Artificial Intelligence vs Machine Learning vs Data Science
Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields. It will be a matter of surprise if any professional has not ever heard of even one of these terms.
With the beginning of FOURTH INDUSTRIAL REVOLUTION — a technological revolution that is blurring the lines between the physical, digital, and biological spheres — it is now essential to have a better understanding of the terminology of fast-changing tech.
Is it easy to have a complete understanding of each of these terms?
We are assuming that you have no prior knowledge of any of these terms. Our goal is to dive deep into each of these concepts and spotlight the characteristics that make each of these distinct.
What is Artificial Intelligence (AI)?
“Artificial” can be anything that is made by humans and is not natural. And what do you understand by the word “Intelligence”? It is the ability to understand, think ,and learn. Therefore, artificial intelligence is a broad area of computer science that makes machines seem like they have human intelligence.
The goal of AI is to mimic the human brain and create systems that can function intelligently and independently. AI can manifest itself in many different ways.
If you have ever asked Alexa to order your food or browse Netflix movie suggestions, you are interacting with AI without realizing it.
AI is designed so that you do not realize that there is a machine calling the shots. In the near future, AI is expected to become a little less artificial and a lot more intelligent.
The definition of the word “Intelligence” is important here. Let’s define intelligence in two more ways. “Intelligence” is the ability to make the right decision given a set of inputs and a variety of possible actions, or it is a set of properties of the mind — the ability to plan, solve problems, and reason.
What Does Intelligent Behavior Exhibit?
- Problem Solving — process of finding solutions to complex issues.
- Reasoning — the act of thinking about something in a logical way.
- Planning — the process of making plans for something.
- Decision Making — the process of making important decisions.
- Making Inferences — to conclude or judge from evidence.
- Learning — acquisition of knowledge through study, experience or being taught.
What is Machine Learning (ML)?
We will begin with the most informal and simple definition. Machine learning is a thing-labeler where you explain your task with examples instead of instructions.
The concept behind Machine Learning is that you feed data to machines and let them learn on their own without any human intervention (in the process of learning). Consider a small scenario. Let’s say that you have enrolled for some swimming classes and you have no prior experience of swimming.
Needless to say that initially, you would perform not so well because you have no idea about how to swim, but as you observe and pick up more information, your performance keeps getting better.
The observance is just another way of data collection. Just like how we humans learn from our observations and experiences, machines are also capable of learning on their own when they are fed a good amount of data.
This is precisely how we employ the concept of Machine Learning. It’s the process of getting machines to learn and improve from experience without being explicitly programmed automatically.
So How Do You Make A Machine Learn From the Experience?
To understand this part, we need to understand what algorithms are. A simple google definition states that an algorithm is “a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.”
In other words an algorithm can be a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation. Now we know what algorithms are, let’s explore what makes machines learn.
A machine learning algorithm is essentially a process or set of procedures that help a model adapt to the data given an objective. An ML algorithm normally specifies the way the data is transformed from input to output and how the model learns the appropriate mapping from input to output.
In simpler words, Machine learning algorithms are programs (math and logic) that adjust themselves to perform better as they are exposed to more data.
The “learning” part of machine learning means that those programs change how they process data over time, much as humans change how they process data by learning.
They change in such a way that the probability of any particular input value getting mapped on its correct output value rises every time it gets exposed to a new data by making changes to a set of variables (or structures) inside the algorithm which are used to perform computations on the input values and spit out a final output value.
It is evident from the word “learning” used in the term “Machine Learning” that it is related to Artificial Intelligence, which comprises the learning ability of a human brain.
Machine Learning is about machines experiencing related data altogether and picking up patterns, just like a human being can figure out patterns in any data-set.
Machine Learning Is A Subset of Artificial Intelligence
Machine learning algorithms are of different types. Some algorithms are fed labeled data, and these algorithms adjust themselves to spit out correct labels if later exposed to any unlabeled data (Supervised Learning).
Some algorithms are given unlabeled data to figure out hidden patterns in the data-set (Unsupervised Learning).
If Machine Learning Is A Subset of Artificial Intelligence, what Artificial Intelligence Is Not Machine Learning?
There are plenty of other ways machines can show intelligence in their performance. Machine Learning algorithms feed on data to perform intelligently.
Are there AI algorithms that do not feed on data and yet still show intelligence?
Well, let’s explore a search algorithm of artificial intelligence.
What Is Meant By “Search” In Artificial Intelligence?
Search in AI is the process of navigating from a starting state to a goal state by transitioning through intermediate states. Search algorithms are of great use to Data Scientists.
The values in red are heuristics of each node, and the values in black are the cost of moving between the two nodes. The heuristic value of any node indicates the cost it would take to reach the goal node from that particular node. The cost of moving from ‘a’ to ‘e’ would be 3 + 10 = 13.
A* algorithm makes use of costs and heuristic values to find the shortest path from the initial state to goal state by calculating the estimated cost of the cheapest solution i.e., f(n) = g(n) + h(n). g(n) is the cost of moving from the initial node to node n, and h(n) is the estimate of the cost of reaching goal state from node n (the heuristic value).
