“Machine Learning is defined as the study of computer programs that leverage algorithms and statistical models to learn through inference and patterns without being explicitly programed. Machine Learning field has undergone significant developments in the last decade.”
In this article, we explain machine learning, the types of machine learning and its applications in enterprise settings.
Machine Learning (ML) has proven to be one of the most game-changing technological advancements of the past decade. In the increasingly competitive corporate world, ML is enabling companies to fast-track digital transformation and move into an age of automation. Some might even argue that AI/ML is required to stay relevant in some verticals, such as digital payments and fraud detection in banking or product recommendations .
The eventual adoption of machine learning algorithms and its pervasiveness in enterprises is also well-documented, with different companies adopting machine learning at scale across verticals.
Today, every other app and software all over the Internet uses machine learning in some form or the other. Machine Learning has become so pervasive that it has now become the go-to way for companies to solve a bevy of problems.
In this article, we’ll dive deeper into what machine learning is, the basics of ML, types of machine learning algorithms, and a few examples of machine learning in action. We will also take a look at the difference between artificial intelligence and machine learning.
To understand what machine learning is, we must first look at the basic concepts of artificial intelligence (AI). AI is defined as a program that exhibits cognitive ability similar to that of a human being. Making computers think like humans and solve problems the way we do is one of the main tenets of artificial intelligence.
AI exists as an umbrella term that is used to denote all computer programs that can think as humans do. Any computer program that shows characteristics, such as self-improvement, learning through inference, or even basic human tasks, such as image recognition and language processing, is considered to be a form of AI.
The field of artificial intelligence includes within it the sub-fields of machine learning and deep learning. Deep Learning is a more specialized version of machine learning that utilizes more complex methods for difficult problems. One thing to note, however, is the difference between machine learning and artificial intelligence. While machine learning is probabilistic (output can be explained, thereby ruling out the black box nature of AI), deep learning is deterministic.
The process of self-learning by collecting new data on the problem has allowed machine learning algorithms to take over the corporate space.
With machine learning algorithms, AI was able to develop beyond just performing the tasks it was programmed to do. Before ML entered the mainstream, AI programs were only used to automate low-level tasks in business and enterprise settings.
This included tasks like intelligent automation or simple rule-based classification. This meant that AI algorithms were restricted to only the domain of what they were processed for. However, with machine learning, computers were able to move past doing what they were programmed and began evolving with each iteration.
Machine learning is fundamentally set apart from artificial intelligence, as it has the capability to evolve. Using various programming techniques, machine learning algorithms are able to process large amounts of data and extract useful information. In this way, they can improve upon their previous iterations by learning from the data they are provided.
We cannot talk about machine learning without speaking about big data, one of the most important aspects of machine learning algorithms. Any type of AI is usually dependent on the quality of its dataset for good results, as the field makes use of statistical methods heavily.
Machine learning is no exception, and a good flow of organized, varied data is required for a robust ML solution. In today’s online-first world, companies have access to a large amount of data about their customers, usually in the millions. This data, which is both large in the number of data points and the number of fields, is known as big data due to the sheer amount of information it holds.
Big data is time-consuming and difficult to process by human standards, but good quality data is the best fodder to train a machine learning algorithm. The more clean, usable, and machine-readable data there is in a big dataset, the more effective the training of the machine learning algorithm will be.
As explained, machine learning algorithms have the ability to improve themselves through training. Today, ML algorithms are trained using three prominent methods. These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
As with any method, there are different ways to train machine learning algorithms, each with their own advantages and disadvantages. To understand the pros and cons of each type of machine learning, we must first look at what kind of data they ingest. In ML, there are two kinds of data — labeled data and unlabeled data.
Labeled data has both the input and output parameters in a completely machine-readable pattern, but requires a lot of human labor to label the data, to begin with. Unlabeled data only has one or none of the parameters in a machine-readable form. This negates the need for human labor but requires more complex solutions.
There are also some types of machine learning algorithms that are used in very specific use-cases, but three main methods are used today.
Supervised learning is one of the most basic types of machine learning. In this type, the machine learning algorithm is trained on labeled data. Even though the data needs to be labeled accurately for this method to work, supervised learning is extremely powerful when used in the right circumstances.
In supervised learning, the ML algorithm is given a small training dataset to work with. This training dataset is a smaller part of the bigger dataset and serves to give the algorithm a basic idea of the problem, solution, and data points to be dealt with. The training dataset is also very similar to the final dataset in its characteristics and provides the algorithm with the labeled parameters required for the problem.
The algorithm then finds relationships between the parameters given, essentially establishing a cause and effect relationship between the variables in the dataset. At the end of the training, the algorithm has an idea of how the data works and the relationship between the input and the output.
This solution is then deployed for use with the final dataset, which it learns from in the same way as the training dataset. This means that supervised machine learning algorithms will continue to improve even after being deployed, discovering new patterns and relationships as it trains itself on new data.
