The modern world isn’t stopping to surprise us with a variety of new concepts. Recently the world blew up with the enormous success of Chat GPT, and the general public became more aware of Artificial Intelligence world.
However, the terms AI, Data Science, and Machine learning are often used to describe one concept by mistake. In this post, we’ll overview their major differences and highlight similarities that may lead to particular confusion.
What is Data Science?
The central aspect around which the whole data science concept is rolling around is extracting new o results from data. Data Science, or DS, is based on strict analytical algorithms and patterns that help deal with unstructured and structured data and supports the process of data selection, preparation, and analysis.
DS also helps to automate the process of searching for demanded information among huge raw data scopes.
DS becomes a multipurpose solution for:
- Social researching (Operating surveys and questionnaires)
- Predictive analytics (Analysis of overall demand and requirements)
- Automated decision-making mechanisms (Engaged in drones systems and face recognition)
- Suggestions and recommendation systems (Used by YouTube, Amazon, etc.)
Top companies using DS
Implementation of DS lies on the shoulders of particular experts – data scientists. Data scientists have a clear understanding of data’s internal structure. Usually, such specialists should know the basics of programming and the working principle of SAS (Statistical Analysis System) and have solid skills in statistical analysis.
However, these abilities do not limit the list of data scientists’ specifications. They also need certain expertise in required business domains, quality control, number theory, and finances.
What is Artificial intelligence?
The main purpose behind AI implementation is to boost any kind of machinery with human intelligence elements. Today AI is already being incorporated in multiple fields: from an intelligent machine that becomes your chess partner to numerous speech recognition systems.
The concept is to make daily used devices think like humans to improve our digital lifetime by helping us solve different kinds of problems.
AI is now most commonly used as:
- Processing of natural languages
- Optimization (mostly recognized in Google Maps road optimization)
- Additional learning
- Game-playing mechanisms
Self-driving cars or modern robots appear to be the brightest examples of AI implementation.
Different companies are already using AI abilities to improve efficiency. Let’s overview the example of Amazon.
Earlier Amazon employees revolved delivery of particular products directly to customers. This process engaged the same algorithm that barely changed. So the company decided to optimize this operation and handle this task with AI-powered robots.
Amazon constructed multiple distribution centers; each customer could receive same-day delivery at the nearest center. The whole delivery chain started working much faster as robots replaced the regular employees.
What is ML or Machine Learning?
Machine learning (ML) is indeed a branch of AI. ML is a study that examines and improves the machinery’s abilities to act like humans. This includes an ability to learn independently.
It’s unnecessary to write a code, you just give the scope of information to algorithms capable of making a logical conclusion based on this data. In simple words, today, computers are capable of learning themselves.
ML helps to improve to boost productivity and scalability in shorter timelines.
You may have certain questions about how ML is used in practice. Now it is impossible to start a streaming service without ML implementation. For example, Netflix uses it to improve its predictive analytics to give users more advanced recommendations and suggestions. The mechanisms can provide users with content that they are truly interested in, which helps to improve customer loyalty too.
ML experts have to provide ‘clean’ data to simplify algorithms’ learning and extraction process. They must also be capable of adequately operating data to bring solid business value into it.
ML specialists need to have a strong skill set in the following aspects:
- Experience in MALLET (Java-based mechanism that helps in natural language processing and recognition)
- Knowing of Apache, Opensource/Tomcat
- C++, Python Knowledge
- Experience in NetworkX, NLTK, Spacy, etc.
Artificial Intelligence vs. Data Science vs. Machine Learning
So, let’s see the primary difference and the connection between AI, ML, and DS. The following tip will provide you with a detailed illustration:
Machine Learning and Data Science
ML and statistics analysis is tightly connected to Data Science, so they have many things in common. ML algorithms use data from Data Science to improve and deliver better business predictions. For Machine Learning, data is vital, otherwise, it won’t be able to perform any operations.
While Data Science is autonomous, with no need to extract data from machinery sources. Survey and questionnaire information, for example, may be collected manually.
The difference is that Data Science engages all data operations, not being limited to statistics or algorithms.
AI and Data Science
Data science stands mostly for data management, which often engages AI helping tools. So, ML and AI help data scientists to create analysis about competitors’ decisions via insights.
While DS is more about visualization and predictions, AI integrates patterns to build future events predictions. Summing up, DS is for creating models by engaging statistical insights, and AI is for creating models to make machines closer to human recognition and thinking.
Artificial Intelligence and Machine Learning
If AI is a way of imitating human behavioural patterns, ML helps AI learn by drawing and providing it with clear extraction of required data.
AI is a scientific method of business process optimization, making robots make human-like decisions. Machine Learning is engaging Data Science in these automation processes.
If examining both of these concepts by their cases, with AI, we imagine Human-AI systems like Alexa, or Google Home, while with ML, we imagine recommendations and suggestions algorithms like in mentioned above Netflix, Spotify, and YouTube.
Besides, AI and ML can successfully cooperate, improving overall user experience while using multiple services, digital assistants, or even self-driving vehicles.
As you see, there are vast similarities between these three concepts. However, if you consider engaging only one, you’ll face the inability to implement each singularly.
Machine Learning is incapable of learning without data engagement provided by Data Science. Same with ML and AI: AI creates human-like thinking machinery, which can then learn by engaging ML.