Artificial Intelligence vs Machine Learning: What’s the Difference?

Artificial intelligence and Machine learning are two terms that are often used interchangeably, but are they really the same thing? Many people are confused about the distinction between these two concepts, and it’s important to understand the difference. In recent years, AI and machine learning have become increasingly prevalent. From self-driving cars to virtual assistants, these technologies are shaping our daily lives. However, if you’re not familiar with the intricacies of these terms, it can be difficult to grasp their nuances.

While artificial intelligence and machine learning are related, they are not synonymous. It’s important to understand the basics of these concepts to fully comprehend their capabilities and potential applications. In this blog, we will explore the key differences between artificial intelligence vs machine learning, providing you with a clear understanding of each and how they intersect. We will break down each concept, explain their unique characteristics, and highlight their respective applications.

Whether you’re a professional in the tech industry or an aspiring AI enthusiast, understanding the difference between artificial intelligence and machine learning is essential. Let’s take a quick look at the basics.

What is Artificial Intelligence?

Artificial Intelligence refers to the creation of intelligent machines that can perform tasks typically requiring human intelligence. These tasks include learning, reasoning, problem-solving, perception, and natural language understanding. There are multiple uses of artificial intelligence in daily life.

What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence that focuses on the development of algorithms and statistical models. These algorithms allow computers to learn and improve their performance on a specific task without being explicitly programmed. This is why demand for ML experts is rapidly growing across various industries as organizations realize the potential of ML to improve efficiency, gain insights, and make better decisions.

Key Difference between Machine Learning and Artificial Intelligence

The distinctions between Artificial Intelligence (AI) and Machine Learning (ML) lie in several parameters, each highlighting specific aspects of these technologies. Let’s delve into these parameters to understand artificial intelligence and machine learning difference:

I. Scope:

• AI: Encompasses a wide range of capabilities, including problem-solving, natural language understanding, perception, and decision-making.
• ML: Concentrates on developing algorithms that allow systems to learn patterns and make predictions or decisions based on data.

II. Real-Life Examples:

• AI: Virtual assistants like Siri, Alexa, and Google Assistant, Self-driving cars, Fraud detection for banks and gaming.
• ML: Recommendation systems for online platforms like Netflix, Amazon, and Spotify, Spam filtering for emails, Image recognition and Market prediction.

III. Applications:

• AI: Artificial intelligence has widespread applications in various industries. It includes transforming healthcare with medical image analysis and predictive healthcare. In finance, AI powers algorithmic trading, fraud detection, and credit scoring, while retail benefits from personalized recommendations and supply chain optimization. AI-driven autonomous vehicles redefine transportation, and customer service is enhanced through chatbots and sentiment analysis. Manufacturing sees gains in predictive maintenance and quality control, showcasing AI’s versatility across sectors.
• ML: Machine Learning (ML) applications are equally diverse. In healthcare, ML aids in disease prediction and patient monitoring, while finance utilizes ML for portfolio optimization and customer churn prediction. Marketing benefits from ML-powered customer segmentation and churn prediction. NLP applications include real-time language translation and chatbots with natural language understanding. In cybersecurity, ML detects anomalies and identifies fraudulent activities. Energy industries leverage ML for predictive maintenance and energy consumption optimization.

IV. Interdisciplinary Nature:

• AI: Encompasses computer science, neuroscience, philosophy, psychology, etc.
• ML: Mainly involves computer science, statistics, and mathematics.

V. Autonomous Decision-making:

• AI: Can make decisions autonomously based on predefined rules or learned patterns.
• ML: Makes predictions based on patterns but may require human intervention for decision-making.

VI. Categorisation:

• AI: Three broad categories of AI are Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI)
• ML: Three broad categories of ML are Supervised Learning, Unsupervised Learning and Reinforcement Learning

VII. Career Opportunities:

• AI: Career opportunities in AI are diverse and include roles such as AI Engineer, AI Research Scientist, and AI Specialist. Professionals in AI are involved in designing, developing, and implementing intelligent systems across various industries.
• ML: Machine Learning offers career paths such as Machine Learning Engineer, Data Scientist, and Data Analyst. These roles involve working with data, developing algorithms, and extracting valuable insights for decision-making.

