The term machine learning may not mean much to a layman. But, when we say Artificial Intelligence (AI), the notorious image like the blockbuster hits Terminator, and the character of Skynet will pop up your head. If you are a bit optimistic about these technologies, your mind may fill up with the futuristic movies like Star Wars.
Machine Learning is a type of Artificial Intelligence that has gained significant traction in the previous few years, and it is affecting every facet of our lives. Machine Learning is making its place in our daily lives even as the great science and technology visionaries like Bill Gates, Elon Musk, and Steven Hawkins and others have warned that the AI robots may turn against us and may banish the human race.
Data Handling Capability
One of the most robust features of machine learning is its data handling capability. In 2018, approximately 4 billion people were using the Internet. 3.5 billion searches took place on the Internet and counting. The total time humanity spends on the Internet is 4 billion years, every year. It is a lot of data. The Internet has become an enormous data generation machine, and most of it as valuable as gold for data experts and organizations where they work. Organization and handling of this data would have never been possible without machine learning.
A machine learning system looks at the patterns in the data. Once the data is processed, it results in actionable insights for organizations that either is doing good for the value of these companies or doing good for humanity in many different ways, such as indicating nearby healthcare facilities in the case of a disaster.
Machine learning AI equipment is playing a pivotal role in the fight against the devastation of environmental damage. The enormous data gathered from several thousands of sources through AI-enabled sensors with the help of machine learning is helping ecological scientists to understand the ecological changes better and to devise new solutions.
Dirty, Dangerous and Daring Tasks
In the case of bomb disposal, now there is no need to risk human lives. Machine learning and AI-enabled robots are taking care of the tasks that are too dangerous or dirty such as in the case of handling hazardous materials like nuclear waste or harmful chemical disposal.
AI Robots equipped with machine learning software are already interacting and communicating with elderly people, keeping them engaged with entertainment, and providing them company if they are lonely.
Shortly, for the elderly who have problems moving around and doing regular work because of their weak muscles or ailments. Equipped with machine learning and AI these robots will make their lives more comfortable and independent by helping them with their daily personal routines such as eating, going to the washroom, bathing, moving around, and even helping them in doing exercises.
Innovative and Secure Banking
The Machine Learning AI software has its applications in the banking and financial industry also. The biggest threat and loss to the banking sector comes from fraud and network hacking. To control such incidences and to curb the fraudulent activities, the banks employ sophisticated and state-of-the-art Machine learning systems that look at the patterns in the banking data and detect unusual or suspicious activities before an incident occurs enhancing the security.
Improvement in Healthcare
The old healthcare records had piles of sheets that the physicians and other medical professionals had to go through to make sense of the medical needs of the patients and to prescribe appropriate medications. The healthcare records are going online and becoming digitized and helping doctors to have insights into the illnesses of patients in the shortest time possible by giving precisely the data he or she needs to make decisions. All this had been made possible through machine learning and data science applications.
Smart Domestic Security
Machine learning AIs are also making their impact on household cameras and surveillance systems. The CCTV Cameras now come with AI and machine learning facial recognition systems keeping the data of frequent visitors and can detect strangers and newcomers to warn you before they enter your home premises. These systems are capable enough to inform you when the kids come home, thanks to machine learning. They can track the movement of pets in the compound, for instance. These systems are also intelligent enough to call security services if need be.
These are just a few of the applications of machine learning that are shaping our world at a dramatic pace, and soon there will be many other applications that we cannot even imagine at present
How Machine Learning Will Change the World?
We will have a look at how Machine Learning has progressed and evolved. In the end, we will look at some emerging technologies and the way machine learning will affect the world. Machine Learning (ML) is a silent revolution that is taking place having a profound impact on different sectors of our societies, such as health, communication, transportation, and many more. The Historical Perspectives of Machine Learning are the current concepts of machine learning that culminate from two significant perspectives which are the perspective of Statisticians and the perspective of Computer Scientists.
The Perspective of Statisticians
The Statisticians’ perspective is related to the study, understanding, and interpretation of data. The field of data analysis has its origins in mathematics, more specifically, statistics. In Stats (short for statistics), the persuasion of the fundamental questions took place to understand the world that surrounds us through the study of events.
Probability is a concept of statistics in which an understanding of how likely an occurrence can happen, is studied. The models based on probability were devised in statistics to model the world around us. The following one simple examples will allow you to understand the statistics and probability if you are new to the subject.
Example: The statisticians analyzed several books and recorded the number of times a word occurred (word frequency) in a body of text and attempted to create a model that explained which word was the most widely used or least used in the English language for instance.
Statisticians did the study of data to understand and make predictions of the world around us. But as it turned out that these models were an oversimplification of the complexities that exist in the real world, and the probability-based models did not reveal satisfactory results in simulating the actual data observed in the real world. Here where the perspective of the computer scientists jumps in, and with the crossbreed of statistics and computer science, the field of machine learning was born.
The Perspective of Computer Scientists
During the 1950s, computer experts started to realize that the hard coding of every decision that a computer is programmed to make in a never-ending if and else statements are causing the computer systems to become very inefficient. Computer scientists foresaw that it would be better, that computer could look at data and recognize patterns and make the best decisions. This idea of identifying patterns in data and making decisions is the basic idea behind Machine Learning (ML).
To avoid hard coding, the programming rules to make decisions as mentioned before, the rules of distribution from statistics got into play. But, using the distributions of statistics as generators of data created huge discrepancies between the data generated by statistical models, the data generated in the real world. In other words, both types of data did not reveal the same results.
Despite that, machine learning continued to evolve with extremely flexible models based more and more on data and lesser on human coded if and else statements. These ML models have proven to be better than previously crafted modeling techniques. The current ML models became so accurate in correlating themselves with the real world that predictions have become a key output after sufficient data input. The prevailing models are often very complex and flexible, and they deceitfully outperform the models that follow if-else statements and the distribution models that are well known as probability distributions.
One most significant disadvantage of these ML models is that they do not explain the “Why” aspect of the model. But these ML models help us better understand cancer tumors better than medical doctors and can beat the champions in the game of “Go.” But the creators of these models cannot always explain “Why” the algorithms are making these decisions.
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