The Evolution From Artificial Intelligence to Machine Learning to Data Science – KDnuggets

The Evolution From Artificial Intelligence to Machine Learning to Data Science
Photo by DeepMind on Unsplash
 

Recent years have seen many breakthroughs and discoveries in artificial intelligence (AI), machine learning (ML), and data science. These fields intersect so much that they have become synonymous. Unfortunately, it has caused some ambiguity. 

This guide aims to clarify the confusion by defining the terms and explaining how they are applied to business and science. We won’t cover them in-depth; however, by the end of this article, you should be able to distinguish between these concepts.  

As a field, AI centers around creating flexible automated systems. The ultimate goal of AI is to build systems that can function intelligently and independently much as human beings can. As such, AI must be able to mimic some of the senses that human beings have. 

They must at least be able to hear, see, and sometimes sense touch and smell. The AI must then be able to interpret stimuli received through these senses and respond accordingly. Thus, different fields and branches under the AI umbrella are dedicated to giving machines and systems these abilities.

Major Branches of AI

The major branches of AI are:

  • Machine Learning (ML)
  • Deep Learning (DL)
  • Natural Language Processing (NLP)
  • Fuzzy Logic
  • Expert Systems
  • Neural Networks 

These concepts aren’t separate fields from artificial intelligence but make modern and future implementations of AI possible. 

Stages of AI

The three phases/stages of AI are as follows:

  • Artificial Narrow Intelligence (ANI) is the current stage of artificial intelligence. It’s also known as weak AI and describes systems of AI that can perform a limited set of defined tasks. 
  • Artificial General Intelligence (AGI): We’re slowly approaching this stage, also known as strong AI. It describes AI as capable of reasoning just as well as human beings. Some academics feel that the AGI label should be limited to sentient AI.  
  • Artificial Super Intelligence (ASI): This is a hypothetical stage of AI where the intelligence and capabilities of computers surpass those of human beings. For now, ASI does not exist outside of the realms of science fiction.  

The above information may seem a little jargon-heavy and esoteric for business users. How does this translate in the real world, and how is AI applied? 

Common Applications of AI

  • Image processing functions in photo-editing software
  • Customer engagement services 
  • Social media algorithms
  • Online advertising platforms
  • Translations provided through Natural Language Processing
  • Robotic Process Automation (RPA)
  • Marketing and product use analytics
  • Non-playable and enemy characters in video and textual games
  • AI Enhancements in Augmented Reality (AR)
  • Sales and trend predictions
  • Self-driving cars
  • Traffic Detection

The term machine learning (ML) is often used interchangeably with artificial intelligence. While they aren’t the same thing, they’re closely connected. 

Applications and software function on code that is mostly fixed. This code contains a limited set of parameters that can only be changed when a programmer edits or adds to them. Machine learning aims to make computing more flexible, allowing software to change its source code at will. It’s similar to how when a person learns something new, they change their brain structures in subtle and drastic ways. 

Major Branches of ML

The four major branches of machine learning are:

  • Supervised Learning
  • Semi-supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Of course, there are subsets and new paradigms such as reinforcement learning, dimensionality reduction, etc. Machine learning is typically implemented using a model.

Types of Machine Learning Models

  • Artificial neural networks
  • Decision trees
  • Support-vector machines
  • Regression analysis
  • Bayesian networks
  • Genetic algorithms
  • Federated learning
  • Reinforcement learning

Deep learning is one of the most well-known and utilized subsets of machine learning. It fundamentally consists of a multi-layered neural network. Neural networks attempt to mimic cognition by closely imitating the structure of the human brain. They are considered the most viable pathways to AGI.  

Applications of Machine Learning in Business

Some examples of where machine learning is used in business and consumer products include:

Product Recommendations

Product recommendations are arguably one of the most popular applications of ML and AI – particularly in e-commerce. In this application, a merchant’s website or app tracks your behavior based on your activities using machine learning. These activities may include your previous purchases, search patterns, clicks, cart history, etc. The merchant will then use an algorithm to create customized product recommendations.

Fraud Detection

With the implementation of machine learning in finance and banking, financial institutions have been able to uncover hidden patterns, detect suspicious activities, and anticipate clerical errors before it’s too late. Capgemini, a technology consulting firm, claims that a well-trained machine learning solution can reduce all incidents of fraud by 70% while increasing the accuracy of transactions by 90%.   

Healthcare

Machine learning has improved the detection rate of anomalies in medical diagnosis, allowing medical practitioners to make more accurate diagnoses. Recently, ML-powered software has been shown to diagnose patients more accurately than experienced medical doctors. It does this by processing medical records and assessing changing parameters in real-time. Its ability to quickly adapt to changes in the environment is one of the biggest advantages of machine learning in the healthcare sector.

Data Science is a broad term that refers to all facets of data management, including gathering, storage, analysis, etc. As such, it’s a field that involves multiple disciplines, including:

  • Statistics
  • Informatics
  • Data analysis
  • Computer science
  • Mathematics
  • Domain knowledge
  • Information science, etc.

An estimated 2.5 quintillion bytes of data are generated daily (globally). Much of this data is unstructured and noisy. A great deal of data scientists’ efforts is dedicated to structuring, sorting, and gaining insights from this data. 

Because data science is a multidisciplinary science and not a concept, it can’t be categorized in the same way as artificial intelligence and machine learning. However, let’s expand on the different professions involved in data science before we cover how it can be used in a business context.

Most Important Professions in Data Science

Some of the most common types of data scientists include: 

  • Machine learning scientists
  • Data engineers
  • Software engineers
  • Actuarial scientists
  • Statisticians
  • Digital analysts
  • Business analysts
  • Spatial data scientists
  • Quality analysts

It is recommended that data scientists must be able to develop software (code), use analytical tools and software, develop predictive models, analyze data integrity and quality, and be able to optimize the flow of data collection. 

Applications of Data Science in Business

Data Science has been an extremely useful tool for businesses. Much of the data generated daily is potential consumer data. For instance, a machine learning implementation can process old medical records or observe and gather information on user behavior. This is a form of data mining. Other ways data science can be applied in business include:

  • Targeted advertising: Companies such as Google, Facebook, and Baidu earn most of their revenue from digital ads. Whether running a blog or an online store, you can use data science to perform customer segmentation or clustering before publishing targeted ad campaigns. The best way to perform clustering and grouping is through an unsupervised ML model. 
  • Sales prediction for stock management: You can use data science predictive models to forecast future sales. Predictive models attempt to predict future sales based on historical data.
  • Recommendation engine for e-commerce: You can use data science to build custom product recommendations for loyal customers by looking at their purchase history.  

Data science (mainly implemented through data analytics) can also be used in business intelligence. Companies can extract valuable insights from data warehouses and use them to make informed business decisions.   

The above guide serves as a simple primer that mostly highlights the differences between artificial intelligence, machine learning, and data science and how they can be applied in a business context. To learn more about these topics, you can visit one of KDnuggets’ many guides and articles on these subjects.
 
 
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed — among other intriguing things — to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.
 

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