The next significant achievement of mankind after the industrial revolution is perhaps the introduction of digital technology and an increase in digital infrastructure. In the Internet of Things (IoT), Cloud computing, Artificial Intelligence, and Machine Learning, there is a humongous requirement of data analysts, Big data scientists, etc. With the recent pandemic situation hovering all over the world, people are forced to sit at their homes. The technological wave has swept almost in every sector, be it education, or any other industry.
Artificial intelligence and Machine Learning (ML) are two different words that are often perceived to be the same. People use these two words at times, simultaneously without realizing the difference between the two.For students and people who are just curious about tech or anyone who wants to establish a big data career, this article will help distinguish between the two terms AI and ML.
Before I begin on how the two terms are different and the similarity between them, let me give a small gist of what the two are. Artificial Intelligence and Machine Learning both stem out from the concept of machines and technology. But Artificial Intelligence is a much broader concept that encompasses Machine Learning. ML can be thought of as a subset of AI. With this very basic concept, let’s jump into the activities of both!
What is Artificial Intelligence (AI)?
Artificial Intelligence is a vast field of making intelligent systems using computers where human decisions are basically mimicked. As we all know, computers and machines were essentially used to make human lives more comfortable. But with AI, algorithm or machine language is tweaked in such a way that there is no requirement of human assistance; instead, the technology does everything.
The prime example is that of Siri and Alexa. You must have noticed that there are suggestions for songs, videos, or movies on Netflix, YouTube, and other streaming channels. This is also the working of AI. These days, more and more websites are opting for AI bots to assist people visiting the website for the first time. You must have noticed the pop-up of ‘May I help you?’ on some sites! This is another example of an AI-based system.
There are majorly two groups of AI
- Applied AI
- Generalized AI
Applied AI is intelligent systems created to trade stocks or shares. Manoeuvring a vehicle also fits in the category of Applied AI.
Generalized AI is more of a device or system that automatically handles the tasks individually. This area of specialization of AI has Machine Learning at its roots.
History of Artificial Intelligence
Did you know, Greek mythology has the mention of mechanical men who were designed to mimic human behavior! This can be thought of as the first stance of talks of AI.
AI has always been there since the introduction of machines. But with more advancement in technology, AI has progressed. Artificial Intelligence was first coined formally at a conference at Dartmouth College in 1956, where scientists were quite optimistic about the future of this technological system.
What is Machine Learning (ML)?
Machine Learning is a subset of the overarching AI. In the words of Tom M. Mitchell, a computer scientist, Machine Learning is the study of computer algorithms to make the computer program much more efficient and user friendly.
You can say that ML is one of the ways to achieve an AI system. Every website you see on the internet, every system you find on the digital space requires a particular program and algorithm. ML is just the protocol of that algorithm.
For example, as I told you before, movie recommendations on Netflix are an example of AI. Now to generate that list of recommendations, you will use ML to make a database of movies and then the genre or any other details of that movie. This way, the system will understand your preference and will give you a recommended list of films! Isn’t that just great!
But often, AI and ML also come with a large concern for privacy. In fact, this topic is highly debated upon, but one thing that we cannot disregard is that with AI, our lives have become quite easier. Like who would have imagined that just by saying “Alexa, put the alarm for 6:00 am”, the task of setting the alarm would be done!
History of Machine Learning
Machine Learning is one of the fundamental vehicles driving the development of AI. There are two facets to the history of ML.
- In the year 1959, Arthur Samuel realized that instead of teaching computers how to carry out tasks, it would be better if computers were taught to learn everything by themselves.
- With the massive requirement of digital information to be stored and ready for analysis, engineers thought that it would be better to make computers work like efficient machines instead of plugging every information themselves.
So basically, the birth of ML happened to simplify the workload of human beings. However, by giving computers all the responsibility, is this truly a good thing? Well, that is again debatable. The emphasis is still on the fact that we can’t do away from this!
Let’s try to summarise what we just learned now!
- Artificial Intelligence is a broader topic of discussion that encompasses creating technology-based systems to solve problems and make human life easier.
- Machine Learning is a subset of AI that teaches us the algorithm required to sustain the system of AI-based technologies.
With these differences, let us now turn our objective towards a more deep understanding of these terms.
Basics of three different types of AI
There are three different types of AI, or you can say categories of AI according to the working and efficiency.
1. ANI Artificial Narrow Intelligence
The ANI, also known as weak AI, is the most basic of all the AI. Weak AI is generally suited in doing a particular task, but it will not work outside if it’s defined capacities. It will do all the work but is not that strong to be efficient like that of human intelligence. It works on the algorithms already set before. In weak AI, the capability of an AI system is narrow.
An example can be any games like Deep Blue or Alpha Go that were capable of beating humans in the games that it played.
Various software systems provide a limited recommendation of business to its user. That can also be an example of weak AI.