Does this algorithm perform intelligently?
The algorithm makes calculations at each step, keeps knowledge of previous calculations, and makes a decision at each step. This is indeed a form of Artificial Intelligence.
But does this anywhere require a data-set to learn and perform intelligently like any Machine Learning algorithm? No. So is this AI? Yes! Is this an example of Machine Learning? No!
Artificial Intelligence Is A Much Broader Concept Than Machine Learning
AI is a technology that has a goal of creating intelligent systems that can simulate human intelligence. In contrast, Machine Learning is one of these ways systems can be made to acquire a particular form of human intelligence. To differentiate these two better, we will use a table.
Artificial Intelligence Machine Learning Overarching field. Subset of AI.The goal is to simulate human intelligence to solve complex problems.
The goal is to learn from data and be able to predict results when new data is presented or just figure out the hidden patterns in unlabeled data. Leads to intelligence or wisdom.Leads to knowledge.
Attempts to find the optimal solution. Tries to find the only solution whether it is optimal or not.
It is now pretty clear how to distinguish Machine Learning from other applications of Artificial Intelligence.
Despite the difference, these terms are often used interchangeably. Therefore it is important to know the key differences. AI often employs ML with its other subsets, for example, Natural Language Processing (NLP) to solve a problem such as text classification.
Data Science
“In the next 10 years, data science and software will do more for medicine than all of the biological sciences together.” — Vinod Khosla
Do you know what a Recommendation Engine is? You might have used Amazon for online shopping. Have you noticed that when you search for a particular item on Amazon, you get similar product recommendations?
How does Amazon work behind all this? How does it manage to show you items that are relevant to your interest? The reason why companies like Amazon, Walmart, and Netflix are performing great is because of how they make use of user-generated data.
These are the data-driven companies. The key to these companies has always been data.
A recommendation system filters down a list of choices for each user based on their browsing history, ratings, profile details, transaction details, cart details, and so on. Such a system is used to obtain useful insights into the shopping patterns of a customer.
It provides every user with a particular (unique) view of their e-commerce website based on their profile.
For example, if you are searching for a Laptop on Amazon, there is a possibility that you would need to buy a laptop bag as well. Amazon maps similar transactions together, and then it suggests relevant items to its user.
Before we dive deeper into this topic, it is necessary to understand the meaning of a few terms that are often associated with Data Science.
What is Data Science?
Data science is a multidisciplinary field focused on discovering actionable insights from large sets of raw (unstructured) and structured data.
Data scientists use different techniques to get answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive datasets to establish solutions to problems that haven’t been thought of yet.
The main goal of the Data Science experts is to ask questions and locate potential avenues of study, with less concern for specific answers and more emphasis placed on a search of the right question to ask.
Have You Ever Heard of Big Data?
Big Data refers to the vast volume of data that is difficult to store and process in real-time. This data can be used to analyze insights that can lead to better decision making.
What is Data Analysis?
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. It is NOT the same as Data Science.
What Steps Are Used to Work On A Data Science Project?
- Understanding of the Business Problem — Asking relevant questions, understanding, and defining objectives for the problem.
- Data Acquisition — Gather and scrape data from multiple sources.
- Data Preparation — Data cleaning and transformation.
- Exploratory Data Analysis — Defining and refining the selection of feature variables that will be used in model development.
- Data Modeling — This is the core activity of the Data Science Project. This involves the repetitive application of diverse machine learning techniques like KNN, Decision Tree, Naïve Bayes to the data to identify the model that best fits the business requirement.
- Visualization and Communication — Meeting clients and communication of business findings. Powerful reports and dashboards are created in this step.
- Deployment and Maintenance — Testing model in pre-production environment before its deployment in the production environment.
While exploring Data Science, we have already figured out where exactly Machine Learning is applied in its diverse environment (in the Data Modeling step). Let’s ask ourselves some important questions.
What sort of relationship does Data Science have with Machine Learning? Is Machine Learning an intersection of Data Science and Artificial Intelligence? The figure below visualizes the relationship between Data Science, AI, and, Machine Learning.
Clearly, you can see that neither ML nor AI is a subset of Data Science, and Data Science is a subset of neither of these. There is much more to Data Science than just AI and ML.
There is much more to AI and ML than just Data Science. There are ML techniques used in Data Science for performing particular tasks and solving specific problems.
There are AI concepts — that are NOT ML techniques — employed in the field of Data Science.
Text mining (an intersection of AI and Data Science, but not ML) is an AI technology that uses Natural Language Processing to transform the raw (unstructured) text in documents and databases into normalized, structured data suitable for analysis or to drive Machine Learning algorithms.
The Importance Of Knowing The Differences
The field of data science offers plenty of promising careers. In order to choose the right specialty for yourself, it is essential to know the distinctions between these different terms that are often wrongly used interchangeably.
We hope that now you have a better idea of what is data science, what is machine learning, and what is the concept of artificial intelligence. However, there is still a lot more you can explore about AI and data science.
