The Exciting Evolution of Machine Learning

Machine Learning – ML: For businesses, particularly those that specialize in generating vast amounts of data like social media platforms, this opportunity may appear to be extremely lucrative. Regrettably, the present iteration of machine learning in industrial applications is exceedingly restricted and solely designated for accomplishing mundane duties. What is significantly lacking in the business world is the ability to fully comprehend, illustrate, derive genuine benefits, and effectively utilize these commonly used terms, which requires immediate attention.

In this blog post, I am attempting to create a short text movie of machine learning timelines to give a high-level view of machine learning evolution.

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For Basics around Machine Learning please read this.

Machine Learning(ML) – Introduction to Basics

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Machine Learning and AI

Artificial intelligence and machine learning are often used interchangeably, but they are not the same. Machine learning is one of the most active areas and a way to achieve AI. Why ML is so good today? For this, there are a couple of reasons, like those listed below but not limited to them.

• The explosion of big data
• Hunger for new business and revenue streams in these shrinking business times
• Advancements in machine learning algorithms
• Development of extremely powerful machines with high capacity and faster computing ability
• Storage capacity

Today’s machines are learning and performing tasks that were only done by humans in the past, like making better judgments and decisions, playing games, etc. This is possible because machines can now analyze and read through patterns and remember learnings for future use. Today, the major problem is finding resources that are skilled enough to demonstrate and differentiate their learning from university and PhD books in real business rather than just arguing on social media with others.

The adoption of a culture of machine learning within an organization is essential, wherein knowledge of this technological domain is imparted across business teams, managers, and executives at the most basic levels. To engender a cultural shift, it is necessary to implement sustained programs and conduct frequent road shows. Numerous educational programs have been developed with the explicit purpose of providing students, employees lacking experience, managers, professionals, and executives with a comprehensive insight into the mechanisms involved in effectively utilizing this remarkable technology within a business context.

The Journey of Automation

In a machine learning environment or setup, various algorithms and services are managed between the actual source of the data and the learning platform, which can be on the cloud. The ML of today, in all cases, does its work on centralized infrastructure. Though there are some success stories where it also runs exceptionally well on a distributed infrastructure, These methods may or may not be the most efficient, but for now, they work well.

The domain of machine learning has developed autonomously from its origins as a subset of artificial intelligence (although not comprehensively). The current situation has reached a critical juncture, particularly with regard to messaging. During my academic tenure, while attending a statistics course, I recall being enlightened about the concepts of Artificial Intelligence (AI) and Machine Learning (ML) which was met with a humorous response from my peers and I during the era of the 1990s.

Currently, the utilization of blockchain, quantum computing, and wireless connectivity as a means to support the Internet of Things on 4G/5G networks has become customary. It is noteworthy to consider that the emergence of 6G networks is on the horizon. These technological advancements have evolved from being intriguing concepts to standard practices.

The evolution has happened from Turing machines to the current very highly intelligent robots. How far machine learning has come from its origin is difficult to judge and measure, but the results are clear and visible. In recent years, the term ‘machine learning‘ has become very popular among developers and business alike, even though research in the field has been going on for five decades or more.

“A focal point where business, data, and experience meet emerging technologies and decide to work together”.

AILabPage defines machine learning in the same simple and easy manner as above. The classic example of ML, which we use knowingly or unknowingly on an almost daily basis,

• Managing our email box. Each time you click the “not junk” or “junk” button on a miscategorized email, the machine learns a little bit more.
• Using Siri, Cortana, or Google Assistant on smartphones

These tools become more efficient with usage, actions we take, or each interaction with them. Every time the machine learns from its mistake and adjusts the attributes, e.g., in the case of an email system, it looks at each email accordingly. In short, the more we torture the data, the better the algorithm gets in machine learning.

Machine Learning Approach

The approach to developing ML includes learning from data inputs based on “what has happened”. Evaluating and optimizing different model results remains the focus here. As of today, machine learning is widely used in data analytics as a method to develop algorithms for making predictions on data. It is related to probability, statistics, and linear algebra. Point out that it’s not just regression or a fancy version of regression.

