machine learning?
Before going deep into the topic, it’s extremely important to know what machine learning actually is. It is a subpart of artificial intelligence which focuses on learning through computation and by recognizing the patterns of provided data. It is now used to create such machines which can take decision on their own with the help of many sophisticated algorithms.
Using machine learning algorithms, machines will be capable of acquiring knowledge, knowing different things by exploring the real world, ask questions regarding the knowledge they acquire and so much more. These capabilities help the machine to think, understand, and likewise, even learn from their surroundings, find the logic behind every concept, predict and then make a prediction accordingly.
How machine learning works?
This concept is not actually very new. Machine learning is nothing other than a set of algorithms which can learn from the given pool of data and make predictions on them. Data and the accuracy of the prediction go hand in hand, so with more data, we get a more accurate prediction.
As such, it doesn’t require any predefined rules to govern its operation. This concept works in a continuous manner. It applies many different types of sophisticated algorithms automatically on a set of data to get better results. This continuous and iterative cycle helps in analyzing the surroundings carefully, predicting the right solution to a certain problem and taking the correct decision ultimately.
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Why machine learning is so important?
The answer to this question lies in few factors, which are the main triggers to make this concept successful. Let’s have a look at these factors:
Data to be used in machine learning
Nowadays, with the help of new technologies for database management, a massive amount of data can be collected at a very less cost. The companies which use these systems don’t have to think about which data to keep and which should be deleted. This used to be a very important question previously, as the data which used to have no relevance to the present situation could help in taking big decisions in the future. But with database system like Hadoop, storage of data has become very easy. This vast pool of data helps algorithms to predict the outcomes of decisions accurately.
Advance of compute
The computation techniques are also advancing gradually after Moore’s law. Different companies like IBM, Nvidia and more, are carrying out several innovations to improve the way of computation. These advancements help to create great computation techniques for processing the data in a better manner.
Sophistication in algorithms
This factor completely depends upon the data and computation technique. As the field of data management and computation management will flourish, the various ways of exploring the domain through algorithm also tend to do the same. The main work of these algorithms is to seek out different kinds of patterns, analyze them and give significant guidance to a stakeholder for making the proper decisions in less time frame. It also helps in reducing the cost incurred in making those decisions.
When these factors are optimized, they help in synthesizing a large number of data in information and knit many fragmented data into one source. This synthesized information can accelerate the performance of the future outcomes. Google uses the best computation technique and has a corpus of stored data. When it was facing problems in its image recognition for decades, they turned to the algorithm of machine learning and improvised it in just a few quarters.
Advantages of machine learning
Every business process can get benefits from data synthesis, as each process has different departments which have their own set of data. When these data are joined together in a meaningful way and in a reasonable time period, then a business can make proper decisions and grow further.
But, synthesizing these huge pools of data is not possible by an individual or by a group in a fixed time frame. Machine learning is like a savior in these fields, as it is an ideal way to exploit the prospects which are hidden in big data. It can extract information from a corpus of unrelated data with negligible human intervention. It runs on a machine and is driven by only the data stored. Unlike the conventional way which changes the outcomes as the new data arrives, machine learning learns from the data and flourishes on changing and growing sets of data. It is a way to discover different patterns which are buried in a data set.
What are the pitfalls?
Ideally, execution of this concept should bring growth exponentially but in reality, this concept also has some factors which can derail the growth line. These factors are discussed below.
Black box (For some algorithms)
Few approaches to algorithms are termed as black box depending upon the singular points of data and the understanding of the process. They sometimes create technical and cultural problems for an organization.
If a black box approach under performs, when the data is going through a significance change, then due to the lack of understanding, the system can be at risk. It’s very hard to explain why the model fails because it can set the organization’s growth towards a backward direction.
Selection of most appropriate algorithm
There is no master algorithm which is used as a standard for machine learning and which knows everything so, the selection process is very important. No algorithm can be perfect in different kinds of fields like anomaly detection, segmentation, generation of the different features, analytics, and pattern matching.
In the present world, there are many algorithms and many different approaches which come with their set of pros and cons and serve a particular purpose. Choice of the wrong algorithmic tool can increase the cost instead of decreasing, so it’s very important to know about every feature of the algorithm and use the best one depending upon the environment. The best way to solve this is to employ many different algorithms together and let the computation and framework decide which one to use and when.
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These systems can accumulate a technical debt over time as they are not self-optimizing in nature. Technical debts can show itself in many different ways like jungles of pipelines, entanglement, undeclared customer, hidden feedback, loops, data dependencies which are underutilized etc. They can result in obfuscation and unintended outcomes and can reduce the performance of the system drastically. This can be resolved by hiring mathematicians and engineers in a balance to plan the algorithm in a way as to reduce these debts.
The selection of algorithms is done by humans and can thus, be biased. This can lead to a situation of the wrong selection of an algorithm.
For example, a team whose members are graduated from the same school will have a tendency of choosing the same set of algorithms. So it’s best to inject your team with different kinds of algorithmic variety or employ many different algorithms together.
What is the future?
Our world is slowly transforming itself with the help of new and evolving technologies. Machine learning will help in guiding the drive to your destination by providing sufficient aid in the decision-making process. It will not only help in reducing the costs of a company, but also show the right way to improve the quality of a business by taking all surveys and data on the account. It shows promising traits of providing a better solution in the future.
Machine learning is such a concept which has gathered a lot of attention from the practitioners and will probably live up to the buildup. It is very transformative, so it has the capability to work on every workflow for any business. Any organization which will integrate this service in the right manner will be the beneficiary. But, it is also very important to know about both the sides of the coin for integrating it properly.