The Promises and Pitfalls of Machine Learning
Machine learning has enormous potential, but it must be properly implemented for it to be useful.
Machine learning is a special type of algorithm which can learn from data and make predictions. As we collect more data from various sources, machine learning can make predictions more appropriately. However, there are pitfalls which also need to be examined carefully.
What Is Machine Learning?
Before getting too deep into the topic, it’s extremely important to know what machine learning actually is. It is a branch of artificial intelligence which focuses on learning through computation and by recognizing the patterns of provided data. It is now used to create machines which can make decisions 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, asking 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 based on it. 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 ultimately making the correct decision.
Why Machine Learning Is So Important
The answer to this lies in few factors, which are the main causes for making 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 much lower 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, as the data which used to have no relevance to the present situation could potentially help in making big decisions in the future. But with database systems like Hadoop, storage of data has become very easy. This vast pool of data helps algorithms to predict the outcomes of decisions accurately. (To learn more about machine learning working with Hadoop, see Machine Learning & Hadoop in Next-Generation Fraud Detection.)
The computation techniques are also advancing gradually according to Moore’s law. Different companies like IBM, NVIDIA and others are developing several innovations to improve the methods of computation. These advancements help to create computation techniques for processing the data in a better manner.
This factor completely depends upon the data and computation technique. As the field of data management and computation management flourishes, the various ways of exploring the domain through algorithms 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 a shorter 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 amount of data and knitting fragmented data into one source. This synthesized information can accelerate the performance of future outcomes. Google uses an advanced computation technique and has a corpus of stored data. When it was facing problems in its image recognition for decades, they turned to a machine learning algorithm and improved it in just a few quarters.
Advantages of Machine Learning
Every business process can gain benefits from data synthesis, as each process has different departments which have their own sets 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.
However, synthesizing these huge pools of data is not possible by an individual or by a group in a fixed time frame. Machine learning is a champion 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. These factors are discussed below.
A few approaches to algorithms are termed as black boxes, depending upon the singular points of data and the understanding of the process. Typically, a black box is a system or algorithm which can only be viewed in terms of the input taken and the output provided. These algorithms or systems do not offer a view of the internal workings or the logic behind them, thereby offering only opaqueness (black). These are known to create technical and cultural problems for an organization.
If a black box approach under-performs when the data is going through a significant change, then due to the lack of understanding, the system can be at risk. It’s very difficult to explain why the model fails, and it can set the organization's growth back substantially.
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 algorithm selection process is very important. No algorithm can be perfect in different kinds of fields like anomaly detection, segmentation, analytics and pattern matching.
Presently there are many algorithms and many different approaches which each come with their own sets of pros and cons and serve a particular purpose. Choice of the wrong algorithmic tool can increase the cost instead of decreasing it, so it’s very important to understand 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.
Technical debts, with regards to programming, refer to cases where code that is easy to implement in the short term is often opted for, rather than the best overall solution. It is typically a very poor approach to programming and as such code can go on to develop deeper issues later, which are termed as debts.
These systems can accumulate a technical debt over time as they are not self-optimizing in nature. Technical debts can show themselves in many different ways like jungles of pipelines, entanglement, undeclared customers, hidden feedback loops, data dependencies which are unutilized, 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 so as to reduce these debts. (For more on the pros who implement machine learning, see Data Scientists: The New Rock Stars of the Tech World.)
The selection of algorithms is done by humans and thus, can be biased. This can lead to a situation where an improper algorithm is selected.
For example, a team whose members all graduated from the same school will have a tendency to choose 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 into account. It shows promising traits of providing a better solution in the future.
Machine learning is a concept which has gathered a lot of attention and will most likely live up to all the hype. It is very transformative, so it has the capability to work on any workflow for any business. Any organization which integrates this service in the right manner will see significant benefits. However, it is also very important to know about both the sides of the coin in order to integrate it properly.