The Potential & Limitations of AI: A Critical Examination of Machine Learning

Machine learning is a field of artificial intelligence that allows its system to learn & get improvement through the help of experience. This topic is getting more popular in recent years due to its many practical applications. We will learn about the basics of machine learning, by discussing advanced topics to show how it helps to solve real-world problems. To go through this detail whether you are a beginner or you are going to learn about machine learning. You will stay up-to-date on the latest developments it brings.

What is Machine Learning?

Machine learning is an application of artificial intelligence that will use statistical techniques to enable computers to learn & make decisions without using programs.  In this study, we can look at it as a subset of Artificial Intelligence. It is related to the study of machines acting human-like in their behavior to get the ability to learn how to develop their plans. This learning process gets improved by the means of experiences through the process of machines.

History of Machine Learning

In this portion, we will get to know about the development of machine learning throughout the past years. All this began in the year 1943. The big shift happened in the 1990s when machine learning moved from being knowledge-driven to a data-driven technique due to the availability of huge volumes of data. Open AI is an organization that was created by Elon Musk in 2015 to develop safe & friendly AI to provide benefits to humanity.

What is the reason for learning the Machine Learning?

Machine learning is a tool which is powerful that can be used to solve a large number of problems. It provides a helping hand to computers to learn from data.

Here are important reasons, why machine learning is getting more important:

• In most industries, Machine learning is widely used, it includes healthcare, finance & e-commerce. By using this tool you can open up a wide range of career opportunities in all these fields.
• Machine learning is used to build intelligent systems which can make decisions related to data predictions. This organization can help to make better decisions to create new services.
• It is an important tool for data analysis. It makes you able to get insights from large datasets. This makes you understand complex systems.
• Machine learning is also becoming a large growing field. When you learn machine learning, you can stay aware of the latest research or progress in these fields.

Types of Machine Learning

There are three main types of Machine learning:

Supervised Learning

This first type of machine learning is a class of problems that usually use a model to learn the mapping between the input & target variables. This kind of application consists of training data while describing target variables.

Read Also: Chatbots Vs. Conversational AI What’s The Difference?

There are two types of supervised problems: Classification – which involves the prediction of a class label to involve the prediction of a numerical value.

Unsupervised Learning

An unsupervised learning problem model tries to learn by itself to recognize different patterns. Unlike supervised learning, this type deals with input variables. There are no targeted variables in this learning process.

There are further two main categories of unsupervised learning; where the task is divided into different groups of data.

Reinforcement Learning

Reinforcement is the type of problem where there is an agent and this agent operates in an environment that is based on feedback or reward. The reinforcement agents deal with the steps to perform a particular task. There is no fixed dataset & the machine learns on its own.

Applications of Machine Learning

These kinds of applications help in building intelligent systems that work with a combination of their past experiences & historical data. Healthcare, defense, financial services, marketing, and security services make use of machine learning.

• Image Recognition

The most common application is image or facial recognition, its simple example is the iPhone. There are many security purposes like identifying criminals or searching for missing individuals.

• Speech Recognition

Speech recognition is used to convert speech into digital text. It provides its users to perform tasks based on human voice inputs. Speech patterns & vocabulary are used in this system to train the model.

• Financial Services

Machine learning has many applications related to financial services. Machine learning algorithms help to detect fraud by monitoring the activities of every user. It also helps to maintain decisions related to various trades.

• Marketing & Sales

Marketing & sales analysis deals with various parameters such as website visits, email checking, downloads, etc. It also helps businesses to improve their dynamic pricing models.

Read Also: The Importance Of Diversity In AI: Addressing Bias And Promoting Inclusivity

Machine learning also helps computer visions to help brands identify their products by using images & videos. Chatbots are also giving more responsive & intelligent tools.

• Healthcare

These technologies are becoming more helpful to search for diagnoses or predictive analysis. Scientists around the world are using machine learning technologies to predict epidemic breakouts.

Future of Machine Learning

To conclude: machine learning is presenting a constantly developing field that is influenced by numerous factors. Machine learning appears to be a major force in many fields of science, technology, and society.

It is also likely that machine learning will continue to advance with time. By use of new developments & algorithms to make machine learning more powerful & effective.