Most people use the two buzzwords – Artificial Intelligence (AI) and Machine Learning (ML) interchangeably & they believe both are synonymous. But they are not quite the same. AI is an umbrella term, while ML is a sub-domain of this umbrella term. This article will give a clear notion of the two terms and also throw light on where they are interrelated.
What is Artificial Intelligence (AI)?
Artificial Intelligence is the field of computer science that deals with computers and other digital systems that can mimic human intelligence and work. This simulation of human intelligence gets processed by machines through techniques like Natural Language Processing (NLP), computer vision (CV), speech recognition, neural networks, etc. Artificial Intelligence does not always require pre-programming. They can also use their algorithms to learn and recognize things or perform a task with their own intelligence. Machine learning is also a part of AI that not just learns from previous experience but modifies its operation, delivering better tasks. You can learn Machine Learning Projects at ProjectPro. Even the robots and humanoids we see use different complex artificial algorithms and the sub-domains in parallel. Modern applications of Artificial Intelligence are:
- AI-powered assistance
- Personalized learning
- Autonomous vehicles
- Personalized shopping
With traditional AI tools, the AI engineers and developers had to set precise rules and instructions to tell the system what data to analyze or use and what to expect as an outcome. AI systems work satisfactorily for rule-based tasks. Such systems require explicit knowledge and are meant significantly for automating repetitive tasks.
Types of Artificial Intelligence (AI) –
There are three different types of artificial intelligence. Two of them theoretically exist, while one exists practically.
- Artificial Narrow Intelligence (ANI): It is also known as weak AI and works based on a particular range of functions. They leverage the algorithm and pull data from a broad spectrum of events or experiences to perform a task. Alexa and other artificial assistants are examples of Artificial Narrow Intelligence.
- Artificial General Intelligence (AGI): It is known as deep AI or strong AI that has the power to accomplish intelligent tasks that simulate human intelligence and actions. They can think, learn, and solve complex problems without human intervention. At present, achieving Artificial General Intelligence is not possible because we don’t have a complete understanding of our brain.
- Artificial Super Intelligence: It is a hypothetical state where machines and systems will surpass human capabilities. Here the AIs will start evoking emotions, become self-aware, and might have beliefs or desires.
What is Machine Learning (ML)?
Machine learning is a subset of Artificial Intelligence that uses various algorithms and techniques to become accurate at doing specific operations/tasks or predicting any outcome. They operate as part of artificial intelligence to learn from previous experience and use the data to deliver better performance on every iteration. For evolving as better algorithms & more accurate in their tasks, they do not require explicit programming. These algorithms also employ historical data and user responses (as input) to predict new result values. Modern applications of machine learning are:
- Malware threat detection
- Fraud detection
- Face reading
- Spam filtering
- Traffic prediction
- Speech recognition
- Object identification
With the advent of machine learning, AI systems started developing on their own from previous experience, observations, and data-driven analysis like we humans develop. These machine learning algorithms simulate the brain’s neurons (through neural networks) and copy the approaches we humans use for learning and becoming intelligent.
Types of Machine Learning (ML) –
ML algorithms are of three different types:
- Supervised learning: In this type of ML algorithm, the system learns under supervision. They get spoon-feeding labeled data & gets explicitly suggest that this is the input and this type of output is what we expect as the outcome.
- Unsupervised learning: In this type, the system learns from experiences and past data patterns. They do not require supervision.
- Reinforcement learning: In this type, the system gets the training to make informed decisions for achieving its objectives in complicated scenarios.
How do they differ?
This section will sketch a tabular differentiation between artificial intelligence and machine learning.
| Artificial Intelligence | Machine Learning |
| It enables digital systems to simulate or mimic human-like behavior and intelligence. | It enables the system to learn and become better from previous experiences and data without explicit programming or programmers’ intervention. |
| Artificial Intelligence is the umbrella term that comprises machine learning, neural network, natural language processing, voice recognition, image recognition, etc. | Machine learning is a subset of artificial intelligence, and deep learning is its subset. |
| In AI, the focus remains on making the systems work like humans and performing repetitive tasks with intelligence. | In ML, ML engineers and scientists teach them to accomplish a particular task with more accuracy and to get better every time by learning from past experience. |
| AI is more about intelligent decision-making. | ML is more about self-learning from past events. |
| AI will look for the optimal solution. It will do the repetitive tasks based on pre-written optimal conditions. | ML will look for whether the outcome is optimal or not. If not, it learns from the events and data and performs better in the next iteration. |
| AI system focuses on maximizing the possibilities of getting successful work. | ML systems focus on maximizing the possibilities of accuracy and understanding patterns for a successful algorithm |
| AI is of three types: Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence | Machine learning is of three types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning |
| AI leads to intelligence. | ML leads to knowledge. |
| Since AI comprises various other sub-domains, it has a wide array of scopes. | Since ML is a part of AI, its applications are many, but scopes are comparatively lesser than AI. |
| Without machine learning algorithms, AI cannot train itself and get better at rendering accurate results. | Engineers and scientists use ML in multiple sub-domains of AI, such as computer vision, speech recognition, object recognition, language processing, etc. |
We hope this article has given a clear picture of how both AI and ML are not synonymous and how they differ from each other. Also, we saw a clear differentiation between both in a tabular format.