Machine learning is classified into three categories at a high level, depending on the nature of the learning and the learning system.

1. Supervised learning: the machine gets labeled inputs and their desired outputs. The goal is to learn a general rule to map inputs to outputs.
2. Unsupervised learning: the machine gets the inputs without desired outputs; the goal is to find structure in the inputs.
3. Reinforcement learning: This algorithm interacts with a dynamic environment and must perform a certain task without a guide or teacher.

In recent years, modern technology has made remarkable strides, leading to the creation of machines capable of autonomous learning and performing tasks that were previously thought to be solely within the realm of human capabilities, such as optimizing decisions, making superior judgments, and participating in leisure activities. The capability mentioned before has now become achievable thanks to the development of sophisticated computer systems that possess the ability to efficiently analyze complex patterns, understand them, and memorize the obtained information for future use.

The current challenge relates to the lack of proficient individuals who can demonstrate and distinguish their academic achievements in practical business settings rather than just engage in online disputes with colleagues. This goes beyond what is typically found in university and scholarly literature.

Machine Learning’s Primary Goal

AILabPage defines machine learning as “a focal point where business, data, and experience meet emerging technology and decide to work together”. If you have not yet unfolded machine learning jargon, then please take a look at our machine learning post series library.

Machine learning is the process of a machine attempting to accomplish a task, independent of human intervention, more efficiently and effectively with every passing attempt, i.e., the learning phase. At this point, AI—a machine that mimics the human mind—is still a pipe dream. In the middle, we have the meat of the pipeline, the model, which is the machine learning algorithm that learns to predict given input data.

Machine learning is a subset of artificial intelligence where computer algorithms are used to autonomously learn from data and information. Machine learning, however, has been a reality in our lives for quite some time. On the other hand, deep learning is a subcategory of machine learning algorithms that use multi-layered neural networks to learn complex relationships between inputs and outputs.

The most basic rule and meaningful point to understand anything about machine learning is the quality of the data it gets served with. So this means it’s purely driven by data sets to provide answers. It follows the GIGO method. The set of algorithms is as important as the underlying data sets.

Machine Learning Evolution

The image mentioned as “Machine Learning and Some Use Cases” showcases notable developments that have significantly aided in resolving real-world problems.

In particular, these advancements have yielded optimistic outcomes for solving practical problems. There are many examples of organizations making considerable progress and improving their operations through diverse educational opportunities. Having a profound understanding and mastery of a specific business matter is essential for making wise choices about implementing optimal management approaches.

Each type of machine learning provides a strategic and competitive advantage, but the availability of quality data based on which technique is chosen is far more important. The types of machine learning algorithms and which one should be used when are extremely important to know. The goal of any machine learning task and all the things that are being done in the field that put you in a better position is to break down a real problem into design form for machine learning systems.

ML develops its own encompassing strategy from the experiences it comes across over the course of time. Sir Andrew Ng says AI is a new form of electricity, so, in my opinion, ML is no different. This has been one of the most active and rewarding areas of research due to its widespread use in many areas. Some of the major breakthroughs in ML include natural language processing and deep learning.

Points to Note:

All credits, if any, remain with the original contributor only. We have covered all the basics of machine learning. Machine learning is all about data, computing power, and algorithms to look for information. In the upcoming post, we will cover a new type of machine learning task under the umbrella of generative adversarial networks. A family of artificial neural networks.

Books + Other readings Referred

• Research through the open internet, news portals, white papers, and imparted knowledge via live conferences and lectures.
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you have any questions about Supervised Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.

Conclusion – Machine learning is a branch of computer science, unlike statistics, which is a graphical branch of mathematics. Though it’s not entirely true, up to some extent, statistics can be called a graphical branch of mathematics (in my personal opinion). ML deals with the development of computer algorithms that learn and grow themselves. Machine learning can be very task-oriented as well. In a specific environment, a machine can be forced to learn just the rules of a game. It had to try out millions of different strategies for each situation in this case before it prepared itself to play against its opponent. For example, a game of chess or backgammon

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