Difference Between AI, Machine Learning, and Data Science
Though data science, artificial intelligence, and machine learning are all related, there are key differences. Keep reading to learn about the differences.
The three terms Data Science, Artificial Intelligence (AI), and Machine Learning have grown in popularity in recent years. Most manufacturing organizations are beginning their digital transformation toward a fully connected factory, or Industry 4.0.
While the terms Artificial Intelligence (AI), Machine Learning, and Data Science all belong to the same subject and are related, they each have their applications and meaning. Although there may occasionally be some overlap in these domains, each of these three terminologies has a unique application.
By the end of the article, you will clearly understand what these terms signify and how they differ or connect to one another.
But let's first examine what each of them means.
What is Data Science?
It is a vast branch of study that concentrates on data systems and processes to sustain data sets and derive meaning from them. Data scientists employ various tools, applications, principles, and algorithms to make sense of random data clusters. Because practically all types of enterprises generate exponential volumes of data worldwide, monitoring and storing this data becomes tough.
Data science focuses on data modeling and warehousing to manage ever-expanding data collection. Data science applications extract information to influence business processes and achieve organizational goals.
What is Artificial Intelligence?
AI, a widely overused tech phrase in our popular culture, has come to be linked only with futuristic-looking robots and a machine-dominated world. Though, Artificial Intelligence is far from that.
Artificial intelligence aims to enable machines to execute reasoning by mimicking human intelligence. Because the primary goal of AI processes is to teach machines through experience, proper information feeding, and self-correction are critical. AI professionals use deep learning and natural language processing to assist robots in identifying patterns and inferences.
What is Machine Learning?
A subset of Artificial Intelligence that automatically provides mechanisms for computers to learn and improve based on their experiences is called Machine Learning.
AI and machine learning differ in that the latter aims to provide computers with independent learning mechanisms so that they do not require instructions. Machine learning is the process of monitoring and analyzing data or experiences to uncover patterns and build a reasoning system based on the results.
Relationship Between Data Science, Machine Learning, and Artificial Intelligence
Artificial intelligence and data science encompass various applications, platforms, and other technologies that mimic human intelligence through machines. Artificial intelligence is defined as action-planned perception feedback.
Perception > planning > action > Feedback of perception
Data Science employs various components of this pattern or loop to solve specific problems. For instance, data scientists try to identify patterns in data in the first step, perception. Similar to that, the next phase, planning, contains two components:
- Identifying all possible solutions
- Selecting the most adequate solution possible
Data science creates a system that connects both areas mentioned above and assists organizations in moving forward.
Even if machine learning can be explained alone, it is best understood in the context of the system in which it is used.
Simply put, machine learning is the connecting factor between Data Science and AI. This is because it is the process of learning from data through time. So, AI is a technology that assists data scientists in obtaining results and solutions to specific challenges. However, machine learning is what makes that aim possible. Google's Search Engine is a real-world example of this.
- The Google search engine is the result of data science.
- It employs predictive analysis, a system utilized by artificial intelligence, to provide users with intelligent outcomes.
- For example, if a user types "best jackets in NY" into Google's search engine, the AI will collect this information using machine learning.
- When a user types "best place to purchase" into the search bar, the AI immediately steps in and completes the sentence as "best place to buy jackets in NY," using predictive analysis, which is the most likely suffix to the user's original query.
More specifically, Data Science encompasses AI, including machine learning. However, machine learning comprises another sub-technology known as Deep Learning.
Deep Learning is a sort of machine learning that employs Neural Networks to mimic brain function partially and uses a 3D hierarchy in data to find far more critical patterns.
AI vs. Machine Learning vs. Data Science
Artificial intelligence denotes the computer's ability to mimic human behavior. Machine Learning refers to approaches that allow computers to draw conclusions from data and then pass those insights on to AI applications.
Data Science applies to more than Machine Learning and AI. Information in Data Science can come from a machine, a mechanical process, an IT system, etc. It could be entirely about data visualization and have nothing to do with learning.
Data Science can also use AI as a tool for data insights; the primary distinction is that Data Science encompasses the entire spectrum of data gathering, preparation, and analysis. It is not restricted to algorithmic or statistical features. So, while Machine Learning and AI professionals are busy constructing algorithms throughout the project lifecycle, data scientists must be more adaptable, switching between different data roles as needed.
Analysis, visualization, and prediction are all part of data science. It employs many statistical techniques, whereas AI and Machine Learning use models and algorithms to anticipate future events.
How Do Data Science, AI, and Machine Learning Work Together?
