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Machine Learning and Artificial Intelligence– the Branch of Computer Science That Is Shaping the Future of Humanity
The two terms, Machine Learning and Artificial Intelligence (AI), have been the tech town talk for the past couple of years. Tech giants like Apple, Google, Microsoft, IBM, etc., use Artificial Intelligence to make breakthroughs every day. Machine Learning and Artificial Intelligence enabled companies like Tesla to make the dream of self-driving cars come true. AI will write the future of human beings (and robots, too!).
But what exactly are Machine Learning and Artificial Intelligence, and how do machines actually ‘learn’? If that is what you are thinking, then my friend, you have just landed on the right article.
Today, we will talk about Machine Learning and Artificial Intelligence, what they are, how machines actually ‘learn’ things, the latest advancements in the field of AI, and what this all means for the future of our world. Sounds intriguing, right?
So, let’s get started!
What Is Machine Learning?
Humans are capable of thinking independently and learning things from their past experiences, and machines follow the instructions given by humans; we all know this. But have you ever thought about what would happen if humans could train machines to learn and evolve from their data and past experiences? That, in layman’s terms, is what Machine Learning is all about.
The term Machine Learning was given by Arthur Samuel in the year 1959. Samuel was an American IBMer and was a pioneer in Artificial Intelligence and computer gaming. In his words, Machine Learning is “a field of study that gives computers the capability to learn without being explicitly programmed.”
Machine Learning is a subset or a part of Artificial Intelligence. It studies developing applications or machines capable of learning from data and improving their predictions over time with minimum human intervention.
It focuses on training algorithms to find patterns in the enormous amount of given data to make accurate predictions based on new data. Of course, the greater the data’s size, the more accurate the predictions will be. And as the big data keeps on getting more significant, the Machine Learning models will ultimately make more accurate predictions.
Today, we can easily find examples of Machine Learning all around us. Self-driving cars, face recognition in smartphones, video suggestions made by platforms like YouTube are all examples of Machine Learning that we encounter every single day in our lives.
But the real question is, how do machines actually ‘learn’? Well, you need to keep reading to find that out.
How Do Machines Actually ‘Learn’?
As we have already discussed above, in Machine Learning, algorithms are designed to find and learn patterns in a massive chunk of data to make them capable of making decisions and predicting logical and fairly accurate outputs. AI can learn via three different methods:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Let’s have a quick look at each of these methods.
1. Supervised Learning
Supervised learning is the method in which the ML models are trained with the help of properly labeled data sets. Meaning some data is already tagged with the correct answer. These labeled data sets are provided to the algorithms, and they are then allowed to analyze them and produce interpreted functions/ outputs.
This method is used in most practical ML models. What happens here is that the data scientists take input variables (X) and an output variable (Y), and they use an algorithm to comprehend the mapping function from the input to the output.
Y = f(X)
This method aims to approximate the mapping function in such a good way that when you give a new input data (X) to the machine, it can predict the output variables (Y) for that data with maximum accuracy.
A supervised learning algorithm helps in accurately predicting outcomes for unforeseen situations. However, successfully developing it requires much time and the technical expertise of highly trained data scientists who act as a supervisor.
2. Unsupervised Learning
Unsupervised learning is the Machine Learning method where the data scientists only have the input data (X), and there are no corresponding output variables (Y).
The main goal for unsupervised learning is to let the Machine Learning model work independently to discover the information without human supervision, hence the name- unsupervised learning. Unlike the supervised learning method discussed above, there are no correct answers (labeled data sets). The algorithms are then allowed to find out and demonstrate the data structure on their own.
3. Reinforcement Learning
The reinforcement learning method is a behavioral Machine Learning model. Here, the labeled data set is not used to train the algorithm; instead, the algorithm learns on its own by trial and error method. The method enables an agent to learn through the consequences of actions based on a specific environment and situation.
