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Machine learning and artificial intelligence are two of the most disruptive technologies today. Each is behind some of the top products on the market, including ChatGPT, Alexa, and Siri. But what’s the difference between AI vs. machine learning?
In this article, we break down the fundamental difference between AI vs. ML and consider them as separate, although related, career paths, highlighting the possible roles and salaries.
Key Takeaways
- AI is a concept where computer systems are used to simulate human intelligence and perform tasks.
- Machine learning is a subsection of AI development that focuses on developing algorithms to teach systems to learn and adapt independently.
- While there is some crossover between AI and machine learning, the two are ultimately different concepts.
- ML engineers can earn an average salary of $165,000 as of July 2024.
- AI engineers can expect an average salary of $201,000 per year.
- Show Full Guide
Artificial Intelligence vs Machine Learning: The Basics
This can include everything from problem solving to decision making, responding to questions in natural language, speech recognition, image recognition, and translation.
There are three main types of AI: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI). These can be defined as follows:
- ANI: A type of AI where an ML algorithm is developed to perform a single task.
- AGI: A type of AI that mimics human intelligence and can learn independently.
- ASI: A type of AI that’s autonomous, self-aware, and surpasses human intelligence.
Typically, these algorithms will process patterns in a training dataset and use them to make decisions or predictions in the future, in turn developing their own algorithm.
In this sense, the difference between AI and machine learning is that AI is the overall concept of using machines to simulate human cognition, while machine learning is one of the under-the-hood approaches that make it possible for computer systems to perform tasks independently.
It’s also important to highlight that not all AI-related tasks fall under the banner of machine learning. For instance, areas like computer vision and robotics could fall outside of machine learning due to the role of images and other visual data.
Machine Learning vs AI: Side-by-Side Comparison
| Artificial Intelligence?? | Machine Learning? | |
| Definition? | The concept of using computer systems to simulate human intelligence and perform certain tasks. | A subset of AI used to refer to systems that are taught to learn and adapt via algorithms without following explicit instructions. |
| Objective | Develop a machine that can recreate human cognition and automate various tasks. | Give a computer system the ability to learn from past data and make decisions independently. |
| Data Inputs? | Structured, semi-structured, unstructured | Structured and semi-structured |
| Skills/Related Fields? | Natural language processing, natural language generation, deep learning, computer vision, robotics, neural networks, data science | Natural language processing, natural language generation, deep learning, neural networks, data science |
| Roles? | AI researcher, AI engineer, AI product manager | Machine learning researcher, ML engineer, NLP engineer, deep learning engineer |
| Pay? | The average pay for an AI engineer is $201,000 per year | The average pay for a machine learning engineer is $165,000 |
Major Differences Between AI and Machine Learning
Focus & Objectives
AI and machine learning each have different core focuses and objectives. In a nutshell, the difference between AI vs. ML is that the former is about using computer systems to replicate human cognition to automate certain tasks, whereas machine learning is a specific technique used to enable a computer system to learn independently.
Areas & Specializations
Despite crossover in a number of areas, there are some typical specializations that are more closely related to each field. For instance, we can consider deep learning as a subcategory of machine learning. Likewise, areas like computer vision and robotics are generally associated with AI.
Real-Life Use Cases
Please design (could be with some suitable icons/images per each use case)
AI and machine learning support a wide range of use cases. Some of the most common use cases include:
AI/ML are foundational technologies for chatbots and personal assistants like ChatGPT, Gemini, and Claude, which use natural language processing and natural language generation to answer questions and generate content.
AI/ML can also be used as part of customer service, allowing chatbots to automatically respond to customer requests and automate processes such as refunds or returns.
AI/ML can ingest and enter data into data analytics solutions and provide written summaries of notable trends.
AI and ML provide a solution for supply chain management, using data collected throughout the supply chain to predict future demand and shortages, making it easier to anticipate operational disruption.
AI and ML also play a role in fraud detection, where financial services institutions can use them to identify fraudulent transactions, conduct risk assessments, monitor compliance, and optimize costs.
AI/ML can help healthcare and pharmaceutical companies accelerate the development of drugs and treatments and automate documentation.
Career Prospects in Artificial Intelligence
Roles & Responsibilities
You can undertake a wide range of AI-related roles and responsibilities.
That being said, AI engineers will typically be expected to develop new AI models and software, write code, evaluate and test AI-driven products, and collect training data.
Top 8 AI Jobs Include
- AI Engineer
- AI Product Manager
- AI Research Scientist
- AI Ethics Specialist
- Cybersecurity Analyst With AI Expertise
- Computer Vision Engineer
- Data Scientist
- Natural Language Processing Engineer
- Robotics Engineer
Salary Potential
Average Annual Salary: $201,000
Salary Range: $156,000 to $264,000
Careers in AI can be very well-paying, as it’s an emerging technology that’s in high demand.
According to Glassdoor, as of July 8, 2024, an AI engineer can expect to make an average salary of $201,000 in the U.S., with pay ranging from $156,000 to $264,000, depending on the level of experience.
Career Prospects in Machine Learning
Roles & Responsibilities
Machine learning also provides a pathway to lots of different roles, but typically, ML engineers can expect to perform responsibilities including developing machine learning systems and algorithms, training and retraining models, testing and evaluating algorithms, programming and coding products, and performing statistical analysis.
ML-specific roles include:
- ML engineers
- MLOps Engineer
- Machine Learning Research Scientist
Both AI and ML fields require the work of data scientists, cybersecurity analysts, and NLP engineers.
Salary Potential
Average Annual Salary: $165,000
Salary Range: $131,000 to $210,000
Careers in machine learning also have the potential to demand high salaries. In fact, Glassdoor finds that an ML engineer can expect to make an average of $165,000 per year in the U.S., with pay ranging from $131,000 to $210,000.
So, with regards to the machine learning vs. artificial intelligence pay debate, AI is the winner.
Bonus: AI vs. Machine Learning vs. Deep Learning vs. Generative AI
But what about the difference between AI vs. machine learning vs. deep learning?
Well, the difference between AI vs. deep learning is that AI is the concept of using computer systems to simulate human intelligence, whereas deep learning is a subset of machine learning where a researcher builds algorithms with neural networks, structures designed to emulate human cognition.
Likewise, the difference between deep learning vs. machine learning is that deep learning uses algorithmic structures known as neural networks to recreate the human brain. This means that deep learning can automate more complex tasks.
Finally, the difference between generative AI vs. machine learning is that generative AI models are developed to generate “original” content, whereas machine learning is the process of using algorithms to train computer systems to make decisions independently.
The Bottom Line: Which Career Should You Choose?
Machine learning and AI are both well-paying and in-demand fields with plenty of crossover and opportunities for advancement.
If you’re struggling to choose between the two, then consider AI on account of the broad range of disciplines it encompasses and the higher pay it offers on average.
To secure your dream job, estimate your knowledge, skills, and background and consider what education, certificates, and courses you can pursue to get the necessary qualifications.