Exploring how data science, machine learning, and artificial intelligence intersect is critical. They enable us to manage business operations, prevent dangers, and live, work, and enjoy life safely.
Data science, machine learning, and AI work together to enable predictive analytics, allowing data scientists to estimate customer behavior and allowing retail businesses to better serve customers through improved inventory control and delivery systems. It enables conversational chatbot technology, improving customer service and healthcare support, and speech recognition technology, which controls smart TVs.
Personalized product recommendations, financial advice, and medical treatment. All of these employ Machine Learning. Best-in-class cybersecurity and fraud detection are also supported by a mix of data science, machine learning, and AI.
Differences Between AI, Machine Learning, and Data Science
Although the terms Data Science, Machine Learning, and Artificial Intelligence are related and interconnected, each is distinct and serves diverse functions. Machine Learning is a part of the large field of data science. The critical distinction between the terms is as follows.
AI, Machine Learning, and Data Science Impact on Business
Data science supports businesses by enabling them to make data-driven business choices using valuable insights and advanced analytics. Conversely, AI may support firms in making these data-centric decisions by assisting with implementation and automated analytical procedures.
AI and machine learning help businesses save time and money by improving the precision and accuracy of existing tools and services.
Here are some examples of how Machine Learning, Artificial Intelligence, and data science influence and power various business solutions:
- Enhance human-machine interactions in products and services.
- Allow marketers to target audiences more precisely during the marketing and advertising campaigns.
- Assist in company analytics and problem-solving.
- Improve the efficiency and accuracy of production.
- Assist in the design and planning of items or projects.
- Encourage the effective use of user or customer data.
- Improve customer service and respond quickly to service queries.
- Assist in administration, finance, and management.
A Simple Use Case of AI, Machine Learning, and Data Science
Assume your firm makes a chocolate cake. Your machines follow the instructions daily, but the cake doesn't always turn out well. You will need all three concepts to tackle this quality problem: Data Science, Machine Learning, and Artificial Intelligence.
Your company has started receiving complaints about the taste of your famous chocolate cake changing. When you are notified of this change, you begin to speculate on what the problem could be—did we overcook a batch? Was it the flour's fault? Did the batter sit too long because of our unexpected downtime last week? Data Science allows your team to pull data models to determine which factors may have influenced this shift in product quality.
Following that, your Data Scientist creates a supervised Machine Learning model that includes the ideal recipe and manufacturing method. Over time, the model learns similar variables that produce the desired outcomes and variables that cause modifications to the cake. Your organization determines that changes in the flour caused the product interruption using Machine Learning. Machine Learning was able to prescribe the correct period to sift the flour to maintain the right consistency for the tastiest cake, despite unavoidable raw material variability.
You receive another batch of flour with the incorrect consistency a week later. But don't worry, because Machine Learning assisted in identifying this change and the settings to work at, your mixer automatically detects the texture variation. It continues to mix the batter to the right consistency. Through AI, your cake comes out perfectly!
Future of Data Science, AI, and Machine Learning
With organizations becoming more reliant on technology to run effectively, some industries are seeing the most Data Science and Artificial Intelligence implementations. These industries include healthcare, advertising, banking, education, and machinery. For example, AI and data science are utilized in healthcare to successfully manage patients and human resources such as doctors and nurses.
We can use advanced machine learning and AI to solve numerous medical problems that have plagued us for years, or we can synthesize new forms of drugs using advanced AI and previously acquired and analyzed data. Data Science and Artificial Intelligence have a promising future because of the advanced automation it provides for the numerous systems we use daily.
ML and AI are widely used in industrial applications. Still, there is a rising need for AI to support routine human interactions with machines and enhance goods or services, leading to more efficient, rapid, and autonomous devices and service user interfaces.
AI's future supports efficiency, speed, and superior resolutions, paving the road for significant investments from IT firms, financial organizations, and other industries. Companies from all industries are jumping on the AI bandwagon, hoping to use Machine Learning, the holy grail of technology, to improve their services and products.
In conclusion, AI, Data Science, and Machine Learning are meant to support humans rather than replace them in professions requiring analytical, tactical, or strategic thinking. Instead, it can be viewed as a tool that can provide fresh ideas, enhanced motivation, and improved organizational success.
This quick summary intended to demonstrate that these commonly misunderstood buzzwords don't all mean the same thing. Some ML engineers and Data Scientists cannot distinguish between these terms or lack a clear understanding of the distinctions. Therefore, take some time to gain a thorough understanding of the world around you before diving into Machine Learning, Data Science, and AI.