So, a series of correct predictions and outcomes would strengthen the ML model and give the best recommendations for a particular problem.
The best example of reinforcement learning is the recommendation engines used by the platforms like Netflix, Amazon Prime, YouTube, etc., that recommend new shows, videos, and movies based on what you have previously watched. For example, after finishing a show, you must have noticed that Netflix usually recommends several other similar shows that it believes you’d like. So, suppose you start watching the recommended show but leave it without even finishing the first episode. In such a case, the machine understands that the recommendation it made was not a good one and will try another approach next time.
Artificial Intelligence: What It Is and the Its Latest Advancements
Now that we learned about Machine Learning basics, let’s briefly look at what Artificial Intelligence is all about.
Artificial Intelligence or AI is a branch of Computer Science that is primarily concerned with developing ‘intelligent’ machines capable of performing tasks on their own that typically require human assistance.
That was once a mere dream, and distant fantasy has now become the reality of our world. Today, Artificial Intelligence is making considerable breakthroughs in almost every realm of the human world. Be it medicine or finance, hospitality, or the automobile industry, AI applications can be found everywhere.
Self-driving vehicles, which are getting massive popularity nowadays, are built using Artificial Intelligence applications. The robots that you see today are brilliant machines built with the help of AI. The Siri and Google Assistant you rely so much upon are nothing but the gifts of Artificial Intelligence.
Latest Developments in the Field of Artificial Intelligence
Artificial Intelligence is changing the way the human world works, one advancement at a time. Let’s look at five such latest advances in Artificial Intelligence.
1. Fully Automated Vehicles
The first one on our list is the one that has become one of the most discussed topics in the world right now–self-driving cars. Of course, the partial credit goes to companies like Tesla that have made automated vehicles such a rage today.
Fully automated vehicles are built with Artificial Intelligence applications, and they allow rides without a human driver behind the wheels.
2. The Vaccine Development
Gone are the days when developing a vaccine used to take nearly a decade.
Today, it can be done in less than a year, thanks to the AI models that enable scientists and researchers to analyze a massive amount of data at a fast pace. This is one reason why the COVID-19 vaccine came to the market within a year after the first case was reported.
3. Replication of Human Voices
AI can now successfully clone human voices and can deliver high-quality voices. This feature is primarily used in the podcasting industry. Although it lacks emotions and feels robotic right now, the day is not far when AI will create voices precisely like human beings.
4. Advanced Shopping Experience
Online platforms and apps use Artificial Intelligence to provide unique features and shopping experiences to their customers. For example, you can easily replace your image with the model’s image to get a virtual trial of things you are thinking of buying.
Moreover, the tech giant, Facebook, has built a computer vision model with the help of AI, called GrokNet. It is developed to redefine the shopping experience by acting as an AI lifestyle assistant who learns people’s tastes and preferences and suggests the top products. Gone are the days of confusion and buying things based on your intuition. Instead, online shopping is predictable and so much fun today!
5. Natural Language Processing
Natural language processing systems have become significantly advanced over the years, thanks to AI. They have become much better at understanding the language and the intent and emotion of humans. These speech-processing models are getting better and better with each passing day, enabling businesses to create more advanced chatbots, virtual assistants and provide accurate voice search results to the users.
Looking at the new milestones that Artificial Intelligence (AI) and Machine Learning reach every day, there is little doubt that AI will significantly affect the human world in the future. Many of us encounter AI and use it multiple times in a day without even realizing it; that is the power Artificial Intelligence holds in today’s world.
While there is no doubt that the continued advancements in AI will take us to new heights, one should not ignore the fact that it is a two-edged sword. The dangers of AI applications, especially in the field of modern-day warfare, can’t be ignored.
While talking about the power of AI, Bill Gates once said that it is “so incredible, it will change society in some profound ways.”
However, Artificial Intelligence still has a long way to go, and we have to wait and see what it has in store for humankind’s future![/vc_column_text][/vc_column][/vc_row]