Unsupervised machine learning holds the advantage of being able to work with unlabeled data. This means that human labor is not required to make the dataset machine-readable, allowing much larger datasets to be worked on by the program.
In supervised learning, the labels allow the algorithm to find the exact nature of the relationship between any two data points. However, unsupervised learning does not have labels to work off of, resulting in the creation of hidden structures. Relationships between data points are perceived by the algorithm in an abstract manner, with no input required from human beings.
The creation of these hidden structures is what makes unsupervised learning algorithms versatile. Instead of a defined and set problem statement, unsupervised learning algorithms can adapt to the data by dynamically changing hidden structures. This offers more post-deployment development than supervised learning algorithms.
Reinforcement learning directly takes inspiration from how human beings learn from data in their lives. It features an algorithm that improves upon itself and learns from new situations using a trial-and-error method. Favorable outputs are encouraged or ‘reinforced’, and non-favorable outputs are discouraged or ‘punished’.
Based on the psychological concept of conditioning, reinforcement learning works by putting the algorithm in a work environment with an interpreter and a reward system. In every iteration of the algorithm, the output result is given to the interpreter, which decides whether the outcome is favorable or not.
In case of the program finding the correct solution, the interpreter reinforces the solution by providing a reward to the algorithm. If the outcome is not favorable, the algorithm is forced to reiterate until it finds a better result. In most cases, the reward system is directly tied to the effectiveness of the result.
In typical reinforcement learning use-cases, such as finding the shortest route between two points on a map, the solution is not an absolute value. Instead, it takes on a score of effectiveness, expressed in a percentage value. The higher this percentage value is, the more reward is given to the algorithm. Thus, the program is trained to give the best possible solution for the best possible reward.
Machine learning algorithms are used in circumstances where the solution is required to continue improving post-deployment. The dynamic nature of adaptable machine learning solutions is one of the main selling points for its adoption by companies and organizations across verticals.
Machine learning algorithms and solutions are versatile and can be used as a substitute for medium-skilled human labor given the right circumstances. For example, customer service executives in large B2C companies have now been replaced by natural language processing machine learning algorithms known as chatbots. These chatbots can analyze customer queries and provide support for human customer support executives or deal with the customers directly.
Machine learning algorithms also help to improve user experience and customization for online platforms. Facebook, Netflix, Google, and Amazon all use recommendation systems to prevent content glut and provide unique content to individual users based on their likes and dislikes.
Facebook utilizes recommendation engines for its news feed on both Facebook and Instagram, as well as for its advertising services to find relevant leads. Netflix collects user data and recommends various movies and series based on the preferences of the user. Google utilizes machine learning to structure its results and for YouTube’s recommendation system, among many other applications. Amazon uses ML to place relevant products in the user’s field of view, maximizing conversion rates by recommending products that the user actually wants to buy.
However, as ML continues to be applied in various fields and use-cases, it becomes more important to know the difference between artificial intelligence and machine learning.
As American professor Douglas Hofstadter quotes, “AI is whatever hasn’t been done yet.” This is referred to as the AI Effect, wherein new techniques not only obsolete previous ones but also make the latter much more accessible and optimized for use. By this logic, artificial intelligence refers to any advancement in the field of cognitive computers, with machine learning being a subset of AI.
Today, the term ‘artificial intelligence’ has been used as more of an umbrella term to denote technology that exhibits human-like cognitive characteristics. As a rule of thumb, research in AI is moving towards a more generalized form of intelligence, similar to the way toddlers think and perceive the world around them. This could mark the evolution of AI from a program purpose-built for a single ‘narrow’ task to a solution deployed for ‘general’ solutions; the kind we can expect from humans.
Machine learning, on the other hand, is an exclusive subset of AI reserved only for algorithms that can dynamically improve on themselves. They are not statically programmed for one task like many AI programs are, and can be improved even after they are deployed. This not only makes them suitable for enterprise applications, but it is also a novel way to solve problems in an always-changing environment.
Machine learning also includes deep learning, a specialized discipline that holds the key to the future of AI. Deep learning features neural networks, a type of algorithm that is based on the physical structure of the human brain. Neural networks seem to be the most productive path forward for AI research, as it allows for a much closer emulation of the human brain than has ever been seen before.
Understanding the basics of machine learning and artificial intelligence is a must for anyone working in the tech domain today. Due to the pervasiveness of AI in today’s tech world, working knowledge of this technology is required to stay relevant.
Corporates are now in the middle of the adoption curve for artificial intelligence, mainly due to accessible cloud platforms and exponential advancements in the field. This makes AI an interesting career opportunity for those who have the capability and experience to take it up. Since this field functions as a combination of statistics, computer science, and logical thinking, it is varied in what it can offer to new entrants. Moreover, a variety of positions such as data scientists, machine learning engineers, and AI developers offer choices to aspirants across verticals.