Machine learning is a branch of Artificial Intelligence that can be defined as the study of various algorithms that allow computer programs to improve themselves through experience. Machine Learning is one of the many ways through which we try to achieve Artificial Intelligence. The concept of Machine Learning revolves around the idea of working with various data sets and finding a common pattern or exceptions in it by comparing the data.
Machine Learning in Our Everyday Life
Today we find Machine Learning in the products that we use in our everyday life. For example, Spotify has a recorded history of all the songs we have listened to, and based on those that we already like, it further recommends us other songs that we might also like. It is very similar to Youtube or Netflix’s recommendation systems that suggest videos or series that have a higher probability of being liked by you. Like this Machine Learning can be used in more places. For example, if we feed a machine learning program with a large data set that contains the picture of x-rays with a clear description of the diseases and symptoms, it is possible to make a program that would be able to assist the doctors with identifying a patient’s condition. What the program would have to do is find a pattern among all the x-rays in the dataset. When someone puts any new x-ray in the program, what it would have to do is compare the new picture with the patterns and categorize it into the parameters it has set while analyzing the given dataset.
Types of Machine Learning:
The type of machine learning that we just talked about is known as supervised learning. What supervised machine learning does is, it tries to learn the relationship between all the components in a dataset such that the output for the new data can be predicted as it has learned from analyzing the dataset it was previously presented with. Unsupervised learning takes a completely different approach towards predicting the data. It includes a vast amount of machine learning algorithms that are mainly used to detect patterns and for descriptive modeling. The data in unsupervised learning does not have output categories or even labels. In this type of learning, training of the model is done with data that is unlabelled. Reinforcement learning is another popular machine learning type. It makes use of the observations that it made from interacting with its environment. It takes actions that have minimum risk or maximum reward. Continuous learning from the environment is done iteratively. By making use of reinforcement learning, computers can be taught to play games and make it impossible for any human to defeat them.
What Is Artificial Intelligence?
As compared to machine learning, Artificial Intelligence is very vast in its scope. It can be simply defined as the engineering of making computers behave in a way that resembles human thinking but does not require any human aid. In today’s world, artificial intelligence is symbolic to the human-computer interaction devices and software such as Alexa or Google Home or even the prediction system that is implied by Amazon, Netflix, Youtube, and so many commercial or recreational sites. Without our realization, AI has already become a very important part of our everyday life even when we know so less about them. This is what makes it fascinating to learn about something that we are so much dependent on but so unaware of. AI is helping us every day to become more and more productive.
Unlike the static definition of machine learning, AI is moving. The meaning of AI is ever changing with technological advancements. It can be said that in the future, the AI that we have today would be as obsolete as a floppy disc.
AI – A Brief History
The term AI was coined by a group of researchers in the year 1956. The group included Allen Newell and Herbert A. Simon. Since that time, there has been a lot of changes in the industry of AI. With the growth of AI, various terms such as big data, machine learning, and predictive analysis also came into being and started getting popular. Deep learning and machine learning made huge progress in the year 2012 and many organizations started to use these terms for advertising their products. Then there came a time where deep learning was used as a solution to perform tasks that were impossible while considering the regular rule-based programming. Various fields boomed after that and took great leaps in development such as speech recognition, facial recognition, or even image recognition. These are also some of the technologies that we often find ourselves using in our day-to-day life.
Things To Keep In Mind:
While the fields of machine learning and artificial intelligence are very lucrative in monetary terms, big companies and the media continue to over-hype them creating a mystifying aura around the subject. Many companies even offer various AI solutions by often exaggerating them. When they fail to meet the exceptions of the customers, they would usually resort to hiring humans for the job but this creates a bad name for the AI and Machine Learning industry. Machine Learning and Artificial Intelligence are a product of the hard work of many people in the field and it is a gift of our time that makes us more productive every day and even makes our life much easier. It should be treated as a privilege that we have and not taken for granted like the fake or misleading advertisements that revolve around this industry. The saying, with great powers, comes great responsibilities, goes well with the field of Machine Learning and Artificial Intelligence. If the name is misused so much then we would have to see an unwanted downfall of the growth in this technology that we are seeing today.