VIII. Industry:

• AI: The artificial intelligence market size is expected to surge, with projections indicating a staggering $1.8 trillion by 2030, experiencing a compound annual growth rate (CAGR) of 37.3%. This growth is not only evident in financial terms but also translates into substantial job creation, with estimates suggesting a 21–33% increase in global labor demand by 2030, as forecasted by PwC. It is predicted that 97 million jobs in the AI space will be created just by 2025.
• ML: The Machine Learning (ML) industry presents a compelling growth trajectory. According to Fortune Business Insights, the market size for ML will surge to $225.91 billion by 2030. This growth is projected to occur at a noteworthy compound annual growth rate (CAGR) of 36.2%. Moreover, the demand for machine learning skills is a key driver of industry dynamics. A substantial 82% of companies express the need for employees equipped with machine learning expertise, highlighting the critical role of skilled professionals in meeting industry demands.

IX. Techniques:

• AI: AI techniques include rule-based systems for decision-making, knowledge representation, and reasoning for information processing. Natural Language Processing (NLP) facilitates human-computer interaction, while computer vision interprets visual data. Robotics integrates autonomous decision-making, and expert systems emulate human expertise. Speech recognition enables AI to understand spoken language, and planning involves mapping sequences of actions.
• ML: Machine Learning techniques, a subset of AI, encompass supervised learning for mapping inputs to outputs, unsupervised learning for pattern discovery, and reinforcement learning for decision-making based on feedback. Decision trees simplify complex decisions, while random forests improve accuracy through ensemble learning. Support Vector Machines (SVM) classify data using optimal hyperplanes, clustering groups similar data points, and neural networks organize interconnected nodes for powerful deep learning. These techniques showcase ML’s adaptability across diverse datasets and problem domains.

X. Salary Offered:

• AI: The average annual salary offered to AI engineers for early level is $70,000
• ML: The average annual salary offered to ML engineers for early level is $93,000

How to be Part of These Fields?

We have tried to simplify the differences so that you can have a basic, clear understanding. Now that you know the difference between AI and machine learning, you might be wondering if there is an easy way out to be part of this career path. Then you will be happy to know that there is. You can gain all the required knowledge and skills with the help of online artificial intelligence courses and online machine learning programs.

Here are some course recommendations that can help you:

• Introduction to Artificial Intelligence (AI) by Coursera: Introduction to Artificial Intelligence (AI) is designed for beginners with no prior knowledge of AI. The lectures are structured, engaging, and delivered by experts from IBM.
• Artificial Intelligence (AI) by edX: Artificial Intelligence (AI) is offered in collaboration with Columbia University. It provides a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.
• Artificial Intelligence Certification Course by Edureka: Artificial Intelligence Certification Course covers a wide range of AI topics, including machine learning, deep learning, natural language processing, computer vision, and more. This provides a well-rounded foundation for those seeking a broader understanding of AI.
• PG Program in Machine Learning In Collaboration With IBM by DataTrained: PG Program in Machine Learning covers a wide range of machine learning topics, including supervised and unsupervised learning, deep learning, natural language processing, computer vision, and more. It emphasizes practical application through hands-on projects, real-world case studies, and a potential 6-month internship opportunity.
• Basics of Machine Learning by edX: Basics of Machine Learning is offered in collaboration with the RWTH Aachen University. It is a great introduction to the fundamental concepts and tools of machine learning. The instructors do a good job of explaining complex concepts in a way that is easy to understand.
• IBM Machine Learning Professional Certificate by Coursera: IBM Machine Learning Professional Certificate covers essential machine learning topics, including supervised learning, unsupervised learning, deep learning, natural language processing, and model deployment. It integrates hands-on projects and labs using IBM Cloud tools, providing practical experience with real-world scenarios.

Final Thoughts

Thus, it is evident that artificial intelligence and machine learning are not the same. Having your basics clear about diff between machine learning and artificial intelligence makes a lot of sense. We hope after reading this blog, you won’t ever get confused between the two. And a pro tip, it is a great time to invest in gaining machine learning and AI skills. You can easily do that with the help of online courses discussed above. For more convenience, explore Careervira to find all courses at one place and compare to find the suitable one according to your preferences.