With the ever-increasing volume, variety, and speed of data available, the scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. The question often arises: what is the difference here: data science vs machine learning vs AI? Can they be compared at all? Let’s figure it out.
Data science is a set of specific disciplines from different areas responsible for analyzing data and finding optimal solutions based on it. Previously, only mathematical statistics were engaged in this, then machine learning and artificial intelligence began to be used, which added optimization and computer science to mathematical statistics as data analysis methods.
Data science is a truly cutting-edge and fast-growing branch of knowledge. Market watchers are excitedly predicting that in 10 years, we will need billions and billions more data analysts than we currently have.
Many software libraries, platforms, modules, and tools have been developed that effectively implement the most common algorithms and techniques used in data science.
Data science includes all the tools, methods, and technologies that help us process data and use it for our benefit. It combines statistical inference, data analysis, algorithm development, and technology to solve analytically complex problems.
Three main components of data science:
Data science is a vast and subjective topic of discussion that is almost impossible to fit into one article. Data science is not an independent science, but rather a combination of several related disciplines: mathematics and statistics, programming, business intelligence, and strategic planning.
Also, data science is an interdisciplinary science that uses exclusively scientific methods, processes, algorithms, statistics, modern technologies, and complex systems for a deep understanding of data and information.
Data science is called an interdisciplinary science because it is based on theories, methods, and practices from various fields of knowledge – mathematics, computer science, and many others. Data science also uses machine learning, data analysis, and statistics to obtain reliable results from various data.
Therefore, it is not difficult to guess that data researchers are specialists who are well-versed in data analysis and have the appropriate technical knowledge and education, and the necessary skills to solve complex problems.
Companies need data science and data researchers because they can analyze information and provide valuable insights that will help companies and their businesses.
Why do we need data science? “Retrieving or retrieving information from data” can be a rather vague explanation for the importance of data science. Data science can answer many important questions, such as:
Data science can provide comprehensive and accurate answers to these and other questions, ultimately leading the company to success. Because the correct answers to the questions will ensure competitiveness and significantly improve the quality of customer service and increase customer satisfaction.
For those just starting their journey in the study of artificial intelligence (AI), it is sometimes difficult to figure out what it is in general. Even though this term occurs quite often in the surrounding information field, it does not add any help in understanding, and sometimes it simply harms. The problem is that almost everywhere, it is interpreted differently.
Artificial intelligence is the ability of a digital computer or computer-controlled robot to perform tasks usually associated with sentient beings. The term is often applied to developing systems endowed with human-specific intellectual processes, such as the ability to reason, generalize, or learn from past experience. In addition, the definition of the concept of AI (artificial intelligence) is reduced to a description of a set of related technologies and processes, such as, example, machine learning, virtual agents, and expert systems.
In simple terms, AI is a crude representation of the neurons in the brain. Signals are transmitted from neuron to neuron, and finally, a numerical, categorical, or generative result is obtained. This can be illustrated with the following example. If the system takes a picture of a cat and is trained to recognize whether it is a cat or not, the first layer can identify the general gradients that define the overall shape of the cat. The next layer can identify larger objects such as ears and mouths. The third layer defines smaller objects (such as whiskers). Finally, based on this information, the program will print “yes” or “no” to tell if it is a cat or not. The programmer does not need to “tell” the neurons that these are the features they should be looking for. The AI learned them on its own by training on many images (both with and without cats).
To be successful, artificial intelligence requires machine learning (ML), that is, the use of algorithms to analyze data, study it and make decisions or predict without receiving explicit instructions. With advances in computing and data storage, machine learning has recently evolved into more complex structured models such as deep learning (DL) that use neural networks for even greater understanding and automation. Natural language processing (NLP) is another trend that has driven recent advances in AI, especially in the virtual home and IT assistants. NLP uses voice and word recognition to make it easier to interact with computers through natural language prompts and prompts.
The potential for the use of artificial intelligence is vast, it is already being used in many areas of life: medicine, finance, industry, trade, and, of course, human life.
An example is Siri and Alexa voice assistants, which can be downloaded on iOS, Android, or Windows. There are also bots in video games that can always behave in different ways. There are also trained automatic translators and whole complex smart home systems.
Artificial intelligence plays a significant role in the work of enterprises. It helps automate many labor-intensive processes – so much so that human intervention remains minimal. For example, LG plans to open a factory where everything from the purchase of materials to the shipment of the finished product will be controlled by smart systems. The company’s management is going to start production in a new format in 2023.
Machine learning is called the most promising and complex area of AI. Machine learning is the science of making AI learn and act like a human and make it continuously improve its learning and abilities based on the real-world data we provide.
Also, machine learning (ML) is a set of methods in artificial intelligence, a set of algorithms used to create a machine that learns from experience. As a learning process, the machine processes massive arrays of input data and finds patterns in them.
Machine learning is one of the sections of AI, algorithms that allow a computer to draw conclusions based on data without following rigidly defined rules. The machine can find patterns in complex and multi-parametric problems (which the human brain cannot solve), thus finding more accurate answers. The result is a correct prediction.