2. AGI Artificial General Intelligence
General Intelligence or strong AI is the most recent development in AI that can be seen around us. This is the case where computers and machines are more human-like in their work. They can work with less human input and make their own decisions.
Examples are virtual assistant, AI chatbot, Alexa, Siri, etc. widely in use nowadays. Most of the development of AI is taking place in the field of strong AI.
3. ASI Artificial Super Intelligence
The ASI or the superintelligence of AI is the more intellectual development of AI. There has not been much development in this field as in ASI, and AI systems will surpass and become better than human intelligence.
4. Components of Machine Learning
The first thing that must have come to your mind is how machines can learn. Let me tell you that there are established algorithms, datasets, and ML features that are important to know while understanding Machine Learning.
Datasets are simply collections of samples of the data that we want to put into use. These samples can be anything from numbers, images to any kind of texts, etc. The pertinent thing to ponder about here to make or create a good dataset.
Features are the solutions that are provided to any sort of a program. Features can be thought of as the dataset that gives solutions or gives the working of the system.
For example, if the task at hand is deciding the price of a hotel in Goa, the solutions will be to compare the hotels’ availability, price, and location. This task shall be done by the ML feature of correlation of various datasets of hotels in Goa. Features include all those regression models, statistical tools required to find solutions.
Algorithms are basically the steps involved in achieving a particular task. It might be possible that the same task requires two different algorithms. It might also be the case that two various tasks need the same algorithm. So an algorithm can be thought of as the approach of achieving that particular task.
While doing any particular task, the computer decides the algorithm, recognizes patterns, and then gives possible predictions or solutions.
When the dataset and features of the AI system are correct, the algorithm works efficiently, leading to better and productive completion of tasks.
Four different types of algorithm-based ML
So in the previous point, I discussed what algorithms are. Now based on these algorithms, we can divide ML into four different parts. Let us understand each of these briefly.
1. Supervised Learning
Supervised Learning refers to the procedure of making the computer learn all the information with the datasets and then make it run to give the solutions. There is a training procedure done to bring accuracy and efficiency. Supervised Learning can be done for search, classification, spam filtering, and language detection.
2. Unsupervised Learning
Unsupervised Learning is when the computer is not fed with any features and does the work independently. This type of ML is used for analytics where ML can bring to notice certain detects that people might miss out on. This type of ML is used in risk management, anomaly detection, etc.
3. Semi-supervised Learning
In this, the person making the program has the desired prediction outcome, but the task of making the prediction is with the computer only. The computer makes use of patterns to structure the data.
4. Reinforcement Learning
Reinforcement Learning can be seen in games where there are no established datasets. Rather the system is made such that it produces the solutions based on the reinforcement signals that it gets. In this, the computer can learn from a dynamic real-world type of situation.
|Artificial Intelligence||Machine Learning|
|AI is a bigger technology-based system that simulates human behaviour.||ML is a subset of AI that basically teaches to operate AI systems.|
|The basic goal of AI is to solve complex problems in a manner a human would do.||The basic goal of ML is to teach the machines to provide accurate data output.|
|AI has a much wider scope. ||ML has a limited scope as compared to AI.|
|Applications of AI are SiriOnline gamesRobotsVirtual assistant||Applications of ML are Google search algorithmAuto-tagging option of Facebook|
|AI has been divided into Weak AI, Strong AI and General AI. ||ML can be divided into four types that are Supervised Learning; Semi Supervised Learning, Unsupervised Learning and Reinforcement Learning. |
|In AI, the basic task is to make an intelligent system to perform any task just like a human would do.||In ML, the machines are taught to perform a particular task and give accurate results using datasets and algorithms. |
|AI is all about Learning, reasoning and self-correction.||ML is all about Learning and self-correction but with the introduction of new data.|
What are the similarities between AI and ML?
Now that you know the differences between ML and AI, it becomes important to know why people tend to use these terms interchangeably. Basically, the field of AI has been large, and from time to time, there have been tremendous inventions and up-gradation of the field. You must have heard the terms such as deep learning, Big data, predictive learning, etc. Similarly, just now, we encountered Machine Learning.
So as and when there has been any progress in this field, and numerous terms have been coined, those terms have been synonymous with the term AI. But what most people didn’t realize was that all these were a subset of the bigger platform of AI.
So for a layman, you might use these terms interchangeably, but if speaking in front of someone from the technical field, it is better to use these terms according to their original meaning.
Career in AI
For those students or people who want to develop their career in this field, there are a wide variety of things to choose from. They can learn how to code and learn computer languages. Having knowledge of C++, Java, Hadoop are some of the languages you can learn.
You can also learn algorithms that are used for ML. Knowing various regression models, optimization, differential equations are the basics of learning algorithms.
Students and young professionals can also learn Unix tools and Distributed Computing to gain more insights into the field of AI. These days, many colleges provide certifications and degrees in Data Analytics that will again help you in making a career in AI.