First of all, machine learning is designed to make the most accurate predictions based on input data so that business owners, marketers, and employees can make the right decisions in their work. As a result of training, the machine can predict the outcome, remember it, reproduce it if necessary, and choose the best of several options.
At the moment, machine learning covers a wide range of applications, from banks, restaurants, and gas stations to robots in production. New tasks that arise almost daily lead to the emergence of new areas of machine learning.
Artificial intelligence and machine learning are very closely related. And it’s this relationship that makes it so that when you look at the differences between AI and machine learning, you’re looking at how they interact.
Although AI and machine learning are closely related, they are different concepts. Machine learning is considered a subset of AI. A “smart” computer uses AI to think like a human and perform tasks on its own. Machine learning is a way of developing intelligence by a computer system.
How is machine learning different from AI? Today, machine learning is the only possible way to create AI since any modern technology results from computer training.
But the difference is that AI is not only Machine learning but also various computing powers, programs, data, etc. That is, the technology includes many components.
Artificial intelligence will use a different approach to solve the problem. It does not need to label the images, as it will independently determine the specific features of each image. After processing the data, the AI will find the corresponding identifiers in the images and be able to classify them. Here’s what’s the difference between AI and ML.
The main difference lies in the implemented processes and final results. Data science covers, as mathematicians say, an uncountable set of tasks through processing, cleaning, analyzing, predicting, and interpreting results, while machine learning solves a limited range of problems with already cleaned, processed data.
What are the characteristics of each area?
There is also some difference in approaches to education. A data scientist, for example, may focus on gaining knowledge in statistics, mathematics, or actuarial science, while a machine learning engineer will focus on learning aspects of software development.
Let’s start with “data science.” Data science refers to a set of methods, tools, and practices for analyzing data to obtain information for the purpose of decision support. Data science is a broad term, and as a result, true data scientists need to have a wide range of skills, including programming, math/statistics, and domain knowledge in the desired application area.
“Machine learning” is generally considered the ability of a computer program to “learn” or improve performance from examples rather than explicitly programmed rules. Machine learning is one of the critical tools data scientists use to analyze and interpret data. And in turn, machine learning software engineers rely on data science methods and tools to prepare data for use in machine learning. While some organizations have created specific roles for machine learning engineers, in many others, the responsibility for building machine learning models falls to software engineers or data scientists. Whether you are a specialized machine learning engineer or a software developer tasked with implementing a machine learning model, this function in an organization requires a combination of strong mathematical foundations, an understanding of machine learning theory and its algorithms, and sufficient programming skills to implement the models in code. While there is a need in every industry, machine learning engineers are most commonly found in web/technology companies and industry software companies.
“Artificial intelligence” is often used to describe machines capable of replicating the cognitive abilities associated with the human mind. This area as a field of research dates back to the mid-1950s and consists of several sub-fields such as computer vision and robotics. A common distinction is made between “general artificial intelligence,” or replication of the human mind’s capabilities in a broad sense, and “narrow AI,” in which a machine learns to perform a very specific task. Many of the recent advances in AI have been made using machine learning techniques, although the field also includes areas such as expert systems or predictive search.
Machine learning is a central tool in both data science and artificial intelligence. In data science, machine learning is commonly used as a data analysis tool to discover patterns in data and sometimes to make predictions. In artificial intelligence, machine learning is the key to building intelligent agents. Often in AI, the data used for machine learning comes from hardware or sensors, and machine learning tools are used in near real time to enable the machines to act. Another key element that unites all three areas is that data science tools are used to clean, process, and analyze data as input. Although data sources may differ, the same programming methods and tools are often used.
If you consider DS, ML, or AI tools and technologies, it will be almost impossible to distinguish between them accurately. They overlap each other; however, they are not a strict subset of each other. For example, if someone uses the “clustering” algorithm, they can do the job with either DS, ML, or AI, or use combinations of ML+DS, DS+AI, ML+AI, or all three! I would suggest looking at an alternative way of defining these areas, separate from tools and technologies and tying them to the end goal. While they may use an overlapping set of skills, tools, and technologies, DS, ML, and AI can be differentiated in their focus on achieving different end goals.
In general, they correspond to the following goals:
It is easy to see from the definitions that these fields overlap rather intensively. For example, the ability to make human-like decisions may include, among other things, better conclusions. Creating value for an organization may involve creating digital agents with a human approach to decision-making. Similarly, by building training models to make more accurate predictions, you can work with the metrics that will provide the most value to the organization. As you can imagine, the boundaries between these three disciplines are confused, and we often use one of them in the service of the other. Questions such as “why” are you doing this and “what” are you doing with the data can help determine whether your current work should be classified as data science, machine learning, or artificial intelligence.
Another point to keep in mind is that there is almost always a human agent in data science. You may hear “this computer is running machine learning algorithms” or “this digital agent is demonstrating artificial intelligence,” but you won’t hear “this machine is doing data science.” Humans almost always do data science.
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